A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder

PubMed Central

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive. PMID:28018162

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

PubMed

Kamrunnahar, M; Schiff, S J

2011-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

2015-12-01

Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

PubMed Central

Kamrunnahar, M.; Schiff, S. J.

2017-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models. PMID:22255799

Face processing in chronic alcoholism: a specific deficit for emotional features.

PubMed

Maurage, P; Campanella, S; Philippot, P; Martin, S; de Timary, P

2008-04-01

It is well established that chronic alcoholism is associated with a deficit in the decoding of emotional facial expression (EFE). Nevertheless, it is still unclear whether this deficit is specifically for emotions or due to a more general impairment in visual or facial processing. This study was designed to clarify this issue using multiple control tasks and the subtraction method. Eighteen patients suffering from chronic alcoholism and 18 matched healthy control subjects were asked to perform several tasks evaluating (1) Basic visuo-spatial and facial identity processing; (2) Simple reaction times; (3) Complex facial features identification (namely age, emotion, gender, and race). Accuracy and reaction times were recorded. Alcoholic patients had a preserved performance for visuo-spatial and facial identity processing, but their performance was impaired for visuo-motor abilities and for the detection of complex facial aspects. More importantly, the subtraction method showed that alcoholism is associated with a specific EFE decoding deficit, still present when visuo-motor slowing down is controlled for. These results offer a post hoc confirmation of earlier data showing an EFE decoding deficit in alcoholism by strongly suggesting a specificity of this deficit for emotions. This may have implications for clinical situations, where emotional impairments are frequently observed among alcoholic subjects.

Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex

PubMed Central

Perge, János A.; Zhang, Shaomin; Malik, Wasim Q.; Homer, Mark L.; Cash, Sydney; Friehs, Gerhard; Eskandar, Emad N.; Donoghue, John P.; Hochberg, Leigh R.

2014-01-01

Objective Action potentials and local field potentials (LFPs) recorded in primary motor cortex contain information about the direction of movement. LFPs are assumed to be more robust to signal instabilities than action potentials, which makes LFPs along with action potentials a promising signal source for brain-computer interface applications. Still, relatively little research has directly compared the utility of LFPs to action potentials in decoding movement direction in human motor cortex. Approach We conducted intracortical multielectrode recordings in motor cortex of two persons (T2 and [S3]) as they performed a motor imagery task. We then compared the offline decoding performance of LFPs and spiking extracted from the same data recorded across a one-year period in each participant. Main results We obtained offline prediction accuracy of movement direction and endpoint velocity in multiple LFP bands, with the best performance in the highest (200–400Hz) LFP frequency band, presumably also containing low-pass filtered action potentials. Cross-frequency correlations of preferred directions and directional modulation index showed high similarity of directional information between action potential firing rates (spiking) and high frequency LFPs (70–400Hz), and increasing disparity with lower frequency bands (0–7, 10–40 and 50–65Hz). Spikes predicted the direction of intended movement more accurately than any individual LFP band, however combined decoding of all LFPs was statistically indistinguishable from spike based performance. As the quality of spiking signals (i.e. signal amplitude) and the number of significantly modulated spiking units decreased, the offline decoding performance decreased 3.6[5.65]%/month (for T2 and [S3] respectively). The decrease in the number of significantly modulated LFP signals and their decoding accuracy followed a similar trend (2.4[2.85]%/month, ANCOVA, p=0.27[0.03]). Significance Field potentials provided comparable offline decoding performance to unsorted spikes. Thus, LFPs may provide useful external device control using current human intracortical recording technology. (Clinical trial registration number: NCT00912041) PMID:24921388

Nonword Repetition in Children and Adults: Effects on Movement Coordination

ERIC Educational Resources Information Center

Sasisekaran, Jayanthi; Smith, Anne; Sadagopan, Neeraja; Weber-Fox, Christine

2010-01-01

Hearing and repeating novel phonetic sequences, or novel nonwords, is a task that taps many levels of processing, including auditory decoding, phonological processing, working memory, speech motor planning and execution. Investigations of nonword repetition abilities have been framed within models of psycholinguistic processing, while the motor…

Spiking Neural Network Decoder for Brain-Machine Interfaces.

PubMed

Dethier, Julie; Gilja, Vikash; Nuyujukian, Paul; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

2011-01-01

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo , a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations-neuromorphic chips-may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.

Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke.

PubMed

van Dokkum, L E H; Ward, T; Laffont, I

2015-02-01

The idea of using brain computer interfaces (BCI) for rehabilitation emerged relatively recently. Basically, BCI for neurorehabilitation involves the recording and decoding of local brain signals generated by the patient, as he/her tries to perform a particular task (even if imperfect), or during a mental imagery task. The main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity. The recorded signal can be used in several ways: (i) to objectify and strengthen motor imagery-based training, by providing the patient feedback on the imagined motor task, for example, in a virtual environment; (ii) to generate a desired motor task via functional electrical stimulation or rehabilitative robotic orthoses attached to the patient's limb – encouraging and optimizing task execution as well as "closing" the disrupted sensorimotor loop by giving the patient the appropriate sensory feedback; (iii) to understand cerebral reorganizations after lesion, in order to influence or even quantify plasticity-induced changes in brain networks. For example, applying cerebral stimulation to re-equilibrate inter-hemispheric imbalance as shown by functional recording of brain activity during movement may help recovery. Its potential usefulness for a patient population has been demonstrated on various levels and its diverseness in interface applications makes it adaptable to a large population. The position and status of these very new rehabilitation systems should now be considered with respect to our current and more or less validated traditional methods, as well as in the light of the wide range of possible brain damage. The heterogeneity in post-damage expression inevitably complicates the decoding of brain signals and thus their use in pathological conditions, asking for controlled clinical trials. Copyright © 2015. Published by Elsevier Masson SAS.

Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex

PubMed Central

Kaufman, Matthew T; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V

2015-01-01

When choosing actions, we can act decisively, vacillate, or suffer momentary indecision. Studying how individual decisions unfold requires moment-by-moment readouts of brain state. Here we provide such a view from dorsal premotor and primary motor cortex. Two monkeys performed a novel decision task while we recorded from many neurons simultaneously. We found that a decoder trained using ‘forced choices’ (one target viable) was highly reliable when applied to ‘free choices’. However, during free choices internal events formed three categories. Typically, neural activity was consistent with rapid, unwavering choices. Sometimes, though, we observed presumed ‘changes of mind’: the neural state initially reflected one choice before changing to reflect the final choice. Finally, we observed momentary ‘indecision’: delay forming any clear motor plan. Further, moments of neural indecision accompanied moments of behavioral indecision. Together, these results reveal the rich and diverse set of internal events long suspected to occur during free choice. DOI: http://dx.doi.org/10.7554/eLife.04677.001 PMID:25942352

Principal components of hand kinematics and neurophysiological signals in motor cortex during reach to grasp movements

PubMed Central

Aggarwal, Vikram; Thakor, Nitish V.; Schieber, Marc H.

2014-01-01

A few kinematic synergies identified by principal component analysis (PCA) account for most of the variance in the coordinated joint rotations of the fingers and wrist used for a wide variety of hand movements. To examine the possibility that motor cortex might control the hand through such synergies, we collected simultaneous kinematic and neurophysiological data from monkeys performing a reach-to-grasp task. We used PCA, jPCA and isomap to extract kinematic synergies from 18 joint angles in the fingers and wrist and analyzed the relationships of both single-unit and multiunit spike recordings, as well as local field potentials (LFPs), to these synergies. For most spike recordings, the maximal absolute cross-correlations of firing rates were somewhat stronger with an individual joint angle than with any principal component (PC), any jPC or any isomap dimension. In decoding analyses, where spikes and LFP power in the 100- to 170-Hz band each provided better decoding than other LFP-based signals, the first PC was decoded as well as the best decoded joint angle. But the remaining PCs and jPCs were predicted with lower accuracy than individual joint angles. Although PCs, jPCs or isomap dimensions might provide a more parsimonious description of kinematics, our findings indicate that the kinematic synergies identified with these techniques are not represented in motor cortex more strongly than the original joint angles. We suggest that the motor cortex might act to sculpt the synergies generated by subcortical centers, superimposing an ability to individuate finger movements and adapt the hand to grasp a wide variety of objects. PMID:24990564

Noninvasive EEG correlates of overground and stair walking.

PubMed

Brantley, Justin A; Luu, Trieu Phat; Ozdemir, Recep; Zhu, Fangshi; Winslow, Anna T; Huang, Helen; Contreras-Vidal, Jose L

2016-08-01

Automated walking intention detection remains a challenge in lower-limb neuroprosthetic systems. Here, we assess the feasibility of extracting motor intent from scalp electroencephalography (EEG). First, we evaluated the corticomuscular coherence between central EEG electrodes (C1, Cz, C2) and muscles of the shank and thigh during walking on level ground and stairs. Second, we trained decoders to predict the linear envelope of the surface electromyogram (EMG). We observed significant EEG-led corticomuscular coupling between electrodes and sEMG (tibialis anterior) in the high delta (3-4 Hz) and low theta (4-5 Hz) frequency bands during level walking, indicating efferent signaling from the cortex to peripheral motor neurons. The coherence was increased between EEG and vastus lateralis and tibialis anterior in the delta band (<; 2 Hz) during stair ascent, indicating a task specific modulation in corticomuscular coupling. However, EMG was the leading signal for biceps femoris and gastrocnemius coherence during stair ascent, possibly representing afferent feedback loops from periphery to the motor cortex. Decoder validation showed that EEG signals contained information about the sEMG patterns during over ground walking, however, the accuracy of the predicted sEMG patterns decreased during the stair condition. Overall, these initial findings support the feasibility of integrating sEMG and EEG into a hybrid decoder for volitional control of lower limb neuroprostheses.

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.

PubMed

Shen, Guohua; Zhang, Jing; Wang, Mengxing; Lei, Du; Yang, Guang; Zhang, Shanmin; Du, Xiaoxia

2014-06-01

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Adaptive neuron-to-EMG decoder training for FES neuroprostheses

NASA Astrophysics Data System (ADS)

Ethier, Christian; Acuna, Daniel; Solla, Sara A.; Miller, Lee E.

2016-08-01

Objective. We have previously demonstrated a brain-machine interface neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals. Approach. Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle force-to-endpoint force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns. Main results. We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop cursor control. We compared both offline and online results to those obtained with several other direct force decoders, including an optimal decoder computed from concurrently measured neural and force signals. Significance. This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized EMG-to-force models. Broad generalization could be achieved by including data from multiple grasping tasks in the training of the neuron-to-EMG decoder. Our approach would make it possible for persons with SCI to grasp objects with their own hands, using near-normal motor intent.

Multiscale decoding for reliable brain-machine interface performance over time.

PubMed

Han-Lin Hsieh; Wong, Yan T; Pesaran, Bijan; Shanechi, Maryam M

2017-07-01

Recordings from invasive implants can degrade over time, resulting in a loss of spiking activity for some electrodes. For brain-machine interfaces (BMI), such a signal degradation lowers control performance. Achieving reliable performance over time is critical for BMI clinical viability. One approach to improve BMI longevity is to simultaneously use spikes and other recording modalities such as local field potentials (LFP), which are more robust to signal degradation over time. We have developed a multiscale decoder that can simultaneously model the different statistical profiles of multi-scale spike/LFP activity (discrete spikes vs. continuous LFP). This decoder can also run at multiple time-scales (millisecond for spikes vs. tens of milliseconds for LFP). Here, we validate the multiscale decoder for estimating the movement of 7 major upper-arm joint angles in a non-human primate (NHP) during a 3D reach-to-grasp task. The multiscale decoder uses motor cortical spike/LFP recordings as its input. We show that the multiscale decoder can improve decoding accuracy by adding information from LFP to spikes, while running at the fast millisecond time-scale of the spiking activity. Moreover, this improvement is achieved using relatively few LFP channels, demonstrating the robustness of the approach. These results suggest that using multiscale decoders has the potential to improve the reliability and longevity of BMIs.

Brain-computer interface analysis of a dynamic visuo-motor task.

PubMed

Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface. Copyright © 2010 Elsevier B.V. All rights reserved.

Representation of continuous hand and arm movements in macaque areas M1, F5, and AIP: a comparative decoding study

NASA Astrophysics Data System (ADS)

Menz, Veera Katharina; Schaffelhofer, Stefan; Scherberger, Hansjörg

2015-10-01

Objective. In the last decade, multiple brain areas have been investigated with respect to their decoding capability of continuous arm or hand movements. So far, these studies have mainly focused on motor or premotor areas like M1 and F5. However, there is accumulating evidence that anterior intraparietal area (AIP) in the parietal cortex also contains information about continuous movement. Approach. In this study, we decoded 27 degrees of freedom representing complete hand and arm kinematics during a delayed grasping task from simultaneously recorded activity in areas M1, F5, and AIP of two macaque monkeys (Macaca mulatta). Main results. We found that all three areas provided decoding performances that lay significantly above chance. In particular, M1 yielded highest decoding accuracy followed by F5 and AIP. Furthermore, we provide support for the notion that AIP does not only code categorical visual features of objects to be grasped, but also contains a substantial amount of temporal kinematic information. Significance. This fact could be utilized in future developments of neural interfaces restoring hand and arm movements.

Early visuomotor representations revealed from evoked local field potentials in motor and premotor cortical areas.

PubMed

O'Leary, John G; Hatsopoulos, Nicholas G

2006-09-01

Local field potentials (LFPs) recorded from primary motor cortex (MI) have been shown to be tuned to the direction of visually guided reaching movements, but MI LFPs have not been shown to be tuned to the direction of an upcoming movement during the delay period that precedes movement in an instructed-delay reaching task. Also, LFPs in dorsal premotor cortex (PMd) have not been investigated in this context. We therefore recorded LFPs from MI and PMd of monkeys (Macaca mulatta) and investigated whether these LFPs were tuned to the direction of the upcoming movement during the delay period. In three frequency bands we identified LFP activity that was phase-locked to the onset of the instruction stimulus that specified the direction of the upcoming reach. The amplitude of this activity was often tuned to target direction with tuning widths that varied across different electrodes and frequency bands. Single-trial decoding of LFPs demonstrated that prediction of target direction from this activity was possible well before the actual movement is initiated. Decoding performance was significantly better in the slowest-frequency band compared with that in the other two higher-frequency bands. Although these results demonstrate that task-related information is available in the local field potentials, correlations among these signals recorded from a densely packed array of electrodes suggests that adequate decoding performance for neural prosthesis applications may be limited as the number of simultaneous electrode recordings is increased.

Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

PubMed Central

Jiang, Jiefeng; Egner, Tobias

2014-01-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements

PubMed Central

Mollazadeh, Mohsen; Davidson, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

2013-01-01

The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal ensemble and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint angles of the wrist and fingers. LFP amplitude most accurately predicted transition into the reaction (62%) and movement (73%) states, while spikes most accurately decoded arm, hand, and finger kinematics during movement. Using an LFP-based state decoder to trigger a spike-based kinematic decoder [r = 0.72, root mean squared error (RMSE) = 0.15] significantly improved decoding of reach-to-grasp movements from baseline to final hold, compared with either a spike-based state decoder combined with a spike-based kinematic decoder (r = 0.70, RMSE = 0.17) or a spike-based kinematic decoder alone (r = 0.67, RMSE = 0.17). Combining LFP-based state decoding with spike-based kinematic decoding may be a valuable step toward the realization of BMI control of a multifingered neuroprosthesis performing dexterous manipulation. PMID:23536714

Single trial prediction of self-paced reaching directions from EEG signals.

PubMed

Lew, Eileen Y L; Chavarriaga, Ricardo; Silvoni, Stefano; Millán, José Del R

2014-01-01

Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.

Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control

NASA Astrophysics Data System (ADS)

Schroeder, Karen E.; Irwin, Zachary T.; Bullard, Autumn J.; Thompson, David E.; Bentley, J. Nicole; Stacey, William C.; Patil, Parag G.; Chestek, Cynthia A.

2017-08-01

Objective. Challenges in improving the performance of dexterous upper-limb brain-machine interfaces (BMIs) have prompted renewed interest in quantifying the amount and type of sensory information naturally encoded in the primary motor cortex (M1). Previous single unit studies in monkeys showed M1 is responsive to tactile stimulation, as well as passive and active movement of the limbs. However, recent work in this area has focused primarily on proprioception. Here we examined instead how tactile somatosensation of the hand and fingers is represented in M1. Approach. We recorded multi- and single units and thresholded neural activity from macaque M1 while gently brushing individual finger pads at 2 Hz. We also recorded broadband neural activity from electrocorticogram (ECoG) grids placed on human motor cortex, while applying the same tactile stimulus. Main results. Units displaying significant differences in firing rates between individual fingers (p  <  0.05) represented up to 76.7% of sorted multiunits across four monkeys. After normalizing by the number of channels with significant motor finger responses, the percentage of electrodes with significant tactile responses was 74.9%  ±  24.7%. No somatotopic organization of finger preference was obvious across cortex, but many units exhibited cosine-like tuning across multiple digits. Sufficient sensory information was present in M1 to correctly decode stimulus position from multiunit activity above chance levels in all monkeys, and also from ECoG gamma power in two human subjects. Significance. These results provide some explanation for difficulties experienced by motor decoders in clinical trials of cortically controlled prosthetic hands, as well as the general problem of disentangling motor and sensory signals in primate motor cortex during dextrous tasks. Additionally, examination of unit tuning during tactile and proprioceptive inputs indicates cells are often tuned differently in different contexts, reinforcing the need for continued refinement of BMI training and decoding approaches to closed-loop BMI systems for dexterous grasping.

Neural control of finger movement via intracortical brain-machine interface

NASA Astrophysics Data System (ADS)

Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

2017-12-01

Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.

Augmenting intracortical brain-machine interface with neurally driven error detectors

NASA Astrophysics Data System (ADS)

Even-Chen, Nir; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

2017-12-01

Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion

NASA Astrophysics Data System (ADS)

Yao, Lin; Meng, Jianjun; Sheng, Xinjun; Zhang, Dingguo; Zhu, Xiangyang

2015-02-01

Objective. Lack of efficient calibration and task guidance in motor imagery (MI) based brain-computer interface (BCI) would result in the failure of communication or control, especially in patients, such as a stroke with motor impairment and intact sensation, locked-in state amyotrophic lateral sclerosis, in which the sources of data for calibration may worsen the subsequent decoding. In addition, enhancing the proprioceptive experience in MI might improve the BCI performance. Approach. In this work, we propose a new calibrating and task guidance methodology to further improve the MI BCI, exploiting the afferent nerve system through tendon vibration stimulation to induce a sensation with kinesthesia illusion. A total of 30 subjects’ experiments were carried out, and randomly divided into a control group (control-group) and calibration and task guidance group (CTG-group). Main results. Online experiments have shown that MI could be decoded by classifier calibrated solely using sensation data, with 8 of the 15 subjects in the CTG-Group above 80%, 3 above 95% and all above 65%. Offline chronological cross-validation analysis shows that it has reached a comparable performance with the traditional calibration method (F(1,14)=0.14,P=0.7176). In addition, the discrimination accuracy of MI in the CTG-Group is significantly 12.17% higher on average than that in the control-group (unpaired-T test, P = 0.0086), and illusory sensation indicates no significant difference (unpaired-T test, p = 0.3412). The finding of the existed similarity of the discriminative brain patterns and grand averaged ERD/ERS between imagined movement (actively induced) and illusory movement (passively evoked) also validates the proposed calibration and task guidance framework. Significance. The cognitive complexity of the illusory sensation task is much lower and more objective than that of MI. In addition, subjects’ kinesthetic experience mentally simulated during the MI task might be enhanced by accessing sensory experiences from the illusory stimulation. This sensory stimulation aided BCI design could help make MI BCI more applicable.

Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

PubMed

Jiang, Jiefeng; Egner, Tobias

2014-07-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats.

PubMed

Khorasani, Abed; Heydari Beni, Nargess; Shalchyan, Vahid; Daliri, Mohammad Reza

2016-10-21

Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as position and velocity can be inferred from multiple LFP signals as precisely as spiking activities, however, continuous decoding of the force magnitude from the LFP signals in freely moving animals has remained an open problem. Here, we trained three rats to press a force sensor for getting a drop of water as a reward. A 16-channel micro-wire array was implanted in the primary motor cortex of each trained rat, and obtained LFP signals were used for decoding of the continuous values recorded by the force sensor. Average coefficient of correlation and the coefficient of determination between decoded and actual force signals were r = 0.66 and R 2  = 0.42, respectively. We found that LFP signal on gamma frequency bands (30-120 Hz) had the most contribution in the trained decoding model. This study suggests the feasibility of using low number of LFP channels for the continuous force decoding in freely moving animals resembling BMI systems in real life applications.

A computationally efficient method for incorporating spike waveform information into decoding algorithms.

PubMed

Ventura, Valérie; Todorova, Sonia

2015-05-01

Spike-based brain-computer interfaces (BCIs) have the potential to restore motor ability to people with paralysis and amputation, and have shown impressive performance in the lab. To transition BCI devices from the lab to the clinic, decoding must proceed automatically and in real time, which prohibits the use of algorithms that are computationally intensive or require manual tweaking. A common choice is to avoid spike sorting and treat the signal on each electrode as if it came from a single neuron, which is fast, easy, and therefore desirable for clinical use. But this approach ignores the kinematic information provided by individual neurons recorded on the same electrode. The contribution of this letter is a linear decoding model that extracts kinematic information from individual neurons without spike-sorting the electrode signals. The method relies on modeling sample averages of waveform features as functions of kinematics, which is automatic and requires minimal data storage and computation. In offline reconstruction of arm trajectories of a nonhuman primate performing reaching tasks, the proposed method performs as well as decoders based on expertly manually and automatically sorted spikes.

From classic motor imagery to complex movement intention decoding: The noninvasive Graz-BCI approach.

PubMed

Müller-Putz, G R; Schwarz, A; Pereira, J; Ofner, P

2016-01-01

In this chapter, we give an overview of the Graz-BCI research, from the classic motor imagery detection to complex movement intentions decoding. We start by describing the classic motor imagery approach, its application in tetraplegic end users, and the significant improvements achieved using coadaptive brain-computer interfaces (BCIs). These strategies have the drawback of not mirroring the way one plans a movement. To achieve a more natural control-and to reduce the training time-the movements decoded by the BCI need to be closely related to the user's intention. Within this natural control, we focus on the kinematic level, where movement direction and hand position or velocity can be decoded from noninvasive recordings. First, we review movement execution decoding studies, where we describe the decoding algorithms, their performance, and associated features. Second, we describe the major findings in movement imagination decoding, where we emphasize the importance of estimating the sources of the discriminative features. Third, we introduce movement target decoding, which could allow the determination of the target without knowing the exact movement-by-movement details. Aside from the kinematic level, we also address the goal level, which contains relevant information on the upcoming action. Focusing on hand-object interaction and action context dependency, we discuss the possible impact of some recent neurophysiological findings in the future of BCI control. Ideally, the goal and the kinematic decoding would allow an appropriate matching of the BCI to the end users' needs, overcoming the limitations of the classic motor imagery approach. © 2016 Elsevier B.V. All rights reserved.

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

PubMed

Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

2014-01-01

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Electrophysiological difference between mental state decoding and mental state reasoning.

PubMed

Cao, Bihua; Li, Yiyuan; Li, Fuhong; Li, Hong

2012-06-29

Previous studies have explored the neural mechanism of Theory of Mind (ToM), but the neural correlates of its two components, mental state decoding and mental state reasoning, remain unclear. In the present study, participants were presented with various photographs, showing an actor looking at 1 of 2 objects, either with a happy or an unhappy expression. They were asked to either decode the emotion of the actor (mental state decoding task), predict which object would be chosen by the actor (mental state reasoning task), or judge at which object the actor was gazing (physical task), while scalp potentials were recorded. Results showed that (1) the reasoning task elicited an earlier N2 peak than the decoding task did over the prefrontal scalp sites; and (2) during the late positive component (240-440 ms), the reasoning task elicited a more positive deflection than the other two tasks did at the prefrontal scalp sites. In addition, neither the decoding task nor the reasoning task has no left/right hemisphere difference. These findings imply that mental state reasoning differs from mental state decoding early (210 ms) after stimulus onset, and that the prefrontal lobe is the neural basis of mental state reasoning. Copyright © 2012 Elsevier B.V. All rights reserved.

Simultaneous real-time monitoring of multiple cortical systems.

PubMed

Gupta, Disha; Jeremy Hill, N; Brunner, Peter; Gunduz, Aysegul; Ritaccio, Anthony L; Schalk, Gerwin

2014-10-01

Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance. We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time. This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.

Simultaneous Real-Time Monitoring of Multiple Cortical Systems

PubMed Central

Gupta, Disha; Hill, N. Jeremy; Brunner, Peter; Gunduz, Aysegul; Ritaccio, Anthony L.; Schalk, Gerwin

2014-01-01

Objective Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor, or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance. Approach We study these questions using electrocorticographic (ECoG) signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (6 for offline parameter optimization, 6 for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main results Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelope. These decoders were trained separately and executed simultaneously in real time. Significance This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic. PMID:25080161

Factors that affect error potentials during a grasping task: toward a hybrid natural movement decoding BCI.

PubMed

Omedes, Jason; Schwarz, Andreas; Müller-Putz, Gernot R; Montesano, Luis

2018-05-01

This paper presents a hybrid BCI combining neural correlates of natural movements and interaction error-related potentials (ErrP) to perform a 3D reaching task. It focuses on the impact that design factors of such a hybrid BCI have on the ErrP signatures and in their classification. Approach. Users attempted to control a 3D virtual interface that simulated their own hand, to reach and grasp two different objects. Three factors of interest were modulated during the experimentation: (1) execution speed of the grasping, (2) type of grasping and (3) motor commands generated by motor imagery or real motion. Thirteen healthy subjects carried out the protocol. The peaks and latencies of the ErrP were analyzed for the different factors as well as the classification performance. Main results. ErrP are evoked for erroneous commands decoded from neural correlates of natural movements. The ANOVA analyses revealed that latency and magnitude of the most characteristic ErrP peaks were significantly influenced by the speed at which the grasping was executed, but not the type of grasp. This resulted in an greater accuracy of single-trial decoding of errors for fast movements (75.65%) compared to slow ones (68.99%). Significance. Invariance of ErrP to different type of grasping movements and mental strategies proves this type of hybrid interface to be useful for the design of out of the lab applications such as the operation/control of prosthesis. Factors such as the speed of the movements have to be carefully tuned in order to optimize the performance of the system. . © 2018 IOP Publishing Ltd.

Cortical Decoding of Individual Finger and Wrist Kinematics for an Upper-Limb Neuroprosthesis

PubMed Central

Aggarwal, Vikram; Tenore, Francesco; Acharya, Soumyadipta; Schieber, Marc H.; Thakor, Nitish V.

2010-01-01

Previous research has shown that neuronal activity can be used to continuously decode the kinematics of gross movements involving arm and hand trajectory. However, decoding the kinematics of fine motor movements, such as the manipulation of individual fingers, has not been demonstrated. In this study, single unit activities were recorded from task-related neurons in M1 of two trained rhesus monkey as they performed individuated movements of the fingers and wrist. The primates’ hand was placed in a manipulandum, and strain gauges at the tips of each finger were used to track the digit’s position. Both linear and non-linear filters were designed to simultaneously predict kinematics of each digit and the wrist, and their performance compared using mean squared error and correlation coefficients. All models had high decoding accuracy, but the feedforward ANN (R=0.76–0.86, MSE=0.04–0.05) and Kalman filter (R=0.68–0.86, MSE=0.04–0.07) performed better than a simple linear regression filter (0.58–0.81, 0.05–0.07). These results suggest that individual finger and wrist kinematics can be decoded with high accuracy, and be used to control a multi-fingered prosthetic hand in real-time. PMID:19964645

Planning Ahead: Object-Directed Sequential Actions Decoded from Human Frontoparietal and Occipitotemporal Networks

PubMed Central

Gallivan, Jason P.; Johnsrude, Ingrid S.; Randall Flanagan, J.

2016-01-01

Object-manipulation tasks (e.g., drinking from a cup) typically involve sequencing together a series of distinct motor acts (e.g., reaching toward, grasping, lifting, and transporting the cup) in order to accomplish some overarching goal (e.g., quenching thirst). Although several studies in humans have investigated the neural mechanisms supporting the planning of visually guided movements directed toward objects (such as reaching or pointing), only a handful have examined how manipulatory sequences of actions—those that occur after an object has been grasped—are planned and represented in the brain. Here, using event-related functional MRI and pattern decoding methods, we investigated the neural basis of real-object manipulation using a delayed-movement task in which participants first prepared and then executed different object-directed action sequences that varied either in their complexity or final spatial goals. Consistent with previous reports of preparatory brain activity in non-human primates, we found that activity patterns in several frontoparietal areas reliably predicted entire action sequences in advance of movement. Notably, we found that similar sequence-related information could also be decoded from pre-movement signals in object- and body-selective occipitotemporal cortex (OTC). These findings suggest that both frontoparietal and occipitotemporal circuits are engaged in transforming object-related information into complex, goal-directed movements. PMID:25576538

Dopamine D2-receptor blockade enhances decoding of prefrontal signals in humans.

PubMed

Kahnt, Thorsten; Weber, Susanna C; Haker, Helene; Robbins, Trevor W; Tobler, Philippe N

2015-03-04

The prefrontal cortex houses representations critical for ongoing and future behavior expressed in the form of patterns of neural activity. Dopamine has long been suggested to play a key role in the integrity of such representations, with D2-receptor activation rendering them flexible but weak. However, it is currently unknown whether and how D2-receptor activation affects prefrontal representations in humans. In the current study, we use dopamine receptor-specific pharmacology and multivoxel pattern-based functional magnetic resonance imaging to test the hypothesis that blocking D2-receptor activation enhances prefrontal representations. Human subjects performed a simple reward prediction task after double-blind and placebo controlled administration of the D2-receptor antagonist amisulpride. Using a whole-brain searchlight decoding approach we show that D2-receptor blockade enhances decoding of reward signals in the medial orbitofrontal cortex. Examination of activity patterns suggests that amisulpride increases the separation of activity patterns related to reward versus no reward. Moreover, consistent with the cortical distribution of D2 receptors, post hoc analyses showed enhanced decoding of motor signals in motor cortex, but not of visual signals in visual cortex. These results suggest that D2-receptor blockade enhances content-specific representations in frontal cortex, presumably by a dopamine-mediated increase in pattern separation. These findings are in line with a dual-state model of prefrontal dopamine, and provide new insights into the potential mechanism of action of dopaminergic drugs. Copyright © 2015 the authors 0270-6474/15/354104-08$15.00/0.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.

PubMed

Li, Simin; Li, Jie; Li, Zheng

2016-01-01

Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces

PubMed Central

Li, Simin; Li, Jie; Li, Zheng

2016-01-01

Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well. PMID:28066170

Parental Writing Support and Preschoolers' Early Literacy, Language, and Fine Motor Skills

PubMed Central

Bindman, Samantha W.; Skibbe, Lori E.; Hindman, Annemarie H.; Aram, Dorit; Morrison, Frederick J.

2014-01-01

The current study examines the nature and variability of parents' aid to preschoolers in the context of a shared writing task, as well as the relations between this support and children's literacy, vocabulary, and fine motor skills. In total, 135 preschool children (72 girls) and their parents (primarily mothers) in an ethnically diverse, middle-income community were observed while writing a semi-structured invitation for a pretend birthday party together. Children's phonological awareness, alphabet knowledge, word decoding, vocabulary, and fine motor skills were also assessed. Results revealed that parents provided variable, but generally low–level, support for children's approximation of sound-symbol correspondence in their writing (i.e., graphophonemic support), as well as for their production of letter forms (i.e., print support). Parents frequently accepted errors rather than asking for corrections (i.e., demand for precision). Further analysis of the parent-child dyads (n = 103) who wrote the child's name on the invitation showed that parents provided higher graphophonemic, but not print, support when writing the child's name than other words. Overall parental graphophonemic support was positively linked to children's decoding and fine motor skills, whereas print support and demand for precision were not related to any of the child outcomes. In sum, this study indicates that while parental support for preschoolers' writing may be minimal, it is uniquely linked to key literacy-related outcomes in preschool. PMID:25284957

Parental Writing Support and Preschoolers' Early Literacy, Language, and Fine Motor Skills.

PubMed

Bindman, Samantha W; Skibbe, Lori E; Hindman, Annemarie H; Aram, Dorit; Morrison, Frederick J

2014-01-01

The current study examines the nature and variability of parents' aid to preschoolers in the context of a shared writing task, as well as the relations between this support and children's literacy, vocabulary, and fine motor skills. In total, 135 preschool children (72 girls) and their parents (primarily mothers) in an ethnically diverse, middle-income community were observed while writing a semi-structured invitation for a pretend birthday party together. Children's phonological awareness, alphabet knowledge, word decoding, vocabulary, and fine motor skills were also assessed. Results revealed that parents provided variable, but generally low-level, support for children's approximation of sound-symbol correspondence in their writing (i.e., graphophonemic support), as well as for their production of letter forms (i.e., print support). Parents frequently accepted errors rather than asking for corrections (i.e., demand for precision). Further analysis of the parent-child dyads ( n = 103) who wrote the child's name on the invitation showed that parents provided higher graphophonemic, but not print, support when writing the child's name than other words. Overall parental graphophonemic support was positively linked to children's decoding and fine motor skills, whereas print support and demand for precision were not related to any of the child outcomes. In sum, this study indicates that while parental support for preschoolers' writing may be minimal, it is uniquely linked to key literacy-related outcomes in preschool.

United States Air Force Graduate Student Summer Support Program (1987). Program Management Report.

DTIC Science & Technology

1987-12-01

were briefed on the benefits and research opportunities of the SFRP. The targeted groups within the University community were faculty of the...Effects on Fine Mary C. Robinson Motor Skill and Decoding Tasks 78 Design of a Mechanism to Control Wind Filiberto Santiago Tunnel Turbulence 79 Low...Systems 81 The Integration of Decision Support Jon A. Shupe Problems into Feature Modeling Based Design 89 r 0 82 Optimal Control of the Wing

To sort or not to sort: the impact of spike-sorting on neural decoding performance.

PubMed

Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

2014-10-01

Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

To sort or not to sort: the impact of spike-sorting on neural decoding performance

NASA Astrophysics Data System (ADS)

Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

2014-10-01

Objective. Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. Approach. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Main results. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Significance. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

PubMed

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Population decoding of motor cortical activity using a generalized linear model with hidden states.

PubMed

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

2010-06-15

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

PubMed Central

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

2010-01-01

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

PubMed Central

Padmanaban, Subash; Baker, Justin; Greger, Bradley

2018-01-01

Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements—similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the feature vector supplied to the SVM. Main results: The average decoding accuracy for multi-unit features and single-unit features using Mutual Information Maximization (MIM) across 47 sessions was 96.74 ± 3.5% and 97.65 ± 3.36% respectively. The reduction in decoding accuracy between using 100% of the features and 10% of features based on MIM was 45.56% (from 93.7 to 51.09%) and 4.75% (from 95.32 to 90.79%) for multi-unit and single-unit features respectively. MIM had best performance compared to other feature selection methods. Significance: These results suggest improved decoding performance can be achieved by using optimally selected features. The results based on clinically relevant performance metrics also suggest that the decoding algorithm can be made robust by using optimal features and feature selection algorithms. We believe that even a few percent increase in performance is important and improves the decoding accuracy of the machine learning algorithm potentially increasing the ease of use of a brain machine interface. PMID:29467602

Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study.

PubMed

Ibáñez, Jaime; Monge-Pereira, Esther; Molina-Rueda, Francisco; Serrano, J I; Del Castillo, Maria D; Cuesta-Gómez, Alicia; Carratalá-Tejada, María; Cano-de-la-Cuerda, Roberto; Alguacil-Diego, Isabel M; Miangolarra-Page, Juan C; Pons, Jose L

2017-01-01

Background: The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke. Methods: An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention. Results: On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol. Conclusions: According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits.

Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study

PubMed Central

Ibáñez, Jaime; Monge-Pereira, Esther; Molina-Rueda, Francisco; Serrano, J. I.; del Castillo, Maria D.; Cuesta-Gómez, Alicia; Carratalá-Tejada, María; Cano-de-la-Cuerda, Roberto; Alguacil-Diego, Isabel M.; Miangolarra-Page, Juan C.; Pons, Jose L.

2017-01-01

Background: The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke. Methods: An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention. Results: On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol. Conclusions: According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits. PMID:28367109

Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients

NASA Astrophysics Data System (ADS)

Spüler, M.; Walter, A.; Ramos-Murguialday, A.; Naros, G.; Birbaumer, N.; Gharabaghi, A.; Rosenstiel, W.; Bogdan, M.

2014-12-01

Objective. Recently, there have been several approaches to utilize a brain-computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients. Approach. In a screening session we recorded epidural ECoG data over the ipsilesional motor cortex from four chronic stroke patients who had no residual hand movement. Data was analyzed offline using a support vector machine (SVM) to decode different movement intentions. Main results. We showed that up to seven hand movement intentions can be decoded with an average accuracy of 61% (chance level 15.6%). When reducing the number of classes, average accuracies up to 88% can be achieved for decoding three different movement intentions. Significance. The findings suggest that ipsilesional epidural ECoG can be used as a viable control signal for BCI-driven neuroprosthesis. Although patients showed no sign of residual hand movement, brain activity at the ipsilesional motor cortex still shows enough intention-related activity to decode different movement intentions with sufficient accuracy.

Modeling vocalization with ECoG cortical activity recorded during vocal production in the macaque monkey.

PubMed

Fukushima, Makoto; Saunders, Richard C; Fujii, Naotaka; Averbeck, Bruno B; Mishkin, Mortimer

2014-01-01

Vocal production is an example of controlled motor behavior with high temporal precision. Previous studies have decoded auditory evoked cortical activity while monkeys listened to vocalization sounds. On the other hand, there have been few attempts at decoding motor cortical activity during vocal production. Here we recorded cortical activity during vocal production in the macaque with a chronically implanted electrocorticographic (ECoG) electrode array. The array detected robust activity in motor cortex during vocal production. We used a nonlinear dynamical model of the vocal organ to reduce the dimensionality of `Coo' calls produced by the monkey. We then used linear regression to evaluate the information in motor cortical activity for this reduced representation of calls. This simple linear model accounted for circa 65% of the variance in the reduced sound representations, supporting the feasibility of using the dynamical model of the vocal organ for decoding motor cortical activity during vocal production.

Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

PubMed Central

Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

2016-01-01

We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes

NASA Astrophysics Data System (ADS)

Stavisky, Sergey D.; Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.

2015-06-01

Objective. Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. Approach. Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. Main results. LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. Significance. These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

PubMed

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V; Boahen, Kwabena

2013-06-01

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Task-induced frequency modulation features for brain-computer interfacing.

PubMed

Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2017-10-01

Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

Improved prediction of bimanual movements by a two-staged (effector-then-trajectory) decoder with epidural ECoG in nonhuman primates

NASA Astrophysics Data System (ADS)

Choi, Hoseok; Lee, Jeyeon; Park, Jinsick; Lee, Seho; Ahn, Kyoung-ha; Kim, In Young; Lee, Kyoung-Min; Jang, Dong Pyo

2018-02-01

Objective. In arm movement BCIs (brain-computer interfaces), unimanual research has been much more extensively studied than its bimanual counterpart. However, it is well known that the bimanual brain state is different from the unimanual one. Conventional methodology used in unimanual studies does not take the brain stage into consideration, and therefore appears to be insufficient for decoding bimanual movements. In this paper, we propose the use of a two-staged (effector-then-trajectory) decoder, which combines the classification of movement conditions and uses a hand trajectory predicting algorithm for unimanual and bimanual movements, for application in real-world BCIs. Approach. Two micro-electrode patches (32 channels) were inserted over the dura mater of the left and right hemispheres of two rhesus monkeys, covering the motor related cortex for epidural electrocorticograph (ECoG). Six motion sensors (inertial measurement unit) were used to record the movement signals. The monkeys performed three types of arm movement tasks: left unimanual, right unimanual, bimanual. To decode these movements, we used a two-staged decoder, which combines the effector classifier for four states (left unimanual, right unimanual, bimanual movements, and stationary state) and movement predictor using regression. Main results. Using this approach, we successfully decoded both arm positions using the proposed decoder. The results showed that decoding performance for bimanual movements were improved compared to the conventional method, which does not consider the effector, and the decoding performance was significant and stable over a period of four months. In addition, we also demonstrated the feasibility of epidural ECoG signals, which provided an adequate level of decoding accuracy. Significance. These results provide evidence that brain signals are different depending on the movement conditions or effectors. Thus, the two-staged method could be useful if BCIs are used to generalize for both unimanual and bimanual operations in human applications and in various neuro-prosthetics fields.

A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information.

PubMed

Delis, Ioannis; Berret, Bastien; Pozzo, Thierry; Panzeri, Stefano

2013-01-01

Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9-15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns.

Decoding Intention at Sensorimotor Timescales

PubMed Central

Salvaris, Mathew; Haggard, Patrick

2014-01-01

The ability to decode an individual's intentions in real time has long been a ‘holy grail’ of research on human volition. For example, a reliable method could be used to improve scientific study of voluntary action by allowing external probe stimuli to be delivered at different moments during development of intention and action. Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal. These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action. We have used a combination of sensorimotor rhythms and motor imagery training to decode intentions in a single-trial cued-response paradigm similar to those used in human and non-human primate motor control research. Decoding accuracy of over 0.83 was achieved with twelve participants. With this approach, we could decode intentions to move the left or right hand at sub-second timescales, both for instructed choices instructed by an external stimulus and for free choices generated intentionally by the participant. The implications for volition are considered. PMID:24523855

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system.

PubMed

Perge, János A; Homer, Mark L; Malik, Wasim Q; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P; Hochberg, Leigh R

2013-06-01

Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional 'bias' in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs.

Decoding semantic information from human electrocorticographic (ECoG) signals.

PubMed

Wang, Wei; Degenhart, Alan D; Sudre, Gustavo P; Pomerleau, Dean A; Tyler-Kabara, Elizabeth C

2011-01-01

This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects named pictures of objects belonging to different semantic categories. Robust high-gamma band (60-120 Hz) activation was observed at the left inferior frontal gyrus (LIFG) and the posterior portion of the superior temporal gyrus (pSTG) with a temporal sequence corresponding to speech production and perception. Furthermore, Gaussian Naïve Bayes and Support Vector Machine classifiers, two commonly used machine learning algorithms for pattern recognition, were able to predict the semantic category of an object using cortical activity captured by ECoG electrodes covering the frontal, temporal and parietal cortices. These findings have implications for both basic neuroscience research and development of semantic-based brain-computer interface systems (BCI) that can help individuals with severe motor or communication disorders to express their intention and thoughts.

The role of Broca's area in speech perception: evidence from aphasia revisited.

PubMed

Hickok, Gregory; Costanzo, Maddalena; Capasso, Rita; Miceli, Gabriele

2011-12-01

Motor theories of speech perception have been re-vitalized as a consequence of the discovery of mirror neurons. Some authors have even promoted a strong version of the motor theory, arguing that the motor speech system is critical for perception. Part of the evidence that is cited in favor of this claim is the observation from the early 1980s that individuals with Broca's aphasia, and therefore inferred damage to Broca's area, can have deficits in speech sound discrimination. Here we re-examine this issue in 24 patients with radiologically confirmed lesions to Broca's area and various degrees of associated non-fluent speech production. Patients performed two same-different discrimination tasks involving pairs of CV syllables, one in which both CVs were presented auditorily, and the other in which one syllable was auditorily presented and the other visually presented as an orthographic form; word comprehension was also assessed using word-to-picture matching tasks in both auditory and visual forms. Discrimination performance on the all-auditory task was four standard deviations above chance, as measured using d', and was unrelated to the degree of non-fluency in the patients' speech production. Performance on the auditory-visual task, however, was worse than, and not correlated with, the all-auditory task. The auditory-visual task was related to the degree of speech non-fluency. Word comprehension was at ceiling for the auditory version (97% accuracy) and near ceiling for the orthographic version (90% accuracy). We conclude that the motor speech system is not necessary for speech perception as measured both by discrimination and comprehension paradigms, but may play a role in orthographic decoding or in auditory-visual matching of phonological forms. 2011 Elsevier Inc. All rights reserved.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

2013-06-01

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

PubMed Central

Hilt, Pauline M.; Delis, Ioannis; Pozzo, Thierry; Berret, Bastien

2018-01-01

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements. PMID:29666576

The effect of fine and grapho-motor skill demands on preschoolers' decoding skill.

PubMed

Suggate, Sebastian; Pufke, Eva; Stoeger, Heidrun

2016-01-01

Previous correlational research has found indications that fine motor skills (FMS) link to early reading development, but the work has not demonstrated causality. We manipulated 51 preschoolers' FMS while children learned to decode letters and nonsense words in a within-participants, randomized, and counterbalanced single-factor design with pre- and posttesting. In two conditions, children wrote with a pencil that had a conical shape fitted to the end filled with either steel (impaired writing condition) or polystyrene (normal writing condition). In a third control condition, children simply pointed at the letters with the light pencil as they learned to read the words (pointing condition). Results indicate that children learned the most decoding skills in the normal writing condition, followed by the pointing and impaired writing conditions. In addition, working memory, phonemic awareness, and grapho-motor skills were generally predictors of decoding skill development. The findings provide experimental evidence that having lower FMS is disadvantageous for reading development. Copyright © 2015 Elsevier Inc. All rights reserved.

Task-induced frequency modulation features for brain-computer interfacing

NASA Astrophysics Data System (ADS)

Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2017-10-01

Objective. Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects’ intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects’ intents with an accuracy comparable to task-induced amplitude modulation. Approach. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. Main results. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Significance. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

NASA Astrophysics Data System (ADS)

Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

2014-12-01

Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.

PubMed

Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

2014-12-01

To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like 'Face in a Crowd' task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the 'Crowd') using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a 'Crowd Off' condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

Multi-scale recordings for neuroprosthetic control of finger movements.

PubMed

Baker, Justin; Bishop, William; Kellis, Spencer; Levy, Todd; House, Paul; Greger, Bradley

2009-01-01

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (microECoG) epidurally so that it covered the UEA. The microECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey's forearm. Action potentials (APs), local field potentials (LFPs), and microECoG signals were recorded from wired head-stage connectors for the UEA and microECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey's finger movements. Also, analyses of the LFP and microECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable.

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

PubMed Central

Presacco, Alessandro; Goodman, Ronald; Forrester, Larry

2011-01-01

Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function. PMID:21768121

Odor-identity dependent motor programs underlie behavioral responses to odors

PubMed Central

Jung, Seung-Hye; Hueston, Catherine; Bhandawat, Vikas

2015-01-01

All animals use olfactory information to perform tasks essential to their survival. Odors typically activate multiple olfactory receptor neuron (ORN) classes and are therefore represented by the patterns of active ORNs. How the patterns of active ORN classes are decoded to drive behavior is under intense investigation. In this study, using Drosophila as a model system, we investigate the logic by which odors modulate locomotion. We designed a novel behavioral arena in which we could examine a fly’s locomotion under precisely controlled stimulus condition. In this arena, in response to similarly attractive odors, flies modulate their locomotion differently implying that odors have a more diverse effect on locomotion than was anticipated. Three features underlie odor-guided locomotion: First, in response to odors, flies modulate a surprisingly large number of motor parameters. Second, similarly attractive odors elicit changes in different motor programs. Third, different ORN classes modulate different subset of motor parameters. DOI: http://dx.doi.org/10.7554/eLife.11092.001 PMID:26439011

Bayesian decoding using unsorted spikes in the rat hippocampus

PubMed Central

Layton, Stuart P.; Chen, Zhe; Wilson, Matthew A.

2013-01-01

A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces. PMID:24089403

A Direct Brain-to-Brain Interface in Humans

PubMed Central

Rao, Rajesh P. N.; Stocco, Andrea; Bryan, Matthew; Sarma, Devapratim; Youngquist, Tiffany M.; Wu, Joseph; Prat, Chantel S.

2014-01-01

We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the “sender”) and transmits this information over the internet to the motor cortex region of a second subject (the “receiver”). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender’s signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means. PMID:25372285

An extensible infrastructure for fully automated spike sorting during online experiments.

PubMed

Santhanam, Gopal; Sahani, Maneesh; Ryu, Stephen; Shenoy, Krishna

2004-01-01

When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as "spike sorting." In a number of experiments, notably those that involve direct neuroprosthetic control of an effector, this cell-by-cell classification of the incoming signal must be achieved in real time. Several commercial offerings are available for this task, but all of these require some manual supervision per electrode, making each scheme cumbersome with large electrode counts. We present a new infrastructure that leverages existing unsupervised algorithms to sort and subsequently implement the resulting signal classification rules for each electrode using a commercially available Cerebus neural signal processor. We demonstrate an implementation of this infrastructure to classify signals from a cortical electrode array, using a probabilistic clustering algorithm (described elsewhere). The data were collected from a rhesus monkey performing a delayed center-out reach task. We used both sorted and unsorted (thresholded) action potentials from an array implanted in pre-motor cortex to "predict" the reach target, a common decoding operation in neuroprosthetic research. The use of sorted spikes led to an improvement in decoding accuracy of between 3.6 and 6.4%.

Linguistic Effects on Children's Encoding and Decoding Performance in Japan and the United States.

ERIC Educational Resources Information Center

Foorman, Barbara R.; Kinoshita, Yoshiko

The role of linguistic structure in a referential communication task was examined by comparing encoding and decoding performance of 80 five- and seven-year-old children from Japan and the United States. The linguist structure demanded by the task was the simultaneous encoding and decoding of attributes of size, color, pattern, and shape. (In…

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

PubMed Central

Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

2013-01-01

Objective Motor Neural Interface Systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results Eighty-four percent of the recorded units showed a statistically significant change in apparent firing rate (3.8±8.71Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and seventy-four percent of the units showed a significant change in spike amplitude (3.7±6.5μV or 5.5% of mean spike amplitude). Forty percent of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional “bias” in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in fifty-six percent of all performance assessments in participant cursor control (n=2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. Significance We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs. PMID:23574741

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.

PubMed

Bansal, Arjun K; Truccolo, Wilson; Vargas-Irwin, Carlos E; Donoghue, John P

2012-03-01

Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control.

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials

PubMed Central

Truccolo, Wilson; Vargas-Irwin, Carlos E.; Donoghue, John P.

2012-01-01

Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control. PMID:22157115

Adaptive robotic control driven by a versatile spiking cerebellar network.

PubMed

Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio

2014-01-01

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

Robustness of neuroprosthetic decoding algorithms.

PubMed

Serruya, Mijail; Hatsopoulos, Nicholas; Fellows, Matthew; Paninski, Liam; Donoghue, John

2003-03-01

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.

The right look for the job: decoding cognitive processes involved in the task from spatial eye-movement patterns.

PubMed

Król, Magdalena Ewa; Król, Michał

2018-02-20

The aim of the study was not only to demonstrate whether eye-movement-based task decoding was possible but also to investigate whether eye-movement patterns can be used to identify cognitive processes behind the tasks. We compared eye-movement patterns elicited under different task conditions, with tasks differing systematically with regard to the types of cognitive processes involved in solving them. We used four tasks, differing along two dimensions: spatial (global vs. local) processing (Navon, Cognit Psychol, 9(3):353-383 1977) and semantic (deep vs. shallow) processing (Craik and Lockhart, J Verbal Learn Verbal Behav, 11(6):671-684 1972). We used eye-movement patterns obtained from two time periods: fixation cross preceding the target stimulus and the target stimulus. We found significant effects of both spatial and semantic processing, but in case of the latter, the effect might be an artefact of insufficient task control. We found above chance task classification accuracy for both time periods: 51.4% for the period of stimulus presentation and 34.8% for the period of fixation cross presentation. Therefore, we show that task can be to some extent decoded from the preparatory eye-movements before the stimulus is displayed. This suggests that anticipatory eye-movements reflect the visual scanning strategy employed for the task at hand. Finally, this study also demonstrates that decoding is possible even from very scant eye-movement data similar to Coco and Keller, J Vis 14(3):11-11 (2014). This means that task decoding is not limited to tasks that naturally take longer to perform and yield multi-second eye-movement recordings.

A small, portable, battery-powered brain-computer interface system for motor rehabilitation.

PubMed

McCrimmon, Colin M; Ming Wang; Silva Lopes, Lucas; Wang, Po T; Karimi-Bidhendi, Alireza; Liu, Charles Y; Heydari, Payam; Nenadic, Zoran; Do, An H

2016-08-01

Motor rehabilitation using brain-computer interface (BCI) systems may facilitate functional recovery in individuals after stroke or spinal cord injury. Nevertheless, these systems are typically ill-suited for widespread adoption due to their size, cost, and complexity. In this paper, a small, portable, and extremely cost-efficient (<;$200) BCI system has been developed using a custom electroencephalographic (EEG) amplifier array, and a commercial microcontroller and touchscreen. The system's performance was tested using a movement-related BCI task in 3 able-bodied subjects with minimal previous BCI experience. Specifically, subjects were instructed to alternate between relaxing and dorsiflexing their right foot, while their EEG was acquired and analyzed in real-time by the BCI system to decode their underlying movement state. The EEG signals acquired by the custom amplifier array were similar to those acquired by a commercial amplifier (maximum correlation coefficient ρ=0.85). During real-time BCI operation, the average correlation between instructional cues and decoded BCI states across all subjects (ρ=0.70) was comparable to that of full-size BCI systems. Small, portable, and inexpensive BCI systems such as the one reported here may promote a widespread adoption of BCI-based movement rehabilitation devices in stroke and spinal cord injury populations.

Toward more versatile and intuitive cortical brain machine interfaces

PubMed Central

Andersen, Richard A.; Kellis, Spencer; Klaes, Christian; Aflalo, Tyson

2015-01-01

Brain machine interfaces have great potential in neuroprosthetic applications to assist patients with brain injury and neurodegenerative diseases. One type of BMI is a cortical motor prosthetic which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using 1) recordings from cortical areas outside motor cortex; 2) local field potentials (LFPs) as a source of recorded signals; 3) somatosensory feedback for more dexterous control of robotics; and 4) new decoding methods that work in concert to form an ecology of decode algorithms. These new advances hold promise in greatly accelerating the applicability and ease of operation of motor prosthetics. PMID:25247368

Continuous decoding of human grasp kinematics using epidural and subdural signals

NASA Astrophysics Data System (ADS)

Flint, Robert D.; Rosenow, Joshua M.; Tate, Matthew C.; Slutzky, Marc W.

2017-02-01

Objective. Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces. Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials (EFPs). Approach. We measured the accuracy of decoding continuous hand and finger kinematics during naturalistic grasping motions in five human subjects. We recorded subdural surface potentials (electrocorticography; ECoG) as well as with EFPs, with both standard- and high-resolution electrode arrays. Main results. In all five subjects, decoding of continuous kinematics significantly exceeded chance, using either EGoG or EFPs. ECoG decoding accuracy compared favorably with prior investigations of grasp kinematics (mean ± SD grasp aperture variance accounted for was 0.54 ± 0.05 across all subjects, 0.75 ± 0.09 for the best subject). In general, EFP decoding performed comparably to ECoG decoding. The 7-20 Hz and 70-115 Hz spectral bands contained the most information about grasp kinematics, with the 70-115 Hz band containing greater information about more subtle movements. Higher-resolution recording arrays provided clearly superior performance compared to standard-resolution arrays. Significance. To approach the fine motor control achieved by an intact brain-body system, it will be necessary to execute motor intent on a continuous basis with high accuracy. The current results demonstrate that this level of accuracy might be achievable not just with ECoG, but with EFPs as well. Epidural placement of electrodes is less invasive, and therefore may incur less risk of encephalitis or stroke than subdural placement of electrodes. Accurately decoding motor commands at the epidural level may be an important step towards a clinically viable brain-machine interface.

Continuous decoding of human grasp kinematics using epidural and subdural signals

PubMed Central

Flint, Robert D.; Rosenow, Joshua M.; Tate, Matthew C.; Slutzky, Marc W.

2017-01-01

Objective Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces (BMIs). Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are: accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials. Approach We measured the accuracy of decoding continuous hand and finger kinematics during naturalistic grasping motions in five human subjects. We recorded subdural surface potentials (electrocorticography; ECoG) as well as with epidural field potentials (EFPs), with both standard- and high-resolution electrode arrays. Main results In all five subjects, decoding of continuous kinematics significantly exceeded chance, using either EGoG or EFPs. ECoG decoding accuracy compared favorably with prior investigations of grasp kinematics (mean± SD grasp aperture variance accounted for was 0.54± 0.05 across all subjects, 0.75± 0.09 for the best subject). In general, EFP decoding performed comparably to ECoG decoding. The 7–20 Hz and 70–115 Hz spectral bands contained the most information about grasp kinematics, with the 70–115 Hz band containing greater information about more subtle movements. Higher-resolution recording arrays provided clearly superior performance compared to standard-resolution arrays. Significance To approach the fine motor control achieved by an intact brain-body system, it will be necessary to execute motor intent on a continuous basis with high accuracy. The current results demonstrate that this level of accuracy might be achievable not just with ECoG, but with EFPs as well. Epidural placement of electrodes is less invasive, and therefore may incur less risk of encephalitis or stroke than subdural placement of electrodes. Accurately decoding motor commands at the epidural level may be an important step towards a clinically viable brain-machine interface. PMID:27900947

Quantitative evaluation of muscle synergy models: a single-trial task decoding approach

PubMed Central

Delis, Ioannis; Berret, Bastien; Pozzo, Thierry; Panzeri, Stefano

2013-01-01

Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements. Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies encodes task-discriminating variations of muscle activity in individual trials. To address this question, here we conceive and develop a novel computational framework to evaluate muscle synergy decompositions in task space. Unlike previous methods considering the total variance of muscle patterns (VAF based metrics), our approach focuses on variance discriminating execution of different tasks. The procedure is based on single-trial task decoding from muscle synergy activation features. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task-discriminating variability in the synergy activations. In this paper, we first validate the method on plausibly simulated EMG datasets. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. We find that time-varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies. Overall, these findings stress the effectiveness of the decoding metric in systematically assessing muscle synergy decompositions in task space. PMID:23471195

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.

PubMed

Vidaurre, C; Pascual, J; Ramos-Murguialday, A; Lorenz, R; Blankertz, B; Birbaumer, N; Müller, K-R

2013-09-01

Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients). Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Decoding and Encoding Facial Expressions in Preschool-Age Children.

ERIC Educational Resources Information Center

Zuckerman, Miron; Przewuzman, Sylvia J.

1979-01-01

Preschool-age children drew, decoded, and encoded facial expressions depicting five different emotions. Accuracy of drawing, decoding and encoding each of the five emotions was consistent across the three tasks; decoding ability was correlated with drawing ability among female subjects, but neither of these abilities was correlated with encoding…

Reward Motivation Enhances Task Coding in Frontoparietal Cortex

PubMed Central

Etzel, Joset A.; Cole, Michael W.; Zacks, Jeffrey M.; Kay, Kendrick N.; Braver, Todd S.

2016-01-01

Reward motivation often enhances task performance, but the neural mechanisms underlying such cognitive enhancement remain unclear. Here, we used a multivariate pattern analysis (MVPA) approach to test the hypothesis that motivation-related enhancement of cognitive control results from improved encoding and representation of task set information. Participants underwent two fMRI sessions of cued task switching, the first under baseline conditions, and the second with randomly intermixed reward incentive and no-incentive trials. Information about the upcoming task could be successfully decoded from cue-related activation patterns in a set of frontoparietal regions typically associated with task control. More critically, MVPA classifiers trained on the baseline session had significantly higher decoding accuracy on incentive than non-incentive trials, with decoding improvement mediating reward-related enhancement of behavioral performance. These results strongly support the hypothesis that reward motivation enhances cognitive control, by improving the discriminability of task-relevant information coded and maintained in frontoparietal brain regions. PMID:25601237

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

PubMed Central

Khan, M. Jawad; Hong, Melissa Jiyoun; Hong, Keum-Shik

2014-01-01

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology. PMID:24808844

Exploring the Link between Visual Perception, Visual-Motor Integration, and Reading in Normal Developing and Impaired Children using DTVP-2.

PubMed

Bellocchi, Stéphanie; Muneaux, Mathilde; Huau, Andréa; Lévêque, Yohana; Jover, Marianne; Ducrot, Stéphanie

2017-08-01

Reading is known to be primarily a linguistic task. However, to successfully decode written words, children also need to develop good visual-perception skills. Furthermore, motor skills are implicated in letter recognition and reading acquisition. Three studies have been designed to determine the link between reading, visual perception, and visual-motor integration using the Developmental Test of Visual Perception version 2 (DTVP-2). Study 1 tests how visual perception and visual-motor integration in kindergarten predict reading outcomes in Grade 1, in typical developing children. Study 2 is aimed at finding out if these skills can be seen as clinical markers in dyslexic children (DD). Study 3 determines if visual-motor integration and motor-reduced visual perception can distinguish DD children according to whether they exhibit or not developmental coordination disorder (DCD). Results showed that phonological awareness and visual-motor integration predicted reading outcomes one year later. DTVP-2 demonstrated similarities and differences in visual-motor integration and motor-reduced visual perception between children with DD, DCD, and both of these deficits. DTVP-2 is a suitable tool to investigate links between visual perception, visual-motor integration and reading, and to differentiate cognitive profiles of children with developmental disabilities (i.e. DD, DCD, and comorbid children). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

PubMed

De Feo, Vito; Boi, Fabio; Safaai, Houman; Onken, Arno; Panzeri, Stefano; Vato, Alessandro

2017-01-01

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

PubMed Central

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

Toward more versatile and intuitive cortical brain-machine interfaces.

PubMed

Andersen, Richard A; Kellis, Spencer; Klaes, Christian; Aflalo, Tyson

2014-09-22

Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics. Copyright © 2014 Elsevier Ltd. All rights reserved.

Factor-Analysis Methods for Higher-Performance Neural Prostheses

PubMed Central

Santhanam, Gopal; Yu, Byron M.; Gilja, Vikash; Ryu, Stephen I.; Afshar, Afsheen; Sahani, Maneesh; Shenoy, Krishna V.

2009-01-01

Neural prostheses aim to provide treatment options for individuals with nervous-system disease or injury. It is necessary, however, to increase the performance of such systems before they can be clinically viable for patients with motor dysfunction. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural responses to wax and wane in concert as the subject is, for example, more attentive or more fatigued. If a system does not properly account for this variability, it may mistakenly interpret such variability as an entirely different intention by the subject. We report here the design and characterization of factor-analysis (FA)–based decoding algorithms that can contend with this confound. We characterize the decoders (classifiers) on experimental data where monkeys performed both a real reach task and a prosthetic cursor task while we recorded from 96 electrodes implanted in dorsal premotor cortex. The decoder attempts to infer the underlying factors that comodulate the neurons' responses and can use this information to substantially lower error rates (one of eight reach endpoint predictions) by ≲75% (e.g., ∼20% total prediction error using traditional independent Poisson models reduced to ∼5%). We also examine additional key aspects of these new algorithms: the effect of neural integration window length on performance, an extension of the algorithms to use Poisson statistics, and the effect of training set size on the decoding accuracy of test data. We found that FA-based methods are most effective for integration windows >150 ms, although still advantageous at shorter timescales, that Gaussian-based algorithms performed better than the analogous Poisson-based algorithms and that the FA algorithm is robust even with a limited amount of training data. We propose that FA-based methods are effective in modeling correlated trial-to-trial neural variability and can be used to substantially increase overall prosthetic system performance. PMID:19297518

Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.

PubMed

Willett, Francis R; Murphy, Brian A; Memberg, William D; Blabe, Christine H; Pandarinath, Chethan; Walter, Benjamin L; Sweet, Jennifer A; Miller, Jonathan P; Henderson, Jaimie M; Shenoy, Krishna V; Hochberg, Leigh R; Kirsch, Robert F; Ajiboye, A Bolu

2017-04-01

Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]). Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law. We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.

Continuous detection of the self-initiated walking pre-movement state from EEG correlates without session-to-session recalibration

NASA Astrophysics Data System (ADS)

Ioana Sburlea, Andreea; Montesano, Luis; Minguez, Javier

2015-06-01

Objective. Brain-computer interfaces (BCI) as a rehabilitation tool have been used to restore functions in patients with motor impairments by actively involving the central nervous system and triggering prosthetic devices according to the detected pre-movement state. However, since EEG signals are highly variable between subjects and recording sessions, typically a BCI is calibrated at the beginning of each session. This process is inconvenient especially for patients suffering locomotor disabilities in maintaining a bipedal position for a longer time. This paper presents a continuous EEG decoder of a pre-movement state in self-initiated walking and the usage of this decoder from session to session without recalibrating. Approach. Ten healthy subjects performed a self-initiated walking task during three sessions, with an intersession interval of one week. The implementation of our continuous decoder is based on the combination of movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features with sparse classification models. Main results. During intrasession our technique detects the pre-movement state with 70% accuracy. Moreover this decoder can be applied from session to session without recalibration, with a decrease in performance of about 4% on a one- or two-week intersession interval. Significance. Our detection model operates in a continuous manner, which makes it a straightforward asset for rehabilitation scenarios. By using both temporal and spectral information we attained higher detection rates than the ones obtained with the MRCP and ERD detection models, both during the intrasession and intersession conditions.

A brain-machine interface enables bimanual arm movements in monkeys.

PubMed

Ifft, Peter J; Shokur, Solaiman; Li, Zheng; Lebedev, Mikhail A; Nicolelis, Miguel A L

2013-11-06

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

PubMed Central

2017-01-01

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements. PMID:29201041

Feature Interactions Enable Decoding of Sensorimotor Transformations for Goal-Directed Movement

PubMed Central

Barany, Deborah A.; Della-Maggiore, Valeria; Viswanathan, Shivakumar; Cieslak, Matthew

2014-01-01

Neurophysiology and neuroimaging evidence shows that the brain represents multiple environmental and body-related features to compute transformations from sensory input to motor output. However, it is unclear how these features interact during goal-directed movement. To investigate this issue, we examined the representations of sensory and motor features of human hand movements within the left-hemisphere motor network. In a rapid event-related fMRI design, we measured cortical activity as participants performed right-handed movements at the wrist, with either of two postures and two amplitudes, to move a cursor to targets at different locations. Using a multivoxel analysis technique with rigorous generalization tests, we reliably distinguished representations of task-related features (primarily target location, movement direction, and posture) in multiple regions. In particular, we identified an interaction between target location and movement direction in the superior parietal lobule, which may underlie a transformation from the location of the target in space to a movement vector. In addition, we found an influence of posture on primary motor, premotor, and parietal regions. Together, these results reveal the complex interactions between different sensory and motor features that drive the computation of sensorimotor transformations. PMID:24828640

Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm

NASA Astrophysics Data System (ADS)

Makin, Joseph G.; O'Doherty, Joseph E.; Cardoso, Mariana M. B.; Sabes, Philip N.

2018-04-01

Objective. The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity—vectors of spike counts in small temporal windows—as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip. Inferring the state from the observations then takes the form of a dynamical filter, typically some variant on Kalman’s (KF). The KF, however, although fairly robust in practice, is optimal only when the relationships between variables are linear and the noise is Gaussian, conditions usually violated in practice. Approach. To overcome these limitations we introduce a new filter, the ‘recurrent exponential-family harmonium’ (rEFH), that models the spike counts explicitly as Poisson-distributed, and allows for arbitrary nonlinear dynamics and observation models. Furthermore, the model underlying the filter is acquired through unsupervised learning, which allows temporal correlations in spike counts to be explained by latent dynamics that do not necessarily correspond to the kinematic state of the fingertip. Main results. We test the rEFH on offline reconstruction of the kinematics of reaches in the plane. The rEFH outperforms the standard, as well as three other state-of-the-art, decoders, across three monkeys, two different tasks, most kinematic variables, and a range of bin widths, amounts of training data, and numbers of neurons. Significance. Our algorithm establishes a new state of the art for offline decoding of reaches—in particular, for fingertip velocities, the variable used for control in most online decoders.

The manic phase of Bipolar disorder significantly impairs theory of mind decoding.

PubMed

Hawken, Emily R; Harkness, Kate L; Lazowski, Lauren K; Summers, David; Khoja, Nida; Gregory, James Gardner; Milev, Roumen

2016-05-30

Bipolar disorder is associated with significant deficits in the decoding of others' mental states in comparison to healthy participants. However, differences in theory of mind decoding ability among patients in manic, depressed, and euthymic phases of bipolar disorder is currently unknown. Fifty-nine patients with bipolar I or II disorder (13 manic, 25 depressed, 20 euthymic) completed the "Reading the Mind in the Eyes" Task (Eyes task) and the Animals Task developed to control for non-mentalistic response demands of the Eyes Task. Patients also completed self-report and clinician-rated measures of depression, mania, and anxiety symptoms. Patients in the manic phase were significantly less accurate than those in the depressed and euthymic phases at decoding mental states in the Eyes task, and this effect was strongest for eyes of a positive or neutral valence. Further Eyes task performance was negatively correlated with the symptoms of language/thought disorder, pressured speech, and disorganized thoughts and appearance. These effects held when controlling for accuracy on the Animals task, response times, and relevant demographic and clinical covariates. Results suggest that the state of mania, and particularly psychotic symptoms that may overlap with the schizophrenia spectrum, are most strongly related to social cognitive deficits in bipolar disorder. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Bayesian multi-task learning for decoding multi-subject neuroimaging data.

PubMed

Marquand, Andre F; Brammer, Michael; Williams, Steven C R; Doyle, Orla M

2014-05-15

Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically handled efficiently in PR. In this work, we propose to overcome this problem by recasting the decoding problem in a multi-task learning (MTL) framework. In MTL, a single PR model is used to learn different but related "tasks" simultaneously. The primary advantage of MTL is that it makes more efficient use of the data available and leads to more accurate models by making use of the relationships between tasks. In this work, we construct MTL models where each subject is modelled by a separate task. We use a flexible covariance structure to model the relationships between tasks and induce coupling between them using Gaussian process priors. We present an MTL method for classification problems and demonstrate a novel mapping method suitable for PR models. We apply these MTL approaches to classifying many different contrasts in a publicly available fMRI dataset and show that the proposed MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used techniques. Our results demonstrate that MTL provides a promising method for multi-subject decoding studies by focusing on the commonalities between a group of subjects rather than the idiosyncratic properties of different subjects. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Decoding flexion of individual fingers using electrocorticographic signals in humans

NASA Astrophysics Data System (ADS)

Kubánek, J.; Miller, K. J.; Ojemann, J. G.; Wolpaw, J. R.; Schalk, G.

2009-12-01

Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

NASA Astrophysics Data System (ADS)

Simeral, J. D.; Kim, S.-P.; Black, M. J.; Donoghue, J. P.; Hochberg, L. R.

2011-04-01

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

PubMed Central

Simeral, J D; Kim, S-P; Black, M J; Donoghue, J P; Hochberg, L R

2013-01-01

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor. PMID:21436513

Phonological Coding in Good and Poor Readers.

ERIC Educational Resources Information Center

Briggs, Pamela; Underwood, Geoffrey

1982-01-01

A set of four experiments investigates the relationship between phonological coding and reading ability, using a picture-word interference task and a decoding task. Results with regard to both adults and children suggest that while poor readers possess weak decoding skills, good and poor readers show equivalent evidence of direct semantic and…

Is a cerebellar deficit the underlying cause of reading disabilities?

PubMed

Irannejad, Shahrzad; Savage, Robert

2012-04-01

This study investigated whether children with dyslexia differed in their performance on reading, phonological, rapid naming, motor, and cerebellar-related tasks and automaticity measures compared to reading age (RA)-matched and chronological age (CA)-matched control groups. Participants were 51 children attending mainstream English elementary schools in Quebec. All participants completed measures of IQ, word and nonword reading fluency, elision, nonword decoding, rapid naming, bead threading, peg moving, toe tapping, postural stability, and muscle tone. Results from both group contrasts and analyses at the individual case level did not provide support for claims of motor-cerebellar involvement in either typical or atypical reading acquisition. Results were more consistent with a phonological core process account of both typical reading and reading difficulty. Phonological deficits for children with dyslexia compared to RA-matched controls were, however, only evident in group contrasts. Findings thus also have important implications for identifying at-risk readers among their same-aged peers.

Investigating common coding of observed and executed actions in the monkey brain using cross-modal multi-variate fMRI classification.

PubMed

Fiave, Prosper Agbesi; Sharma, Saloni; Jastorff, Jan; Nelissen, Koen

2018-05-19

Mirror neurons are generally described as a neural substrate hosting shared representations of actions, by simulating or 'mirroring' the actions of others onto the observer's own motor system. Since single neuron recordings are rarely feasible in humans, it has been argued that cross-modal multi-variate pattern analysis (MVPA) of non-invasive fMRI data is a suitable technique to investigate common coding of observed and executed actions, allowing researchers to infer the presence of mirror neurons in the human brain. In an effort to close the gap between monkey electrophysiology and human fMRI data with respect to the mirror neuron system, here we tested this proposal for the first time in the monkey. Rhesus monkeys either performed reach-and-grasp or reach-and-touch motor acts with their right hand in the dark or observed videos of human actors performing similar motor acts. Unimodal decoding showed that both executed or observed motor acts could be decoded from numerous brain regions. Specific portions of rostral parietal, premotor and motor cortices, previously shown to house mirror neurons, in addition to somatosensory regions, yielded significant asymmetric action-specific cross-modal decoding. These results validate the use of cross-modal multi-variate fMRI analyses to probe the representations of own and others' actions in the primate brain and support the proposed mapping of others' actions onto the observer's own motor cortices. Copyright © 2018 Elsevier Inc. All rights reserved.

Neuron selection based on deflection coefficient maximization for the neural decoding of dexterous finger movements.

PubMed

Kim, Yong-Hee; Thakor, Nitish V; Schieber, Marc H; Kim, Hyoung-Nam

2015-05-01

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.

Neuron Selection Based on Deflection Coefficient Maximization for the Neural Decoding of Dexterous Finger Movements

PubMed Central

Kim, Yong-Hee; Thakor, Nitish V.; Schieber, Marc H.; Kim, Hyoung-Nam

2015-01-01

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance. PMID:25347884

Decoding negative affect personality trait from patterns of brain activation to threat stimuli.

PubMed

Fernandes, Orlando; Portugal, Liana C L; Alves, Rita de Cássia S; Arruda-Sanchez, Tiago; Rao, Anil; Volchan, Eliane; Pereira, Mirtes; Oliveira, Letícia; Mourao-Miranda, Janaina

2017-01-15

Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The Development of the Graphics-Decoding Proficiency Instrument

ERIC Educational Resources Information Center

Lowrie, Tom; Diezmann, Carmel M.; Kay, Russell

2011-01-01

The graphics-decoding proficiency (G-DP) instrument was developed as a screening test for the purpose of measuring students' (aged 8-11 years) capacity to solve graphics-based mathematics tasks. These tasks include number lines, column graphs, maps and pie charts. The instrument was developed within a theoretical framework which highlights the…

Process-Based Remediation of Decoding in Gifted LD Students: Three Case Studies.

ERIC Educational Resources Information Center

Crawford, Shawn; Snart, Fern

1994-01-01

Three gifted males (ages 10-13) with deficits in successive coding participated in a process-based remedial program which combined global training on tasks requiring successive processing and tasks applying successive processing to decoding in reading, and which utilized verbal mediation. Differences in student improvement were related to entry…

Integrated source and channel encoded digital communications system design study

NASA Technical Reports Server (NTRS)

Huth, G. K.

1974-01-01

Studies on the digital communication system for the direct communication links from ground to space shuttle and the links involving the Tracking and Data Relay Satellite (TDRS). Three main tasks were performed:(1) Channel encoding/decoding parameter optimization for forward and reverse TDRS links,(2)integration of command encoding/decoding and channel encoding/decoding; and (3) modulation coding interface study. The general communication environment is presented to provide the necessary background for the tasks and to provide an understanding of the implications of the results of the studies.

Human Orbitofrontal Cortex Represents a Cognitive Map of State Space.

PubMed

Schuck, Nicolas W; Cai, Ming Bo; Wilson, Robert C; Niv, Yael

2016-09-21

Although the orbitofrontal cortex (OFC) has been studied intensely for decades, its precise functions have remained elusive. We recently hypothesized that the OFC contains a "cognitive map" of task space in which the current state of the task is represented, and this representation is especially critical for behavior when states are unobservable from sensory input. To test this idea, we apply pattern-classification techniques to neuroimaging data from humans performing a decision-making task with 16 states. We show that unobservable task states can be decoded from activity in OFC, and decoding accuracy is related to task performance and the occurrence of individual behavioral errors. Moreover, similarity between the neural representations of consecutive states correlates with behavioral accuracy in corresponding state transitions. These results support the idea that OFC represents a cognitive map of task space and establish the feasibility of decoding state representations in humans using non-invasive neuroimaging. Copyright © 2016 Elsevier Inc. All rights reserved.

Playing charades in the fMRI: are mirror and/or mentalizing areas involved in gestural communication?

PubMed

Schippers, Marleen B; Gazzola, Valeria; Goebel, Rainer; Keysers, Christian

2009-08-27

Communication is an important aspect of human life, allowing us to powerfully coordinate our behaviour with that of others. Boiled down to its mere essentials, communication entails transferring a mental content from one brain to another. Spoken language obviously plays an important role in communication between human individuals. Manual gestures however often aid the semantic interpretation of the spoken message, and gestures may have played a central role in the earlier evolution of communication. Here we used the social game of charades to investigate the neural basis of gestural communication by having participants produce and interpret meaningful gestures while their brain activity was measured using functional magnetic resonance imaging. While participants decoded observed gestures, the putative mirror neuron system (pMNS: premotor, parietal and posterior mid-temporal cortex), associated with motor simulation, and the temporo-parietal junction (TPJ), associated with mentalizing and agency attribution, were significantly recruited. Of these areas only the pMNS was recruited during the production of gestures. This suggests that gestural communication relies on a combination of simulation and, during decoding, mentalizing/agency attribution brain areas. Comparing the decoding of gestures with a condition in which participants viewed the same gestures with an instruction not to interpret the gestures showed that although parts of the pMNS responded more strongly during active decoding, most of the pMNS and the TPJ did not show such significant task effects. This suggests that the mere observation of gestures recruits most of the system involved in voluntary interpretation.

Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study

PubMed Central

2013-01-01

Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state. PMID:23336819

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

PubMed

Kia, Seyed Mostafa; Pedregosa, Fabian; Blumenthal, Anna; Passerini, Andrea

2017-06-15

The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data. To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models. Copyright © 2017 Elsevier B.V. All rights reserved.

Dual-echo ASL based assessment of motor networks: a feasibility study

NASA Astrophysics Data System (ADS)

Storti, Silvia Francesca; Boscolo Galazzo, Ilaria; Pizzini, Francesca B.; Menegaz, Gloria

2018-04-01

Objective. Dual-echo arterial spin labeling (DE-ASL) technique has been recently proposed for the simultaneous acquisition of ASL and blood-oxygenation-level-dependent (BOLD)-functional magnetic resonance imaging (fMRI) data. The assessment of this technique in detecting functional connectivity at rest or during motor and motor imagery tasks is still unexplored both per-se and in comparison with conventional methods. The purpose is to quantify the sensitivity of the DE-ASL sequence with respect to the conventional fMRI sequence (cvBOLD) in detecting brain activations, and to assess and compare the relevance of node features in decoding the network structure. Approach. Thirteen volunteers were scanned acquiring a pseudo-continuous DE-ASL sequence from which the concomitant BOLD (ccBOLD) simultaneously to the ASL can be extracted. The approach consists of two steps: (i) model-based analyses for assessing brain activations at individual and group levels, followed by statistical analysis for comparing the activation elicited by the three sequences under two conditions (motor and motor imagery), respectively; (ii) brain connectivity graph-theoretical analysis for assessing and comparing the network models properties. Main results. Our results suggest that cvBOLD and ccBOLD have comparable sensitivity in detecting the regions involved in the active task, whereas ASL offers a higher degree of co-localization with smaller activation volumes. The connectivity results and the comparative analysis of node features across sequences revealed that there are no strong changes between rest and tasks and that the differences between the sequences are limited to few connections. Significance. Considering the comparable sensitivity of the ccBOLD and cvBOLD sequences in detecting activated brain regions, the results demonstrate that DE-ASL can be successfully applied in functional studies allowing to obtain both ASL and BOLD information within a single sequence. Further, DE-ASL is a powerful technique for research and clinical applications allowing to perform quantitative comparisons as well as to characterize functional connectivity.

Emergence of a Stable Cortical Map for Neuroprosthetic Control

PubMed Central

Ganguly, Karunesh; Carmena, Jose M.

2009-01-01

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram. PMID:19621062

Classification of Hand Grasp Kinetics and Types Using Movement-Related Cortical Potentials and EEG Rhythms.

PubMed

Jochumsen, Mads; Rovsing, Cecilie; Rovsing, Helene; Niazi, Imran Khan; Dremstrup, Kim; Kamavuako, Ernest Nlandu

2017-01-01

Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48 ± 0.05 (grasp types), 0.41 ± 0.07 (kinetic profiles, motor execution), and 0.39 ± 0.08 (kinetic profiles, motor imagination). Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.

EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution

PubMed Central

Alwanni, Hisham; Baslan, Yara; Alnuman, Nasim; Daoud, Mohammad I.

2017-01-01

This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88.8% and 90.2%, respectively, for the subject-dependent training procedure, and 80.8% and 87.8%, respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations. PMID:28832513

Reward Motivation Enhances Task Coding in Frontoparietal Cortex.

PubMed

Etzel, Joset A; Cole, Michael W; Zacks, Jeffrey M; Kay, Kendrick N; Braver, Todd S

2016-04-01

Reward motivation often enhances task performance, but the neural mechanisms underlying such cognitive enhancement remain unclear. Here, we used a multivariate pattern analysis (MVPA) approach to test the hypothesis that motivation-related enhancement of cognitive control results from improved encoding and representation of task set information. Participants underwent two fMRI sessions of cued task switching, the first under baseline conditions, and the second with randomly intermixed reward incentive and no-incentive trials. Information about the upcoming task could be successfully decoded from cue-related activation patterns in a set of frontoparietal regions typically associated with task control. More critically, MVPA classifiers trained on the baseline session had significantly higher decoding accuracy on incentive than non-incentive trials, with decoding improvement mediating reward-related enhancement of behavioral performance. These results strongly support the hypothesis that reward motivation enhances cognitive control, by improving the discriminability of task-relevant information coded and maintained in frontoparietal brain regions. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Decoding 3-D Reach and Grasp Kinematics from High-Frequency Local Field Potentials in Primate Primary Motor Cortex

PubMed Central

Zhuang, Jun; Vargas-Irwin, Carlos; Donoghue, John P.

2011-01-01

Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity, as well as low frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode three dimensional (3-D) reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of 7 different LFP frequency bands in the range of 0.3 Hz – 400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g. 100 – 200 Hz and 200 – 400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broadband high frequency LFPs, likely reflecting multiunit activity, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics. PMID:20403782

Extracting duration information in a picture category decoding task using hidden Markov Models

NASA Astrophysics Data System (ADS)

Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg

2016-04-01

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

A Bidirectional Brain-Machine Interface Algorithm That Approximates Arbitrary Force-Fields

PubMed Central

Semprini, Marianna; Mussa-Ivaldi, Ferdinando A.; Panzeri, Stefano

2014-01-01

We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop. PMID:24626393

Improving brain-machine interface performance by decoding intended future movements

NASA Astrophysics Data System (ADS)

Willett, Francis R.; Suminski, Aaron J.; Fagg, Andrew H.; Hatsopoulos, Nicholas G.

2013-04-01

Objective. A brain-machine interface (BMI) records neural signals in real time from a subject's brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject's intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user's intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user's future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user's future intent can compensate for the negative effect of control delay on BMI performance.

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

PubMed

Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J; Manolakos, Elias S

2013-09-01

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts’ law

PubMed Central

Willett, Francis R.; Murphy, Brian A.; Memberg, William D.; Blabe, Christine H.; Pandarinath, Chethan; Walter, Benjamin L.; Sweet, Jennifer A.; Miller, Jonathan P.; Henderson, Jaimie M.; Shenoy, Krishna V.; Hochberg, Leigh R.; Kirsch, Robert F.; Ajiboye, A. Bolu

2017-01-01

Objective Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts’ law: MT = a + b log2(D/R ) (where MT is movement time, D is target distance, R is target radius, and a,b are parameters). Fitts’ law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio D/R) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to D/R). Approach Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts’ law. Main Results We found that movement times were better described by the equation MT = a + bD + cR−2, which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the D/R ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user’s motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. Significance The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts’ law-like relationship to iBCI movements may require nonlinear decoding strategies. PMID:28177925

Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts’ law

NASA Astrophysics Data System (ADS)

Willett, Francis R.; Murphy, Brian A.; Memberg, William D.; Blabe, Christine H.; Pandarinath, Chethan; Walter, Benjamin L.; Sweet, Jennifer A.; Miller, Jonathan P.; Henderson, Jaimie M.; Shenoy, Krishna V.; Hochberg, Leigh R.; Kirsch, Robert F.; Bolu Ajiboye, A.

2017-04-01

Objective. Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts’ law: \\text{MT}=a+b{{log}2}(D/R) (where MT is movement time, D is target distance, R is target radius, and a,~b are parameters). Fitts’ law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio D/R ) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to D/R ). Approach. Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts’ law. Main results. We found that movement times were better described by the equation \\text{MT}=a+bD+c{{R}-2} , which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the D/R ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user’s motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. Significance. The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts’ law-like relationship to iBCI movements may require non-linear decoding strategies.

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

PubMed Central

Shanechi, Maryam M.; Williams, Ziv M.; Wornell, Gregory W.; Hu, Rollin C.; Powers, Marissa; Brown, Emery N.

2013-01-01

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system. PMID:23593130

Revealing hidden states in visual working memory using electroencephalography

PubMed Central

Wolff, Michael J.; Ding, Jacqueline; Myers, Nicholas E.; Stokes, Mark G.

2015-01-01

It is often assumed that information in visual working memory (vWM) is maintained via persistent activity. However, recent evidence indicates that information in vWM could be maintained in an effectively “activity-silent” neural state. Silent vWM is consistent with recent cognitive and neural models, but poses an important experimental problem: how can we study these silent states using conventional measures of brain activity? We propose a novel approach that is analogous to echolocation: using a high-contrast visual stimulus, it may be possible to drive brain activity during vWM maintenance and measure the vWM-dependent impulse response. We recorded electroencephalography (EEG) while participants performed a vWM task in which a randomly oriented grating was remembered. Crucially, a high-contrast, task-irrelevant stimulus was shown in the maintenance period in half of the trials. The electrophysiological response from posterior channels was used to decode the orientations of the gratings. While orientations could be decoded during and shortly after stimulus presentation, decoding accuracy dropped back close to baseline in the delay. However, the visual evoked response from the task-irrelevant stimulus resulted in a clear re-emergence in decodability. This result provides important proof-of-concept for a promising and relatively simple approach to decode “activity-silent” vWM content using non-invasive EEG. PMID:26388748

Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

PubMed Central

Ma, Xuan; Ma, Chaolin; Huang, Jian; Zhang, Peng; Xu, Jiang; He, Jiping

2017-01-01

Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner. PMID:28223914

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

PubMed

Sartori, Massimo; Yavuz, Utku Ş; Farina, Dario

2017-10-18

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete α-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery.

Automated system for acquisition and image processing for the control and monitoring boned nopal

NASA Astrophysics Data System (ADS)

Luevano, E.; de Posada, E.; Arronte, M.; Ponce, L.; Flores, T.

2013-11-01

This paper describes the design and fabrication of a system for acquisition and image processing to control the removal of thorns nopal vegetable (Opuntia ficus indica) in an automated machine that uses pulses of a laser of Nd: YAG. The areolas, areas where thorns grow on the bark of the Nopal, are located applying segmentation algorithms to the images obtained by a CCD. Once the position of the areolas is known, coordinates are sent to a motors system that controls the laser to interact with all areolas and remove the thorns of the nopal. The electronic system comprises a video decoder, memory for image and software storage, and digital signal processor for system control. The firmware programmed tasks on acquisition, preprocessing, segmentation, recognition and interpretation of the areolas. This system achievement identifying areolas and generating table of coordinates of them, which will be send the motor galvo system that controls the laser for removal

Low Power LDPC Code Decoder Architecture Based on Intermediate Message Compression Technique

NASA Astrophysics Data System (ADS)

Shimizu, Kazunori; Togawa, Nozomu; Ikenaga, Takeshi; Goto, Satoshi

Reducing the power dissipation for LDPC code decoder is a major challenging task to apply it to the practical digital communication systems. In this paper, we propose a low power LDPC code decoder architecture based on an intermediate message-compression technique which features as follows: (i) An intermediate message compression technique enables the decoder to reduce the required memory capacity and write power dissipation. (ii) A clock gated shift register based intermediate message memory architecture enables the decoder to decompress the compressed messages in a single clock cycle while reducing the read power dissipation. The combination of the above two techniques enables the decoder to reduce the power dissipation while keeping the decoding throughput. The simulation results show that the proposed architecture improves the power efficiency up to 52% and 18% compared to that of the decoder based on the overlapped schedule and the rapid convergence schedule without the proposed techniques respectively.

Relationship between speed and EEG activity during imagined and executed hand movements

NASA Astrophysics Data System (ADS)

Yuan, Han; Perdoni, Christopher; He, Bin

2010-04-01

The relationship between primary motor cortex and movement kinematics has been shown in nonhuman primate studies of hand reaching or drawing tasks. Studies have demonstrated that the neural activities accompanying or immediately preceding the movement encode the direction, speed and other information. Here we investigated the relationship between the kinematics of imagined and actual hand movement, i.e. the clenching speed, and the EEG activity in ten human subjects. Study participants were asked to perform and imagine clenching of the left hand and right hand at various speeds. The EEG activity in the alpha (8-12 Hz) and beta (18-28 Hz) frequency bands were found to be linearly correlated with the speed of imagery clenching. Similar parametric modulation was also found during the execution of hand movements. A single equation relating the EEG activity to the speed and the hand (left versus right) was developed. This equation, which contained a linear independent combination of the two parameters, described the time-varying neural activity during the tasks. Based on the model, a regression approach was developed to decode the two parameters from the multiple-channel EEG signals. We demonstrated the continuous decoding of dynamic hand and speed information of the imagined clenching. In particular, the time-varying clenching speed was reconstructed in a bell-shaped profile. Our findings suggest an application to providing continuous and complex control of noninvasive brain-computer interface for movement-impaired paralytics.

Decoding grating orientation from microelectrode array recordings in monkey cortical area V4.

PubMed

Manyakov, Nikolay V; Van Hulle, Marc M

2010-04-01

We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the grating's spatial frequency. Since pyramidal cells are less prominent in visual areas, compared to (pre)motor areas, the recordings contain spikes with smaller amplitudes, compared to the noise level. Hence, rather than performing spike decoding, feature selection algorithms are applied to extract the required information for the decoder. Two types of feature selection procedures are compared, filter and wrapper. The wrapper is combined with a linear discriminant analysis classifier, and the filter is followed by a radial-basis function support vector machine classifier. In addition, since we have a multiclass classification problen, different methods for combining pairwise classifiers are compared.

User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

PubMed Central

Alonso-Valerdi, Luz M.; Gutiérrez-Begovich, David A.; Argüello-García, Janet; Sepulveda, Francisco; Ramírez-Mendoza, Ricardo A.

2016-01-01

Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system. PMID:27458384

Restoration of Hindlimb Movements after Complete Spinal Cord Injury Using Brain-Controlled Functional Electrical Stimulation.

PubMed

Knudsen, Eric B; Moxon, Karen A

2017-01-01

Single neuron and local field potential signals recorded in the primary motor cortex have been repeatedly demonstrated as viable control signals for multi-degree-of-freedom actuators. Although the primary source of these signals has been fore/upper limb motor regions, recent evidence suggests that neural adaptation underlying neuroprosthetic control is generalizable across cortex, including hindlimb sensorimotor cortex. Here, adult rats underwent a longitudinal study that included a hindlimb pedal press task in response to cues for specific durations, followed by brain machine interface (BMI) tasks in healthy rats, after rats received a complete spinal transection and after the BMI signal controls epidural stimulation (BMI-FES). Over the course of the transition from learned behavior to BMI task, fewer neurons were responsive after the cue, the proportion of neurons selective for press duration increased and these neurons carried more information. After a complete, mid-thoracic spinal lesion that completely severed both ascending and descending connections to the lower limbs, there was a reduction in task-responsive neurons followed by a reacquisition of task selectivity in recorded populations. This occurred due to a change in pattern of neuronal responses not simple changes in firing rate. Finally, during BMI-FES, additional information about the intended press duration was produced. This information was not dependent on the stimulation, which was the same for short and long duration presses during the early phase of stimulation, but instead was likely due to sensory feedback to sensorimotor cortex in response to movement along the trunk during the restored pedal press. This post-cue signal could be used as an error signal in a continuous decoder providing information about the position of the limb to optimally control a neuroprosthetic device.

Computerized trainings in four groups of struggling readers: Specific effects on word reading and comprehension.

PubMed

Potocki, Anna; Magnan, Annie; Ecalle, Jean

2015-01-01

Four groups of poor readers were identified among a population of students with learning disabilities attending a special class in secondary school: normal readers; specific poor decoders; specific poor comprehenders, and general poor readers (deficits in both decoding and comprehension). These students were then trained with a software program designed to encourage either their word decoding skills or their text comprehension skills. After 5 weeks of training, we observed that the students experiencing word reading deficits and trained with the decoding software improved primarily in the reading fluency task while those exhibiting comprehension deficits and trained with the comprehension software showed improved performance in listening and reading comprehension. But interestingly, the latter software also led to improved performance on the word recognition task. This result suggests that, for these students, training interventions focused at the text level and its comprehension might be more beneficial for reading in general (i.e., for the two components of reading) than word-level decoding trainings. Copyright © 2015 Elsevier Ltd. All rights reserved.

Observing human movements helps decoding environmental forces.

PubMed

Zago, Myrka; La Scaleia, Barbara; Miller, William L; Lacquaniti, Francesco

2011-11-01

Vision of human actions can affect several features of visual motion processing, as well as the motor responses of the observer. Here, we tested the hypothesis that action observation helps decoding environmental forces during the interception of a decelerating target within a brief time window, a task intrinsically very difficult. We employed a factorial design to evaluate the effects of scene orientation (normal or inverted) and target gravity (normal or inverted). Button-press triggered the motion of a bullet, a piston, or a human arm. We found that the timing errors were smaller for upright scenes irrespective of gravity direction in the Bullet group, while the errors were smaller for the standard condition of normal scene and gravity in the Piston group. In the Arm group, instead, performance was better when the directions of scene and target gravity were concordant, irrespective of whether both were upright or inverted. These results suggest that the default viewer-centered reference frame is used with inanimate scenes, such as those of the Bullet and Piston protocols. Instead, the presence of biological movements in animate scenes (as in the Arm protocol) may help processing target kinematics under the ecological conditions of coherence between scene and target gravity directions.

Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors.

PubMed

Samuel, Oluwarotimi Williams; Geng, Yanjuan; Li, Xiangxin; Li, Guanglin

2017-10-28

To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition (PR) of electromyogram (EMG) signals has been successfully applied. This technique requires amputees to provide sufficient EMG signals to decode their limb movement intentions (LMIs). However, amputees with neuromuscular disorder/high level amputation often cannot provide sufficient EMG control signals, and thus the applicability of the EMG-PR technique is limited especially to this category of amputees. As an alternative approach, electroencephalograph (EEG) signals recorded non-invasively from the brain have been utilized to decode the LMIs of humans. However, most of the existing EEG based limb movement decoding methods primarily focus on identifying limited classes of upper limb movements. In addition, investigation on EEG feature extraction methods for the decoding of multiple classes of LMIs has rarely been considered. Therefore, 32 EEG feature extraction methods (including 12 spectral domain descriptors (SDDs) and 20 time domain descriptors (TDDs)) were used to decode multiple classes of motor imagery patterns associated with different upper limb movements based on 64-channel EEG recordings. From the obtained experimental results, the best individual TDD achieved an accuracy of 67.05 ± 3.12% as against 87.03 ± 2.26% for the best SDD. By applying a linear feature combination technique, an optimal set of combined TDDs recorded an average accuracy of 90.68% while that of the SDDs achieved an accuracy of 99.55% which were significantly higher than those of the individual TDD and SDD at p < 0.05. Our findings suggest that optimal feature set combination would yield a relatively high decoding accuracy that may improve the clinical robustness of MDoF neuroprosthesis. The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.

Electroencephalography(EEG)-based instinctive brain-control of a quadruped locomotion robot.

PubMed

Jia, Wenchuan; Huang, Dandan; Luo, Xin; Pu, Huayan; Chen, Xuedong; Bai, Ou

2012-01-01

Artificial intelligence and bionic control have been applied in electroencephalography (EEG)-based robot system, to execute complex brain-control task. Nevertheless, due to technical limitations of the EEG decoding, the brain-computer interface (BCI) protocol is often complex, and the mapping between the EEG signal and the practical instructions lack of logic associated, which restrict the user's actual use. This paper presents a strategy that can be used to control a quadruped locomotion robot by user's instinctive action, based on five kinds of movement related neurophysiological signal. In actual use, the user drives or imagines the limbs/wrists action to generate EEG signal to adjust the real movement of the robot according to his/her own motor reflex of the robot locomotion. This method is easy for real use, as the user generates the brain-control signal through the instinctive reaction. By adopting the behavioral control of learning and evolution based on the proposed strategy, complex movement task may be realized by instinctive brain-control.

Decoding-Accuracy-Based Sequential Dimensionality Reduction of Spatio-Temporal Neural Activities

NASA Astrophysics Data System (ADS)

Funamizu, Akihiro; Kanzaki, Ryohei; Takahashi, Hirokazu

Performance of a brain machine interface (BMI) critically depends on selection of input data because information embedded in the neural activities is highly redundant. In addition, properly selected input data with a reduced dimension leads to improvement of decoding generalization ability and decrease of computational efforts, both of which are significant advantages for the clinical applications. In the present paper, we propose an algorithm of sequential dimensionality reduction (SDR) that effectively extracts motor/sensory related spatio-temporal neural activities. The algorithm gradually reduces input data dimension by dropping neural data spatio-temporally so as not to undermine the decoding accuracy as far as possible. Support vector machine (SVM) was used as the decoder, and tone-induced neural activities in rat auditory cortices were decoded into the test tone frequencies. SDR reduced the input data dimension to a quarter and significantly improved the accuracy of decoding of novel data. Moreover, spatio-temporal neural activity patterns selected by SDR resulted in significantly higher accuracy than high spike rate patterns or conventionally used spatial patterns. These results suggest that the proposed algorithm can improve the generalization ability and decrease the computational effort of decoding.

The ribosome as an optimal decoder: a lesson in molecular recognition.

PubMed

Savir, Yonatan; Tlusty, Tsvi

2013-04-11

The ribosome is a complex molecular machine that, in order to synthesize proteins, has to decode mRNAs by pairing their codons with matching tRNAs. Decoding is a major determinant of fitness and requires accurate and fast selection of correct tRNAs among many similar competitors. However, it is unclear whether the modern ribosome, and in particular its large conformational changes during decoding, are the outcome of adaptation to its task as a decoder or the result of other constraints. Here, we derive the energy landscape that provides optimal discrimination between competing substrates and thereby optimal tRNA decoding. We show that the measured landscape of the prokaryotic ribosome is sculpted in this way. This model suggests that conformational changes of the ribosome and tRNA during decoding are means to obtain an optimal decoder. Our analysis puts forward a generic mechanism that may be utilized broadly by molecular recognition systems. Copyright © 2013 Elsevier Inc. All rights reserved.

Assessing Specific Grapho-Phonemic Skills in Elementary Students

ERIC Educational Resources Information Center

Robbins, Kelly P.; Hosp, John L.; Hosp, Michelle K.; Flynn, Lindsay J.

2010-01-01

This study examines the relation between decoding and spelling performance on tasks that represent identical specific grapho-phonemic patterns. Elementary students (N = 206) were administered a 597 pseudoword decoding inventory representing 12 specific grapho-phonemic patterns and a 104 real-word spelling inventory representing identical…

More than meets the eye: the role of self-identity in decoding complex emotional states.

PubMed

Stevenson, Michael T; Soto, José A; Adams, Reginald B

2012-10-01

Folk wisdom asserts that "the eyes are the window to the soul," and empirical science corroborates a prominent role for the eyes in the communication of emotion. Herein we examine variation in the ability to "read" the eyes of others as a function of social group membership, employing a widely used emotional state decoding task: "Reading the Mind in Eyes." This task has documented impaired emotional state decoding across racial groups, with cross-race performance on par with that previously reported as a function of autism spectrum disorders. The present study extended this work by examining the moderating role of social identity in such impairments. For college students more highly identified with their university, cross-race performance differences were not found for judgments of "same-school" eyes but remained for "rival-school" eyes. These findings suggest that impaired emotional state decoding across groups may thus be more amenable to remediation than previously realized.

Grasp movement decoding from premotor and parietal cortex.

PubMed

Townsend, Benjamin R; Subasi, Erk; Scherberger, Hansjörg

2011-10-05

Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.

Decoding motor responses from the EEG during altered states of consciousness induced by propofol

NASA Astrophysics Data System (ADS)

Blokland, Yvonne; Farquhar, Jason; Lerou, Jos; Mourisse, Jo; Scheffer, Gert Jan; van Geffen, Geert-Jan; Spyrou, Loukianos; Bruhn, Jörgen

2016-04-01

Objective. Patients undergoing general anesthesia may awaken and become aware of the surgical procedure. Due to neuromuscular blocking agents, patients could be conscious yet unable to move. Using brain-computer interface (BCI) technology, it may be possible to detect movement attempts from the EEG. However, it is unknown how an anesthetic influences the brain response to motor tasks. Approach. We tested the offline classification performance of a movement-based BCI in 12 healthy subjects at two effect-site concentrations of propofol. For each subject a second classifier was trained on the subject’s data obtained before sedation, then tested on the data obtained during sedation (‘transfer classification’). Main results. At concentration 0.5 μg ml-1, despite an overall propofol EEG effect, the mean single trial classification accuracy was 85% (95% CI 81%-89%), and 83% (79%-88%) for the transfer classification. At 1.0 μg ml-1, the accuracies were 81% (76%-86%), and 72% (66%-79%), respectively. At the highest propofol concentration for four subjects, unlike the remaining subjects, the movement-related brain response had been largely diminished, and the transfer classification accuracy was not significantly above chance. These subjects showed a slower and more erratic task response, indicating an altered state of consciousness distinct from that of the other subjects. Significance. The results show the potential of using a BCI to detect intra-operative awareness and justify further development of this paradigm. At the same time, the relationship between motor responses and consciousness and its clinical relevance for intraoperative awareness requires further investigation.

De-Coding Writing Assignments.

ERIC Educational Resources Information Center

Simon, Linda

1991-01-01

Argues that understanding assignments is the first step toward successful college writing. Urges instructors to support students by helping them to decode assignments. Breaks down instructions into individual tasks including (1) writing an essay, (2) examining an issue, (3) reviewing articles and books, and (4) focusing on some texts. Defines each…

Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

2016-09-01

In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to "yes" or "no" intentions (e.g., mental arithmetic calculation for "yes"). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient's internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an "fNIRS-based direct intention decoding" paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing "yes" or "no" intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ±1.39 and 74.08% ±2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p<0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

Enhanced Motor Imagery-Based BCI Performance via Tactile Stimulation on Unilateral Hand.

PubMed

Shu, Xiaokang; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Zhu, Xiangyang

2017-01-01

Brain-computer interface (BCI) has attracted great interests for its effectiveness in assisting disabled people. However, due to the poor BCI performance, this technique is still far from daily-life applications. One of critical issues confronting BCI research is how to enhance BCI performance. This study aimed at improving the motor imagery (MI) based BCI accuracy by integrating MI tasks with unilateral tactile stimulation (Uni-TS). The effects were tested on both healthy subjects and stroke patients in a controlled study. Twenty-two healthy subjects and four stroke patients were recruited and randomly divided into a control-group and an enhanced-group. In the control-group, subjects performed two blocks of conventional MI tasks (left hand vs. right hand), with 80 trials in each block. In the enhanced-group, subjects also performed two blocks of MI tasks, but constant tactile stimulation was applied on the non-dominant/paretic hand during MI tasks in the second block. We found the Uni-TS significantly enhanced the contralateral cortical activations during MI of the stimulated hand, whereas it had no influence on activation patterns during MI of the non-stimulated hand. The two-class BCI decoding accuracy was significantly increased from 72.5% (MI without Uni-TS) to 84.7% (MI with Uni-TS) in the enhanced-group ( p < 0.001, paired t -test). Moreover, stroke patients in the enhanced-group achieved an accuracy >80% during MI with Uni-TS. This novel approach complements the conventional methods for BCI enhancement without increasing source information or complexity of signal processing. This enhancement via Uni-TS may facilitate clinical applications of MI-BCI.

An information-theoretic approach to motor action decoding with a reconfigurable parallel architecture.

PubMed

Craciun, Stefan; Brockmeier, Austin J; George, Alan D; Lam, Herman; Príncipe, José C

2011-01-01

Methods for decoding movements from neural spike counts using adaptive filters often rely on minimizing the mean-squared error. However, for non-Gaussian distribution of errors, this approach is not optimal for performance. Therefore, rather than using probabilistic modeling, we propose an alternate non-parametric approach. In order to extract more structure from the input signal (neuronal spike counts) we propose using minimum error entropy (MEE), an information-theoretic approach that minimizes the error entropy as part of an iterative cost function. However, the disadvantage of using MEE as the cost function for adaptive filters is the increase in computational complexity. In this paper we present a comparison between the decoding performance of the analytic Wiener filter and a linear filter trained with MEE, which is then mapped to a parallel architecture in reconfigurable hardware tailored to the computational needs of the MEE filter. We observe considerable speedup from the hardware design. The adaptation of filter weights for the multiple-input, multiple-output linear filters, necessary in motor decoding, is a highly parallelizable algorithm. It can be decomposed into many independent computational blocks with a parallel architecture readily mapped to a field-programmable gate array (FPGA) and scales to large numbers of neurons. By pipelining and parallelizing independent computations in the algorithm, the proposed parallel architecture has sublinear increases in execution time with respect to both window size and filter order.

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

PubMed Central

Clery, Stephane; Cumming, Bruce G.

2017-01-01

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal “noise” correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. SIGNIFICANCE STATEMENT Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. PMID:28100751

A closed-loop neurobotic system for fine touch sensing

NASA Astrophysics Data System (ADS)

Bologna, L. L.; Pinoteau, J.; Passot, J.-B.; Garrido, J. A.; Vogel, J.; Ros Vidal, E.; Arleo, A.

2013-08-01

Objective. Fine touch sensing relies on peripheral-to-central neurotransmission of somesthetic percepts, as well as on active motion policies shaping tactile exploration. This paper presents a novel neuroengineering framework for robotic applications based on the multistage processing of fine tactile information in the closed action-perception loop. Approach. The integrated system modules focus on (i) neural coding principles of spatiotemporal spiking patterns at the periphery of the somatosensory pathway, (ii) probabilistic decoding mechanisms mediating cortical-like tactile recognition and (iii) decision-making and low-level motor adaptation underlying active touch sensing. We probed the resulting neural architecture through a Braille reading task. Main results. Our results on the peripheral encoding of primary contact features are consistent with experimental data on human slow-adapting type I mechanoreceptors. They also suggest second-order processing by cuneate neurons may resolve perceptual ambiguities, contributing to a fast and highly performing online discrimination of Braille inputs by a downstream probabilistic decoder. The implemented multilevel adaptive control provides robustness to motion inaccuracy, while making the number of finger accelerations covariate with Braille character complexity. The resulting modulation of fingertip kinematics is coherent with that observed in human Braille readers. Significance. This work provides a basis for the design and implementation of modular neuromimetic systems for fine touch discrimination in robotics.

The role of short-term memory impairment in nonword repetition, real word repetition, and nonword decoding: A case study.

PubMed

Peter, Beate

2018-01-01

In a companion study, adults with dyslexia and adults with a probable history of childhood apraxia of speech showed evidence of difficulty with processing sequential information during nonword repetition, multisyllabic real word repetition and nonword decoding. Results suggested that some errors arose in visual encoding during nonword reading, all levels of processing but especially short-term memory storage/retrieval during nonword repetition, and motor planning and programming during complex real word repetition. To further investigate the role of short-term memory, a participant with short-term memory impairment (MI) was recruited. MI was confirmed with poor performance during a sentence repetition and three nonword repetition tasks, all of which have a high short-term memory load, whereas typical performance was observed during tests of reading, spelling, and static verbal knowledge, all with low short-term memory loads. Experimental results show error-free performance during multisyllabic real word repetition but high counts of sequence errors, especially migrations and assimilations, during nonword repetition, supporting short-term memory as a locus of sequential processing deficit during nonword repetition. Results are also consistent with the hypothesis that during complex real word repetition, short-term memory is bypassed as the word is recognized and retrieved from long-term memory prior to producing the word.

A four-dimensional virtual hand brain-machine interface using active dimension selection.

PubMed

Rouse, Adam G

2016-06-01

Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored. ADS utilizes a two stage decoder by using neural signals to both (i) select an active dimension being controlled and (ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures. Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits s(-1) for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control. ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand.

Serotonin and Dopamine Gene Variation and Theory of Mind Decoding Accuracy in Major Depression: A Preliminary Investigation.

PubMed

Zahavi, Arielle Y; Sabbagh, Mark A; Washburn, Dustin; Mazurka, Raegan; Bagby, R Michael; Strauss, John; Kennedy, James L; Ravindran, Arun; Harkness, Kate L

2016-01-01

Theory of mind-the ability to decode and reason about others' mental states-is a universal human skill and forms the basis of social cognition. Theory of mind accuracy is impaired in clinical conditions evidencing social impairment, including major depressive disorder. The current study is a preliminary investigation of the association of polymorphisms of the serotonin transporter (SLC6A4), dopamine transporter (DAT1), dopamine receptor D4 (DRD4), and catechol-O-methyl transferase (COMT) genes with theory of mind decoding in a sample of adults with major depression. Ninety-six young adults (38 depressed, 58 non-depressed) completed the 'Reading the Mind in the Eyes task' and a non-mentalistic control task. Genetic associations were only found for the depressed group. Specifically, superior accuracy in decoding mental states of a positive valence was seen in those homozygous for the long allele of the serotonin transporter gene, 9-allele carriers of DAT1, and long-allele carriers of DRD4. In contrast, superior accuracy in decoding mental states of a negative valence was seen in short-allele carriers of the serotonin transporter gene and 10/10 homozygotes of DAT1. Results are discussed in terms of their implications for integrating social cognitive and neurobiological models of etiology in major depression.

Music models aberrant rule decoding and reward valuation in dementia

PubMed Central

Clark, Camilla N; Golden, Hannah L; McCallion, Oliver; Nicholas, Jennifer M; Cohen, Miriam H; Slattery, Catherine F; Paterson, Ross W; Fletcher, Phillip D; Mummery, Catherine J; Rohrer, Jonathan D; Crutch, Sebastian J; Warren, Jason D

2018-01-01

Abstract Aberrant rule- and reward-based processes underpin abnormalities of socio-emotional behaviour in major dementias. However, these processes remain poorly characterized. Here we used music to probe rule decoding and reward valuation in patients with frontotemporal dementia (FTD) syndromes and Alzheimer’s disease (AD) relative to healthy age-matched individuals. We created short melodies that were either harmonically resolved (‘finished’) or unresolved (‘unfinished’); the task was to classify each melody as finished or unfinished (rule processing) and rate its subjective pleasantness (reward valuation). Results were adjusted for elementary pitch and executive processing; neuroanatomical correlates were assessed using voxel-based morphometry. Relative to healthy older controls, patients with behavioural variant FTD showed impairments of both musical rule decoding and reward valuation, while patients with semantic dementia showed impaired reward valuation but intact rule decoding, patients with AD showed impaired rule decoding but intact reward valuation and patients with progressive non-fluent aphasia performed comparably to healthy controls. Grey matter associations with task performance were identified in anterior temporal, medial and lateral orbitofrontal cortices, previously implicated in computing diverse biological and non-biological rules and rewards. The processing of musical rules and reward distils cognitive and neuroanatomical mechanisms relevant to complex socio-emotional dysfunction in major dementias. PMID:29186630

The role of working memory in decoding emotions.

PubMed

Phillips, Louise H; Channon, Shelley; Tunstall, Mary; Hedenstrom, Anna; Lyons, Kathryn

2008-04-01

Decoding facial expressions of emotion is an important aspect of social communication that is often impaired following psychiatric or neurological illness. However, little is known of the cognitive components involved in perceiving emotional expressions. Three dual task studies explored the role of verbal working memory in decoding emotions. Concurrent working memory load substantially interfered with choosing which emotional label described a facial expression (Experiment 1). A key factor in the magnitude of interference was the number of emotion labels from which to choose (Experiment 2). In contrast the ability to decide that two faces represented the same emotion in a discrimination task was relatively unaffected by concurrent working memory load (Experiment 3). Different methods of assessing emotion perception make substantially different demands on working memory. Implications for clinical disorders which affect both working memory and emotion perception are considered. (Copyright) 2008 APA.

A systematic approach to selecting task relevant neurons.

PubMed

Kahn, Kevin; Saxena, Shreya; Eskandar, Emad; Thakor, Nitish; Schieber, Marc; Gale, John T; Averbeck, Bruno; Eden, Uri; Sarma, Sridevi V

2015-04-30

Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of an iPad-Supported Phonics Intervention on Decoding Performance and Time On-Task

ERIC Educational Resources Information Center

Larabee, Kaitlyn M.; Burns, Matthew K.; McComas, Jennifer J.

2014-01-01

Despite their recent popularity in schools, there is minimal consensus in the educational literature regarding the use of mobile devices for reading intervention. The word box intervention (Joseph "Read Teach" 52:348-356, 1998) has been consistently associated with improvements in student decoding performance. This early efficacy study…

Decoding the Disciplines: An Approach to Scientific Thinking

ERIC Educational Resources Information Center

Pinnow, Eleni

2016-01-01

The Decoding the Disciplines methodology aims to teach students to think like experts in discipline-specific tasks. The central aspect of the methodology is to identify a bottleneck in the course content: a particular topic that a substantial number of students struggle to master. The current study compared the efficacy of standard lecture and…

Crossover of Phonological Processing Skills: A Study of Spanish-Speaking Students in Two Instructional Settings

ERIC Educational Resources Information Center

Leafstedt, Jill M.; Gerber, Michael M.

2005-01-01

This study investigated three questions: Do phonological processes show cross-linguistic transfer? How does the language of instruction influence the relationship between phonological processes and decoding? Does performance on Spanish and English phonological processing tasks similarly predict English decoding for the same English learners (ELs)?…

Accelerating a MPEG-4 video decoder through custom software/hardware co-design

NASA Astrophysics Data System (ADS)

Díaz, Jorge L.; Barreto, Dacil; García, Luz; Marrero, Gustavo; Carballo, Pedro P.; Núñez, Antonio

2007-05-01

In this paper we present a novel methodology to accelerate an MPEG-4 video decoder using software/hardware co-design for wireless DAB/DMB networks. Software support includes the services provided by the embedded kernel μC/OS-II, and the application tasks mapped to software. Hardware support includes several custom co-processors and a communication architecture with bridges to the main system bus and with a dual port SRAM. Synchronization among tasks is achieved at two levels, by a hardware protocol and by kernel level scheduling services. Our reference application is an MPEG-4 video decoder composed of several software functions and written using a special C++ library named CASSE. Profiling and space exploration techniques were used previously over the Advanced Simple Profile (ASP) MPEG-4 decoder to determinate the best HW/SW partition developed here. This research is part of the ARTEMI project and its main goal is the establishment of methodologies for the design of real-time complex digital systems using Programmable Logic Devices with embedded microprocessors as target technology and the design of multimedia systems for broadcasting networks as reference application.

Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding.

PubMed

Bulea, Thomas C; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H; Contreras-Vidal, Jose L

2013-07-26

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Decoding Information for Grasping from the Macaque Dorsomedial Visual Stream.

PubMed

Filippini, Matteo; Breveglieri, Rossella; Akhras, M Ali; Bosco, Annalisa; Chinellato, Eris; Fattori, Patrizia

2017-04-19

Neurodecoders have been developed by researchers mostly to control neuroprosthetic devices, but also to shed new light on neural functions. In this study, we show that signals representing grip configurations can be reliably decoded from neural data acquired from area V6A of the monkey medial posterior parietal cortex. Two Macaca fascicularis monkeys were trained to perform an instructed-delay reach-to-grasp task in the dark and in the light toward objects of different shapes. Population neural activity was extracted at various time intervals on vision of the objects, the delay before movement, and grasp execution. This activity was used to train and validate a Bayes classifier used for decoding objects and grip types. Recognition rates were well over chance level for all the epochs analyzed in this study. Furthermore, we detected slightly different decoding accuracies, depending on the task's visual condition. Generalization analysis was performed by training and testing the system during different time intervals. This analysis demonstrated that a change of code occurred during the course of the task. Our classifier was able to discriminate grasp types fairly well in advance with respect to grasping onset. This feature might be important when the timing is critical to send signals to external devices before the movement start. Our results suggest that the neural signals from the dorsomedial visual pathway can be a good substrate to feed neural prostheses for prehensile actions. SIGNIFICANCE STATEMENT Recordings of neural activity from nonhuman primate frontal and parietal cortex have led to the development of methods of decoding movement information to restore coordinated arm actions in paralyzed human beings. Our results show that the signals measured from the monkey medial posterior parietal cortex are valid for correctly decoding information relevant for grasping. Together with previous studies on decoding reach trajectories from the medial posterior parietal cortex, this highlights the medial parietal cortex as a target site for transforming neural activity into control signals to command prostheses to allow human patients to dexterously perform grasping actions. Copyright © 2017 the authors 0270-6474/17/374311-12$15.00/0.

Neuroimaging of decoding and language comprehension in young very low birth weight (VLBW) adolescents: Indications for compensatory mechanisms.

PubMed

van Ettinger-Veenstra, Helene; Widén, Carin; Engström, Maria; Karlsson, Thomas; Leijon, Ingemar; Nelson, Nina

2017-01-01

In preterm children with very low birth weight (VLBW ≤ 1500 g), reading problems are often observed. Reading comprehension is dependent on word decoding and language comprehension. We investigated neural activation-within brain regions important for reading-related to components of reading comprehension in young VLBW adolescents in direct comparison to normal birth weight (NBW) term-born peers, with the use of functional magnetic resonance imaging (fMRI). We hypothesized that the decoding mechanisms will be affected by VLBW, and expect to see increased neural activity for VLBW which may be modulated by task performance and cognitive ability. The study investigated 13 (11 included in fMRI) young adolescents (ages 12 to 14 years) born preterm with VLBW and in 13 NBW controls (ages 12-14 years) for performance on the Block Design and Vocabulary subtests of the Wechsler Intelligence Scale for Children; and for semantic, orthographic, and phonological processing during an fMRI paradigm. The VLBW group showed increased phonological activation in left inferior frontal gyrus, decreased orthographic activation in right supramarginal gyrus, and decreased semantic activation in left inferior frontal gyrus. Block Design was related to altered right-hemispheric activation, and VLBW showed lower WISC Block Design scores. Left angular gyrus showed activation increase specific for VLBW with high accuracy on the semantic test. Young VLBW adolescents showed no accuracy and reaction time performance differences on our fMRI language tasks, but they did exhibit altered neural activation during these tasks. This altered activation for VLBW was observed as increased activation during phonological decoding, and as mainly decreased activation during orthographic and semantic processing. Correlations of neural activation with accuracy on the semantic fMRI task and with decreased WISC Block Design performance were specific for the VLBW group. Together, results suggest compensatory mechanisms by recruiting additional brain regions upon altered neural development of decoding for VLBW.

Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials

PubMed Central

Iturrate, Iñaki; Grizou, Jonathan; Omedes, Jason; Oudeyer, Pierre-Yves; Lopes, Manuel; Montesano, Luis

2015-01-01

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach. PMID:26131890

Deep learning with convolutional neural networks for EEG decoding and visualization.

PubMed

Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-11-01

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Reading Comprehension in a Large Cohort of French First Graders from Low Socio-Economic Status Families: A 7-Month Longitudinal Study

PubMed Central

Gentaz, Edouard; Sprenger-Charolles, Liliane; Theurel, Anne; Colé, Pascale

2013-01-01

Background The literature suggests that a complex relationship exists between the three main skills involved in reading comprehension (decoding, listening comprehension and vocabulary) and that this relationship depends on at least three other factors orthographic transparency, children’s grade level and socioeconomic status (SES). This study investigated the relative contribution of the predictors of reading comprehension in a longitudinal design (from beginning to end of the first grade) in 394 French children from low SES families. Methodology/Principal findings Reading comprehension was measured at the end of the first grade using two tasks one with short utterances and one with a medium length narrative text. Accuracy in listening comprehension and vocabulary, and fluency of decoding skills, were measured at the beginning and end of the first grade. Accuracy in decoding skills was measured only at the beginning. Regression analyses showed that listening comprehension and decoding skills (accuracy and fluency) always significantly predicted reading comprehension. The contribution of decoding was greater when reading comprehension was assessed via the task using short utterances. Between the two assessments, the contribution of vocabulary, and of decoding skills especially, increased, while that of listening comprehension remained unchanged. Conclusion/Significance These results challenge the ‘simple view of reading’. They also have educational implications, since they show that it is possible to assess decoding and reading comprehension very early on in an orthography (i.e., French), which is less deep than the English one even in low SES children. These assessments, associated with those of listening comprehension and vocabulary, may allow early identification of children at risk for reading difficulty, and to set up early remedial training, which is the most effective, for them. PMID:24250802

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

PubMed

Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

2016-09-01

The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

Real-time SHVC software decoding with multi-threaded parallel processing

NASA Astrophysics Data System (ADS)

Gudumasu, Srinivas; He, Yuwen; Ye, Yan; He, Yong; Ryu, Eun-Seok; Dong, Jie; Xiu, Xiaoyu

2014-09-01

This paper proposes a parallel decoding framework for scalable HEVC (SHVC). Various optimization technologies are implemented on the basis of SHVC reference software SHM-2.0 to achieve real-time decoding speed for the two layer spatial scalability configuration. SHVC decoder complexity is analyzed with profiling information. The decoding process at each layer and the up-sampling process are designed in parallel and scheduled by a high level application task manager. Within each layer, multi-threaded decoding is applied to accelerate the layer decoding speed. Entropy decoding, reconstruction, and in-loop processing are pipeline designed with multiple threads based on groups of coding tree units (CTU). A group of CTUs is treated as a processing unit in each pipeline stage to achieve a better trade-off between parallelism and synchronization. Motion compensation, inverse quantization, and inverse transform modules are further optimized with SSE4 SIMD instructions. Simulations on a desktop with an Intel i7 processor 2600 running at 3.4 GHz show that the parallel SHVC software decoder is able to decode 1080p spatial 2x at up to 60 fps (frames per second) and 1080p spatial 1.5x at up to 50 fps for those bitstreams generated with SHVC common test conditions in the JCT-VC standardization group. The decoding performance at various bitrates with different optimization technologies and different numbers of threads are compared in terms of decoding speed and resource usage, including processor and memory.

Toward Optimal Target Placement for Neural Prosthetic Devices

PubMed Central

Cunningham, John P.; Yu, Byron M.; Gilja, Vikash; Ryu, Stephen I.; Shenoy, Krishna V.

2008-01-01

Neural prosthetic systems have been designed to estimate continuous reach trajectories (motor prostheses) and to predict discrete reach targets (communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses. PMID:18829845

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation.

PubMed

Saproo, Sameer; Shih, Victor; Jangraw, David C; Sajda, Paul

2016-12-01

We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs). We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)-anterior cingulate cortex (ACC) circuit. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

NASA Astrophysics Data System (ADS)

Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

2016-12-01

Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

From language comprehension to action understanding and back again.

PubMed

Tremblay, Pascale; Small, Steven L

2011-05-01

A controversial question in cognitive neuroscience is whether comprehension of words and sentences engages brain mechanisms specific for decoding linguistic meaning or whether language comprehension occurs through more domain-general sensorimotor processes. Accumulating behavioral and neuroimaging evidence suggests a role for cortical motor and premotor areas in passive action-related language tasks, regions that are known to be involved in action execution and observation. To examine the involvement of these brain regions in language and nonlanguage tasks, we used functional magnetic resonance imaging (fMRI) on a group of 21 healthy adults. During the fMRI session, all participants 1) watched short object-related action movies, 2) looked at pictures of man-made objects, and 3) listened to and produced short sentences describing object-related actions and man-made objects. Our results are among the first to reveal, in the human brain, a functional specialization within the ventral premotor cortex (PMv) for observing actions and for observing objects, and a different organization for processing sentences describing actions and objects. These findings argue against the strongest version of the simulation theory for the processing of action-related language.

Utilizing sensory prediction errors for movement intention decoding: A new methodology

PubMed Central

Nakamura, Keigo; Ando, Hideyuki

2018-01-01

We propose a new methodology for decoding movement intentions of humans. This methodology is motivated by the well-documented ability of the brain to predict sensory outcomes of self-generated and imagined actions using so-called forward models. We propose to subliminally stimulate the sensory modality corresponding to a user’s intended movement, and decode a user’s movement intention from his electroencephalography (EEG), by decoding for prediction errors—whether the sensory prediction corresponding to a user’s intended movement matches the subliminal sensory stimulation we induce. We tested our proposal in a binary wheelchair turning task in which users thought of turning their wheelchair either left or right. We stimulated their vestibular system subliminally, toward either the left or the right direction, using a galvanic vestibular stimulator and show that the decoding for prediction errors from the EEG can radically improve movement intention decoding performance. We observed an 87.2% median single-trial decoding accuracy across tested participants, with zero user training, within 96 ms of the stimulation, and with no additional cognitive load on the users because the stimulation was subliminal. PMID:29750195

Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects

PubMed Central

Walter, Armin; Murguialday, Ander R.; Rosenstiel, Wolfgang; Birbaumer, Niels; Bogdan, Martin

2012-01-01

Brain-state-dependent stimulation (BSDS) combines brain-computer interfaces (BCIs) and cortical stimulation into one paradigm that allows the online decoding for example of movement intention from brain signals while simultaneously applying stimulation. If the BCI decoding is performed by spectral features, stimulation after-effects such as artefacts and evoked activity present a challenge for a successful implementation of BSDS because they can impair the detection of targeted brain states. Therefore, efficient and robust methods are needed to minimize the influence of the stimulation-induced effects on spectral estimation without violating the real-time constraints of the BCI. In this work, we compared four methods for spectral estimation with autoregressive (AR) models in the presence of pulsed cortical stimulation. Using combined EEG-TMS (electroencephalography-transcranial magnetic stimulation) as well as combined electrocorticography (ECoG) and epidural electrical stimulation, three patients performed a motor task using a sensorimotor-rhythm BCI. Three stimulation paradigms were varied between sessions: (1) no stimulation, (2) single stimulation pulses applied independently (open-loop), or (3) coupled to the BCI output (closed-loop) such that stimulation was given only while an intention to move was detected using neural data. We found that removing the stimulation after-effects by linear interpolation can introduce a bias in the estimation of the spectral power of the sensorimotor rhythm, leading to an overestimation of decoding performance in the closed-loop setting. We propose the use of the Burg algorithm for segmented data to deal with stimulation after-effects. This work shows that the combination of BCIs controlled with spectral features and cortical stimulation in a closed-loop fashion is possible when the influence of stimulation after-effects on spectral estimation is minimized. PMID:23162436

Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

PubMed Central

Malik, Wasim Q.; Truccolo, Wilson; Brown, Emery N.; Hochberg, Leigh R.

2011-01-01

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 ± 0.5 s (mean ± s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25 ± 3 single units by a factor of 7.0 ± 0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems. PMID:21078582

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout.

PubMed

Clery, Stephane; Cumming, Bruce G; Nienborg, Hendrikje

2017-01-18

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. Copyright © 2017 the authors 0270-6474/17/370715-11$15.00/0.

A four-dimensional virtual hand brain-machine interface using active dimension selection

NASA Astrophysics Data System (ADS)

Rouse, Adam G.

2016-06-01

Objective. Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored. Approach. ADS utilizes a two stage decoder by using neural signals to both (i) select an active dimension being controlled and (ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures. Main results. Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits s-1 for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control. Significance. ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand.

A four-dimensional virtual hand brain-machine interface using active dimension selection

PubMed Central

Rouse, Adam G.

2018-01-01

Objective Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored. Approach ADS utilizes a two stage decoder by using neural signals to both i) select an active dimension being controlled and ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures. Main Results Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits/s for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control. Significance ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand. PMID:27171896

Highly efficient simulation environment for HDTV video decoder in VLSI design

NASA Astrophysics Data System (ADS)

Mao, Xun; Wang, Wei; Gong, Huimin; He, Yan L.; Lou, Jian; Yu, Lu; Yao, Qingdong; Pirsch, Peter

2002-01-01

With the increase of the complex of VLSI such as the SoC (System on Chip) of MPEG-2 Video decoder with HDTV scalability especially, simulation and verification of the full design, even as high as the behavior level in HDL, often proves to be very slow, costly and it is difficult to perform full verification until late in the design process. Therefore, they become bottleneck of the procedure of HDTV video decoder design, and influence it's time-to-market mostly. In this paper, the architecture of Hardware/Software Interface of HDTV video decoder is studied, and a Hardware-Software Mixed Simulation (HSMS) platform is proposed to check and correct error in the early design stage, based on the algorithm of MPEG-2 video decoding. The application of HSMS to target system could be achieved by employing several introduced approaches. Those approaches speed up the simulation and verification task without decreasing performance.

Evidence for Impaired Verbal Identification but Intact Nonverbal Recognition of Fearful Body Postures in Asperger's Syndrome

ERIC Educational Resources Information Center

Doody, John P.; Bull, Peter

2013-01-01

While most studies of emotion recognition in Asperger's Syndrome (AS) have focused solely on the verbal decoding of affective states, the current research employed the novel technique of using both nonverbal matching and verbal labeling tasks to examine the decoding of emotional body postures and facial expressions. AS participants performed…

The Perception and Decoding of Expressive Emotional Information by Hearing and Hearing-Impaired Children.

ERIC Educational Resources Information Center

Sanders, Gina

1985-01-01

Hearing and hearing-impaired children between ages 4.5 to 15.5 years in England and Belgium were invited to abstract the concept of emotion from photographs and line drawings of facial expressions and body postures. A further experiment isloated the element of context in the task of decoding expression of emotion, resulting in comparatively…

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

PubMed Central

Bulea, Thomas C.; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H.; Contreras-Vidal, Jose L.

2013-01-01

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG. PMID:23912203

Dissociating functional brain networks by decoding the between-subject variability

PubMed Central

Seghier, Mohamed L.; Price, Cathy J.

2009-01-01

In this study we illustrate how the functional networks involved in a single task (e.g. the sensory, cognitive and motor components) can be segregated without cognitive subtractions at the second-level. The method used is based on meaningful variability in the patterns of activation between subjects with the assumption that regions belonging to the same network will have comparable variations from subject to subject. fMRI data were collected from thirty nine healthy volunteers who were asked to indicate with a button press if visually presented words were semantically related or not. Voxels were classified according to the similarity in their patterns of between-subject variance using a second-level unsupervised fuzzy clustering algorithm. The results were compared to those identified by cognitive subtractions of multiple conditions tested in the same set of subjects. This illustrated that the second-level clustering approach (on activation for a single task) was able to identify the functional networks observed using cognitive subtractions (e.g. those associated with vision, semantic associations or motor processing). In addition the fuzzy clustering approach revealed other networks that were not dissociated by the cognitive subtraction approach (e.g. those associated with high- and low-level visual processing and oculomotor movements). We discuss the potential applications of our method which include the identification of “hidden” or unpredicted networks as well as the identification of systems level signatures for different subgroupings of clinical and healthy populations. PMID:19150501

A longitudinal analysis of sex differences in math and spatial skills in primary school age children☆

PubMed Central

Lachance, Jennifer A.; Mazzocco, Michèle M.M.

2009-01-01

We report on a longitudinal study designed to assess possible sex differences in math achievement, math ability, and math-related tasks during the primary school age years. Participants included over 200 children from one public school district. Annual assessments included measures of math ability, math calculation achievement scores, rapid naming and decoding tasks, visual perception tests, visual motor tasks, and reading skills. During select years of the study we also administered tests of counting and math facts skills. We examined whether girls or boys were overrepresented among the bottom or top performers on any of these tasks, relative to their peers, and whether growth rates or predictors of math-related skills differed for boys and girls. Our findings support the notion that sex differences in math are minimal or nonexistent on standardized psychometric tests routinely given in assessments of primary school age children. There was no persistent finding suggesting a male or female advantage in math performance overall, during any single year of the study, or in any one area of math or spatial skills. Growth rates for all skills, and early correlates of later math performance, were comparable for boys and girls. The findings fail to support either persistent or emerging sex differences on non-specialized math ability measures during the primary school age years. PMID:20463851

A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models.

PubMed

Kao, Jonathan C; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V

2017-04-01

Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

NASA Astrophysics Data System (ADS)

Hochberg, Leigh R.; Serruya, Mijail D.; Friehs, Gerhard M.; Mukand, Jon A.; Saleh, Maryam; Caplan, Abraham H.; Branner, Almut; Chen, David; Penn, Richard D.; Donoghue, John P.

2006-07-01

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a `neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.

Using Event Related Potentials to Explore Stages of Facial Affect Recognition Deficits in Schizophrenia

PubMed Central

Wynn, Jonathan K.; Lee, Junghee; Horan, William P.; Green, Michael F.

2008-01-01

Schizophrenia patients show impairments in identifying facial affect; however, it is not known at what stage facial affect processing is impaired. We evaluated 3 event-related potentials (ERPs) to explore stages of facial affect processing in schizophrenia patients. Twenty-six schizophrenia patients and 27 normal controls participated. In separate blocks, subjects identified the gender of a face, the emotion of a face, or if a building had 1 or 2 stories. Three ERPs were examined: (1) P100 to examine basic visual processing, (2) N170 to examine facial feature encoding, and (3) N250 to examine affect decoding. Behavioral performance on each task was also measured. Results showed that schizophrenia patients’ P100 was comparable to the controls during all 3 identification tasks. Both patients and controls exhibited a comparable N170 that was largest during processing of faces and smallest during processing of buildings. For both groups, the N250 was largest during the emotion identification task and smallest for the building identification task. However, the patients produced a smaller N250 compared with the controls across the 3 tasks. The groups did not differ in behavioral performance in any of the 3 identification tasks. The pattern of intact P100 and N170 suggest that patients maintain basic visual processing and facial feature encoding abilities. The abnormal N250 suggests that schizophrenia patients are less efficient at decoding facial affect features. Our results imply that abnormalities in the later stage of feature decoding could potentially underlie emotion identification deficits in schizophrenia. PMID:18499704

Online decoding of object-based attention using real-time fMRI.

PubMed

Niazi, Adnan M; van den Broek, Philip L C; Klanke, Stefan; Barth, Markus; Poel, Mannes; Desain, Peter; van Gerven, Marcel A J

2014-01-01

Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for moment-to-moment decoding of attention to spatially overlapped objects belonging to two different object categories. First, a whole-brain classifier was trained on pictures of faces and places. Subjects then saw transparently overlapped pictures of a face and a place, and attended to only one of them while ignoring the other. The category of the attended object, face or place, was decoded on a scan-by-scan basis using the previously trained decoder. The decoder performed at 77.6% accuracy indicating that despite competing bottom-up sensory input, object-based visual attention biased neural patterns towards that of the attended object. Furthermore, a comparison between different classification approaches indicated that the representation of faces and places is distributed rather than focal. This implies that real-time decoding of object-based attention requires a multivariate decoding approach that can detect these distributed patterns of cortical activity. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

The Applicability of Rhythm-Motor Tasks to a New Dual Task Paradigm for Older Adults

PubMed Central

Kim, Soo Ji; Cho, Sung-Rae; Yoo, Ga Eul

2017-01-01

Given the interplay between cognitive and motor functions during walking, cognitive demands required during gait have been investigated with regard to dual task performance. Along with the needs to understand how the type of concurrent task while walking affects gait performance, there are calls for diversified dual tasks that can be applied to older adults with varying levels of cognitive decline. Therefore, this study aimed to examine how rhythm-motor tasks affect dual task performance and gait control, compared to a traditional cognitive-motor task. Also, it examined whether rhythm-motor tasks are correlated with traditional cognitive-motor task performance and cognitive measures. Eighteen older adults without cognitive impairment participated in this study. Each participant was instructed to walk at self-paced tempo without performing a concurrent task (single walking task) and walk while separately performing two types of concurrent tasks: rhythm-motor and cognitive-motor tasks. Rhythm-motor tasks included instrument playing (WalkIP), matching to rhythmic cueing (WalkRC), and instrument playing while matching to rhythmic cueing (WalkIP+RC). The cognitive-motor task involved counting forward by 3s (WalkCount.f3). In each condition, dual task costs (DTC), a measure for how dual tasks affect gait parameters, were measured in terms of walking speed and stride length. The ratio of stride length to walking speed, a measure for dynamic control of gait, was also examined. The results of this study demonstrated that the task type was found to significantly influence these measures. Rhythm-motor tasks were found to interfere with gait parameters to a lesser extent than the cognitive-motor task (WalkCount.f3). In terms of ratio measures, stride length remained at a similar level, walking speed greatly decreased in the WalkCount.f3 condition. Significant correlations between dual task-related measures during rhythm-motor and cognitive-motor tasks support the potential of applying rhythm-motor tasks to dual task methodology. This study presents how rhythm-motor tasks demand cognitive control at different levels than those engaged by cognitive-motor tasks. It also indicates how these new dual tasks can effectively mediate dual task performance indicative of fall risks, while requiring increased cognitive resources but facilitating gait control as a compensatory strategy to maintain gait stability. PMID:29375462

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

PubMed

Mastinu, Enzo; Doguet, Pascal; Botquin, Yohan; Hakansson, Bo; Ortiz-Catalan, Max

2017-08-01

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

NASA Astrophysics Data System (ADS)

Omurtag, Ahmet; Aghajani, Haleh; Onur Keles, Hasan

2017-12-01

Objective. Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system’s ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results. EEG+fNIRS’s decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance. Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain

PubMed Central

Chen, Quanjing; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. PMID:25595179

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control.

PubMed

Tang, Zhichuan; Sun, Shouqian; Zhang, Sanyuan; Chen, Yumiao; Li, Chao; Chen, Shi

2016-12-02

To recognize the user's motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.

How Word Decoding, Vocabulary and Prior Topic Knowledge Predict Reading Comprehension. A Study of Language-Minority Students in Norwegian Fifth Grade Classrooms

ERIC Educational Resources Information Center

Rydland, Veslemoy; Aukrust, Vibeke Grover; Fulland, Helene

2012-01-01

This study examined the contribution of word decoding, first-language (L1) and second-language (L2) vocabulary and prior topic knowledge to L2 reading comprehension. For measuring reading comprehension we employed two different reading tasks: Woodcock Passage Comprehension and a researcher-developed content-area reading assignment (the Global…

Multimodal decoding and congruent sensory information enhance reaching performance in subjects with cervical spinal cord injury.

PubMed

Corbett, Elaine A; Sachs, Nicholas A; Körding, Konrad P; Perreault, Eric J

2014-01-01

Cervical spinal cord injury (SCI) paralyzes muscles of the hand and arm, making it difficult to perform activities of daily living. Restoring the ability to reach can dramatically improve quality of life for people with cervical SCI. Any reaching system requires a user interface to decode parameters of an intended reach, such as trajectory and target. A challenge in developing such decoders is that often few physiological signals related to the intended reach remain under voluntary control, especially in patients with high cervical injuries. Furthermore, the decoding problem changes when the user is controlling the motion of their limb, as opposed to an external device. The purpose of this study was to investigate the benefits of combining disparate signal sources to control reach in people with a range of impairments, and to consider the effect of two feedback approaches. Subjects with cervical SCI performed robot-assisted reaching, controlling trajectories with either shoulder electromyograms (EMGs) or EMGs combined with gaze. We then evaluated how reaching performance was influenced by task-related sensory feedback, testing the EMG-only decoder in two conditions. The first involved moving the arm with the robot, providing congruent sensory feedback through their remaining sense of proprioception. In the second, the subjects moved the robot without the arm attached, as in applications that control external devices. We found that the multimodal-decoding algorithm worked well for all subjects, enabling them to perform straight, accurate reaches. The inclusion of gaze information, used to estimate target location, was especially important for the most impaired subjects. In the absence of gaze information, congruent sensory feedback improved performance. These results highlight the importance of proprioceptive feedback, and suggest that multi-modal decoders are likely to be most beneficial for highly impaired subjects and in tasks where such feedback is unavailable.

From intentions to actions: Neural oscillations encode motor processes through phase, amplitude and phase-amplitude coupling.

PubMed

Combrisson, Etienne; Perrone-Bertolotti, Marcela; Soto, Juan Lp; Alamian, Golnoush; Kahane, Philippe; Lachaux, Jean-Philippe; Guillot, Aymeric; Jerbi, Karim

2017-02-15

Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal activity across widely distributed brain areas. In particular, movement initiation and execution are mediated by patterns of synchronization and desynchronization that occur concurrently across distinct frequency bands and across multiple motor cortical areas. To date, motor-related local oscillatory modulations have been predominantly examined by quantifying increases or suppressions in spectral power. However, beyond signal power, spectral properties such as phase and phase-amplitude coupling (PAC) have also been shown to carry information with regards to the oscillatory dynamics underlying motor processes. Yet, the distinct functional roles of phase, amplitude and PAC across the planning and execution of goal-directed motor behavior remain largely elusive. Here, we address this question with unprecedented resolution thanks to multi-site intracerebral EEG recordings in human subjects while they performed a delayed motor task. To compare the roles of phase, amplitude and PAC, we monitored intracranial brain signals from 748 sites across six medically intractable epilepsy patients at movement execution, and during the delay period where motor intention is present but execution is withheld. In particular, we used a machine-learning framework to identify the key contributions of various neuronal responses. We found a high degree of overlap between brain network patterns observed during planning and those present during execution. Prominent amplitude increases in the delta (2-4Hz) and high gamma (60-200Hz) bands were observed during both planning and execution. In contrast, motor alpha (8-13Hz) and beta (13-30Hz) power were suppressed during execution, but enhanced during the delay period. Interestingly, single-trial classification revealed that low-frequency phase information, rather than spectral power change, was the most discriminant feature in dissociating action from intention. Additionally, despite providing weaker decoding, PAC features led to statistically significant classification of motor states, particularly in anterior cingulate cortex and premotor brain areas. These results advance our understanding of the distinct and partly overlapping involvement of phase, amplitude and the coupling between them, in the neuronal mechanisms underlying motor intentions and executions. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning a common dictionary for subject-transfer decoding with resting calibration.

PubMed

Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

2015-05-01

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.

Post-stroke balance rehabilitation under multi-level electrotherapy: a conceptual review

PubMed Central

Dutta, Anirban; Lahiri, Uttama; Das, Abhijit; Nitsche, Michael A.; Guiraud, David

2014-01-01

Stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to respond to intrinsic or extrinsic stimuli by reorganizing its structure, function, and connections is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. It has been shown that active cortical participation in a closed-loop brain machine interface (BMI) can induce neuroplasticity in cortical networks where the brain acts as a controller, e.g., during a visuomotor task. Here, the motor task can be assisted with neuromuscular electrical stimulation (NMES) where the BMI will act as a real-time decoder. However, the cortical control and induction of neuroplasticity in a closed-loop BMI is also dependent on the state of brain, e.g., visuospatial attention during visuomotor task performance. In fact, spatial neglect is a hidden disability that is a common complication of stroke and is associated with prolonged hospital stays, accidents, falls, safety problems, and chronic functional disability. This hypothesis and theory article presents a multi-level electrotherapy paradigm toward motor rehabilitation in virtual reality that postulates that while the brain acts as a controller in a closed-loop BMI to drive NMES, the state of brain can be can be altered toward improvement of visuomotor task performance with non-invasive brain stimulation (NIBS). This leads to a multi-level electrotherapy paradigm where a virtual reality-based adaptive response technology is proposed for post-stroke balance rehabilitation. In this article, we present a conceptual review of the related experimental findings. PMID:25565937

Preparing novice teachers to develop basic reading and spelling skills in children.

PubMed

Spear-Swerling, Louise; Brucker, Pamela Owen

2004-12-01

This study examined the word-structure knowledge of novice teachers and the progress of children tutored by a subgroup of the teachers. Teachers' word-structure knowledge was assessed using three tasks: graphophonemic segmentation, classification of pseudowords by syllable type, and classification of real words as phonetically regular or irregular. Tutored children were assessed on several measures of basic reading and spelling skills. Novice teachers who received word-structure instruction outperformed a comparison group of teachers in word-structure knowledge at post-test. Tutored children improved significantly from pre-test to post-test on all assessments. Teachers' post-test knowledge on the graphophonemic segmentation and irregular words tasks correlated significantly with tutored children's progress in decoding phonetically regular words; error analyses indicated links between teachers' patterns of word-structure knowledge and children's patterns of decoding progress. The study suggests that word-structure knowledge is important to effective teaching of word decoding and underscores the need to include this information in teacher preparation.

Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System.

PubMed

Zamora-Martinez, Francisco; Castro-Bleda, Maria Jose

2018-02-22

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery

NASA Astrophysics Data System (ADS)

Gomez-Rodriguez, M.; Peters, J.; Hill, J.; Schölkopf, B.; Gharabaghi, A.; Grosse-Wentrup, M.

2011-06-01

The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

Decoding ensemble activity from neurophysiological recordings in the temporal cortex.

PubMed

Kreiman, Gabriel

2011-01-01

We study subjects with pharmacologically intractable epilepsy who undergo semi-chronic implantation of electrodes for clinical purposes. We record physiological activity from tens to more than one hundred electrodes implanted in different parts of neocortex. These recordings provide higher spatial and temporal resolution than non-invasive measures of human brain activity. Here we discuss our efforts to develop hardware and algorithms to interact with the human brain by decoding ensemble activity in single trials. We focus our discussion on decoding visual information during a variety of visual object recognition tasks but the same technologies and algorithms can also be directly applied to other cognitive phenomena.

Children's understanding of facial expression of emotion: II. Drawing of emotion-faces.

PubMed

Missaghi-Lakshman, M; Whissell, C

1991-06-01

67 children from Grades 2, 4, and 7 drew faces representing the emotional expressions of fear, anger, surprise, disgust, happiness, and sadness. The children themselves and 29 adults later decoded the drawings in an emotion-recognition task. Children were the more accurate decoders, and their accuracy and the accuracy of adults increased significantly for judgments of 7th-grade drawings. The emotions happy and sad were most accurately decoded. There were no significant differences associated with sex. In their drawings, children utilized a symbol system that seems to be based on a highlighting or exaggeration of features of the innately governed facial expression of emotion.

Spatial attention and reading ability: ERP correlates of flanker and cue-size effects in good and poor adult phonological decoders.

PubMed

Matthews, Allison Jane; Martin, Frances Heritage

2015-12-01

To investigate facilitatory and inhibitory processes during selective attention among adults with good (n=17) and poor (n=14) phonological decoding skills, a go/nogo flanker task was completed while EEG was recorded. Participants responded to a middle target letter flanked by compatible or incompatible flankers. The target was surrounded by a small or large circular cue which was presented simultaneously or 500ms prior. Poor decoders showed a greater RT cost for incompatible stimuli preceded by large cues and less RT benefit for compatible stimuli. Poor decoders also showed reduced modulation of ERPs by cue-size at left hemisphere posterior sites (N1) and by flanker compatibility at right hemisphere posterior sites (N1) and frontal sites (N2), consistent with processing differences in fronto-parietal attention networks. These findings have potential implications for understanding the relationship between spatial attention and phonological decoding in dyslexia. Copyright © 2015 Elsevier Inc. All rights reserved.

Laterality of Temporoparietal Causal Connectivity during the Prestimulus Period Correlates with Phonological Decoding Task Performance in Dyslexic and Typical Readers

PubMed Central

Liederman, Jacqueline; McGraw Fisher, Janet; Wu, Meng-Hung

2012-01-01

We examined how effective connectivity into and out of the left and right temporoparietal areas (TPAs) to/from other key cortical areas affected phonological decoding in 7 dyslexic readers (DRs) and 10 typical readers (TRs) who were young adults. Granger causality was used to compute the effective connectivity of the preparatory network 500 ms prior to presentation of nonwords that required phonological decoding. Neuromagnetic activity was analyzed within the low, medium, and high beta and gamma subbands. A mixed-model analysis determined whether connectivity to or from the left and right TPAs differed across connectivity direction (in vs. out), brain areas (right and left inferior frontal and ventral occipital–temporal and the contralateral TPA), reading group (DR vs. TR), and/or task performance. Within the low beta subband, better performance was associated with increased influence of the left TPA on other brain areas across both reading groups and poorer performance was associated with increased influence of the right TPA on other brain areas for DRs only. DRs were also found to have an increase in high gamma connectivity between the left TPA and other brain areas. This study suggests that hierarchal network structure rather than connectivity per se is important in determining phonological decoding performance. PMID:21980019

The impact of basal ganglia lesions on sensorimotor synchronization, spontaneous motor tempo, and the detection of tempo changes.

PubMed

Schwartze, Michael; Keller, Peter E; Patel, Aniruddh D; Kotz, Sonja A

2011-01-20

The basal ganglia (BG) are part of extensive subcortico-cortical circuits that are involved in a variety of motor and non-motor cognitive functions. Accumulating evidence suggests that one specific function that engages the BG and associated cortico-striato-thalamo-cortical circuitry is temporal processing, i.e., the mechanisms that underlie the encoding, decoding and evaluation of temporal relations or temporal structure. In the current study we investigated the interplay of two processes that require precise representations of temporal structure, namely the perception of an auditory pacing signal and manual motor production by means of finger tapping in a sensorimotor synchronization task. Patients with focal lesions of the BG and healthy control participants were asked to align finger taps to tone sequences that either did or did not contain a tempo acceleration or tempo deceleration at a predefined position, and to continue tapping at the final tempo after the pacing sequence had ceased. Performance in this adaptive synchronization-continuation paradigm differed between the two groups. Selective damage to the BG affected the abilities to detect tempo changes and to perform attention-dependent error correction, particularly in response to tempo decelerations. An additional assessment of preferred spontaneous, i.e., unpaced but regular, production rates yielded more heterogeneous results in the patient group. Together these findings provide evidence for less efficient processing in the perception and the production of temporal structure in patients with focal BG lesions. The results also support the functional role of the BG system in attention-dependent temporal processing. Copyright © 2010 Elsevier B.V. All rights reserved.

Resting-State Brain Activity in Adult Males Who Stutter

PubMed Central

Zhu, Chaozhe; Wang, Liang; Yan, Qian; Lin, Chunlan; Yu, Chunshui

2012-01-01

Although developmental stuttering has been extensively studied with structural and task-based functional magnetic resonance imaging (fMRI), few studies have focused on resting-state brain activity in this disorder. We investigated resting-state brain activity of stuttering subjects by analyzing the amplitude of low-frequency fluctuation (ALFF), region of interest (ROI)-based functional connectivity (FC) and independent component analysis (ICA)-based FC. Forty-four adult males with developmental stuttering and 46 age-matched fluent male controls were scanned using resting-state fMRI. ALFF, ROI-based FCs and ICA-based FCs were compared between male stuttering subjects and fluent controls in a voxel-wise manner. Compared with fluent controls, stuttering subjects showed increased ALFF in left brain areas related to speech motor and auditory functions and bilateral prefrontal cortices related to cognitive control. However, stuttering subjects showed decreased ALFF in the left posterior language reception area and bilateral non-speech motor areas. ROI-based FC analysis revealed decreased FC between the posterior language area involved in the perception and decoding of sensory information and anterior brain area involved in the initiation of speech motor function, as well as increased FC within anterior or posterior speech- and language-associated areas and between the prefrontal areas and default-mode network (DMN) in stuttering subjects. ICA showed that stuttering subjects had decreased FC in the DMN and increased FC in the sensorimotor network. Our findings support the concept that stuttering subjects have deficits in multiple functional systems (motor, language, auditory and DMN) and in the connections between them. PMID:22276215

Heteromodal Cortical Areas Encode Sensory-Motor Features of Word Meaning.

PubMed

Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Seidenberg, Mark S; Binder, Jeffrey R

2016-09-21

The capacity to process information in conceptual form is a fundamental aspect of human cognition, yet little is known about how this type of information is encoded in the brain. Although the role of sensory and motor cortical areas has been a focus of recent debate, neuroimaging studies of concept representation consistently implicate a network of heteromodal areas that seem to support concept retrieval in general rather than knowledge related to any particular sensory-motor content. We used predictive machine learning on fMRI data to investigate the hypothesis that cortical areas in this "general semantic network" (GSN) encode multimodal information derived from basic sensory-motor processes, possibly functioning as convergence-divergence zones for distributed concept representation. An encoding model based on five conceptual attributes directly related to sensory-motor experience (sound, color, shape, manipulability, and visual motion) was used to predict brain activation patterns associated with individual lexical concepts in a semantic decision task. When the analysis was restricted to voxels in the GSN, the model was able to identify the activation patterns corresponding to individual concrete concepts significantly above chance. In contrast, a model based on five perceptual attributes of the word form performed at chance level. This pattern was reversed when the analysis was restricted to areas involved in the perceptual analysis of written word forms. These results indicate that heteromodal areas involved in semantic processing encode information about the relative importance of different sensory-motor attributes of concepts, possibly by storing particular combinations of sensory and motor features. The present study used a predictive encoding model of word semantics to decode conceptual information from neural activity in heteromodal cortical areas. The model is based on five sensory-motor attributes of word meaning (color, shape, sound, visual motion, and manipulability) and encodes the relative importance of each attribute to the meaning of a word. This is the first demonstration that heteromodal areas involved in semantic processing can discriminate between different concepts based on sensory-motor information alone. This finding indicates that the brain represents concepts as multimodal combinations of sensory and motor representations. Copyright © 2016 the authors 0270-6474/16/369763-07$15.00/0.

Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

PubMed Central

Tao, Zhongping; Zhang, Mu

2014-01-01

Abstract Functional imaging studies have indicated hemispheric asymmetry of activation in bilateral supplementary motor area (SMA) during unimanual motor tasks. However, the hemispherically special roles of bilateral SMAs on primary motor cortex (M1) in the effective connectivity networks (ECN) during lateralized tasks remain unclear. Aiming to study the differential contribution of bilateral SMAs during the motor execution and motor imagery tasks, and the hemispherically asymmetric patterns of ECN among regions involved, the present study used dynamic causal modeling to analyze the functional magnetic resonance imaging data of the unimanual motor execution/imagery tasks in 12 right-handed subjects. Our results demonstrated that distributions of network parameters underlying motor execution and motor imagery were significantly different. The variation was mainly induced by task condition modulations of intrinsic coupling. Particularly, regardless of the performing hand, the task input modulations of intrinsic coupling from the contralateral SMA to contralateral M1 were positive during motor execution, while varied to be negative during motor imagery. The results suggested that the inhibitive modulation suppressed the overt movement during motor imagery. In addition, the left SMA also helped accomplishing left hand tasks through task input modulation of left SMA→right SMA connection, implying that hemispheric recruitment occurred when performing nondominant hand tasks. The results specified differential and altered contributions of bilateral SMAs to the ECN during unimanual motor execution and motor imagery, and highlighted the contributions induced by the task input of motor execution/imagery. PMID:24606178

Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex.

PubMed

Ibayashi, Kenji; Kunii, Naoto; Matsuo, Takeshi; Ishishita, Yohei; Shimada, Seijiro; Kawai, Kensuke; Saito, Nobuhito

2018-01-01

Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs.

Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex

PubMed Central

Ibayashi, Kenji; Kunii, Naoto; Matsuo, Takeshi; Ishishita, Yohei; Shimada, Seijiro; Kawai, Kensuke; Saito, Nobuhito

2018-01-01

Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs. PMID:29674950

Restoring cortical control of functional movement in a human with quadriplegia.

PubMed

Bouton, Chad E; Shaikhouni, Ammar; Annetta, Nicholas V; Bockbrader, Marcia A; Friedenberg, David A; Nielson, Dylan M; Sharma, Gaurav; Sederberg, Per B; Glenn, Bradley C; Mysiw, W Jerry; Morgan, Austin G; Deogaonkar, Milind; Rezai, Ali R

2016-05-12

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

Task-relevant cognitive and motor functions are prioritized during prolonged speed-accuracy motor task performance.

PubMed

Solianik, Rima; Satas, Andrius; Mickeviciene, Dalia; Cekanauskaite, Agne; Valanciene, Dovile; Majauskiene, Daiva; Skurvydas, Albertas

2018-06-01

This study aimed to explore the effect of prolonged speed-accuracy motor task on the indicators of psychological, cognitive, psychomotor and motor function. Ten young men aged 21.1 ± 1.0 years performed a fast- and accurate-reaching movement task and a control task. Both tasks were performed for 2 h. Despite decreased motivation, and increased perception of effort as well as subjective feeling of fatigue, speed-accuracy motor task performance improved during the whole period of task execution. After the motor task, the increased working memory function and prefrontal cortex oxygenation at rest and during conflict detection, and the decreased efficiency of incorrect response inhibition and visuomotor tracking were observed. The speed-accuracy motor task increased the amplitude of motor-evoked potentials, while grip strength was not affected. These findings demonstrate that to sustain the performance of 2-h speed-accuracy task under conditions of self-reported fatigue, task-relevant functions are maintained or even improved, whereas less critical functions are impaired.

Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

PubMed Central

Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

2015-01-01

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID:25265627

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance.

PubMed

Astrand, Elaine

2018-06-01

Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, [Formula: see text]. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r  =  0.47, p  <  0.05). It is furthermore shown that this measure allows to predict task performance before action (r  =  0.49, p  <  0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain-machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance

NASA Astrophysics Data System (ADS)

Astrand, Elaine

2018-06-01

Objective. Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Approach. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, n\\in [1,2] . Main results. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r  =  0.47, p  <  0.05). It is furthermore shown that this measure allows to predict task performance before action (r  =  0.49, p  <  0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. Significance. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain–machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

PubMed Central

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

Mapping of MPEG-4 decoding on a flexible architecture platform

NASA Astrophysics Data System (ADS)

van der Tol, Erik B.; Jaspers, Egbert G.

2001-12-01

In the field of consumer electronics, the advent of new features such as Internet, games, video conferencing, and mobile communication has triggered the convergence of television and computers technologies. This requires a generic media-processing platform that enables simultaneous execution of very diverse tasks such as high-throughput stream-oriented data processing and highly data-dependent irregular processing with complex control flows. As a representative application, this paper presents the mapping of a Main Visual profile MPEG-4 for High-Definition (HD) video onto a flexible architecture platform. A stepwise approach is taken, going from the decoder application toward an implementation proposal. First, the application is decomposed into separate tasks with self-contained functionality, clear interfaces, and distinct characteristics. Next, a hardware-software partitioning is derived by analyzing the characteristics of each task such as the amount of inherent parallelism, the throughput requirements, the complexity of control processing, and the reuse potential over different applications and different systems. Finally, a feasible implementation is proposed that includes amongst others a very-long-instruction-word (VLIW) media processor, one or more RISC processors, and some dedicated processors. The mapping study of the MPEG-4 decoder proves the flexibility and extensibility of the media-processing platform. This platform enables an effective HW/SW co-design yielding a high performance density.

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

PubMed

Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

2011-01-01

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain.

PubMed

Chen, Quanjing; Garcea, Frank E; Mahon, Bradford Z

2016-04-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Interfacing to the brain’s motor decisions

PubMed Central

2017-01-01

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject’s motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components. PMID:28003406

Comparing Recalibration Strategies for Electroencephalography-Based Decoders of Movement Intention in Neurological Patients with Motor Disability.

PubMed

López-Larraz, Eduardo; Ibáñez, Jaime; Trincado-Alonso, Fernando; Monge-Pereira, Esther; Pons, José Luis; Montesano, Luis

2017-12-17

Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention.

Impaired affective prosody decoding in severe alcohol use disorder and Korsakoff syndrome.

PubMed

Brion, Mélanie; de Timary, Philippe; Mertens de Wilmars, Serge; Maurage, Pierre

2018-06-01

Recognizing others' emotions is a fundamental social skill, widely impaired in psychiatric populations. These emotional dysfunctions are involved in the development and maintenance of alcohol-related disorders, but their differential intensity across emotions and their modifications during disease evolution remain underexplored. Affective prosody decoding was assessed through a vocalization task using six emotions, among 17 patients with severe alcohol use disorder, 16 Korsakoff syndrome patients (diagnosed following DSM-V criteria) and 19 controls. Significant disturbances in emotional decoding, particularly for negative emotions, were found in alcohol-related disorders. These impairments, identical for both experimental groups, constitute a core deficit in excessive alcohol use. Copyright © 2018 Elsevier B.V. All rights reserved.

Potentiation of motor sub-networks for motor control but not working memory: Interaction of dACC and SMA revealed by resting-state directed functional connectivity

PubMed Central

Diwadkar, Vaibhav A.; Asemi, Avisa; Burgess, Ashley; Chowdury, Asadur; Bressler, Steven L.

2017-01-01

The dorsal Anterior Cingulate Cortex (dACC) and the Supplementary Motor Area (SMA) are known to interact during motor coordination behavior. We previously discovered that the directional influences underlying this interaction in a visuo-motor coordination task are asymmetric, with the dACC→SMA influence being significantly greater than that in the reverse direction. To assess the specificity of this effect, here we undertook an analysis of the interaction between dACC and SMA in two distinct contexts. In addition to the motor coordination task, we also assessed these effects during a (n-back) working memory task. We applied directed functional connectivity analysis to these two task paradigms, and also to the rest condition of each paradigm, in which rest blocks were interspersed with task blocks. We report here that the previously known asymmetric interaction between dACC and SMA, with dACC→SMA dominating, was significantly larger in the motor coordination task than the memory task. Moreover the asymmetry between dACC and SMA was reversed during the rest condition of the motor coordination task, but not of the working memory task. In sum, the dACC→SMA influence was significantly greater in the motor task than the memory task condition, and the SMA→dACC influence was significantly greater in the motor rest than the memory rest condition. We interpret these results as suggesting that the potentiation of motor sub-networks during the motor rest condition supports the motor control of SMA by dACC during the active motor task condition. PMID:28278267

Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation.

PubMed

Bajaj, Sahil; Butler, Andrew J; Drake, Daniel; Dhamala, Mukesh

2015-01-01

Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood. To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task. We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity. These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability.

Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm

PubMed Central

Dura-Bernal, Salvador; Chadderdon, George L; Neymotin, Samuel A; Francis, Joseph T; Lytton, William W

2015-01-01

Brain-machine interfaces can greatly improve the performance of prosthetics. Utilizing biomimetic neuronal modeling in brain machine interfaces (BMI) offers the possibility of providing naturalistic motor-control algorithms for control of a robotic limb. This will allow finer control of a robot, while also giving us new tools to better understand the brain’s use of electrical signals. However, the biomimetic approach presents challenges in integrating technologies across multiple hardware and software platforms, so that the different components can communicate in real-time. We present the first steps in an ongoing effort to integrate a biomimetic spiking neuronal model of motor learning with a robotic arm. The biomimetic model (BMM) was used to drive a simple kinematic two-joint virtual arm in a motor task requiring trial-and-error convergence on a single target. We utilized the output of this model in real time to drive mirroring motion of a Barrett Technology WAM robotic arm through a user datagram protocol (UDP) interface. The robotic arm sent back information on its joint positions, which was then used by a visualization tool on the remote computer to display a realistic 3D virtual model of the moving robotic arm in real time. This work paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, to be used as a platform for developing biomimetic learning algorithms for controlling real-time devices. PMID:26709323

Goal-Directed Modulation of Neural Memory Patterns: Implications for fMRI-Based Memory Detection.

PubMed

Uncapher, Melina R; Boyd-Meredith, J Tyler; Chow, Tiffany E; Rissman, Jesse; Wagner, Anthony D

2015-06-03

Remembering a past event elicits distributed neural patterns that can be distinguished from patterns elicited when encountering novel information. These differing patterns can be decoded with relatively high diagnostic accuracy for individual memories using multivoxel pattern analysis (MVPA) of fMRI data. Brain-based memory detection--if valid and reliable--would have clear utility beyond the domain of cognitive neuroscience, in the realm of law, marketing, and beyond. However, a significant boundary condition on memory decoding validity may be the deployment of "countermeasures": strategies used to mask memory signals. Here we tested the vulnerability of fMRI-based memory detection to countermeasures, using a paradigm that bears resemblance to eyewitness identification. Participants were scanned while performing two tasks on previously studied and novel faces: (1) a standard recognition memory task; and (2) a task wherein they attempted to conceal their true memory state. Univariate analyses revealed that participants were able to strategically modulate neural responses, averaged across trials, in regions implicated in memory retrieval, including the hippocampus and angular gyrus. Moreover, regions associated with goal-directed shifts of attention and thought substitution supported memory concealment, and those associated with memory generation supported novelty concealment. Critically, whereas MVPA enabled reliable classification of memory states when participants reported memory truthfully, the ability to decode memory on individual trials was compromised, even reversing, during attempts to conceal memory. Together, these findings demonstrate that strategic goal states can be deployed to mask memory-related neural patterns and foil memory decoding technology, placing a significant boundary condition on their real-world utility. Copyright © 2015 the authors 0270-6474/15/358531-15$15.00/0.

Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons

PubMed Central

Law, Andrew J.; Rivlis, Gil

2014-01-01

Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture—such as the similarity of preferred directions—had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill. PMID:24920030

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.

PubMed

Gao, Qiang; Dou, Lixiang; Belkacem, Abdelkader Nasreddine; Chen, Chao

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System

PubMed Central

Gao, Qiang

2017-01-01

A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, “teeth clenching” state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of “teeth clenching” condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word “HI” which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control. PMID:28660211

Effect of type of cognitive task and walking speed on cognitive-motor interference during dual-task walking.

PubMed

Patel, P; Lamar, M; Bhatt, T

2014-02-28

We aimed to determine the effect of distinctly different cognitive tasks and walking speed on cognitive-motor interference of dual-task walking. Fifteen healthy adults performed four cognitive tasks: visuomotor reaction time (VMRT) task, word list generation (WLG) task, serial subtraction (SS) task, and the Stroop (STR) task while sitting and during walking at preferred-speed (dual-task normal walking) and slow-speed (dual-task slow-speed walking). Gait speed was recorded to determine effect on walking. Motor and cognitive costs were measured. Dual-task walking had a significant effect on motor and cognitive parameters. At preferred-speed, the motor cost was lowest for the VMRT task and highest for the STR task. In contrast, the cognitive cost was highest for the VMRT task and lowest for the STR task. Dual-task slow walking resulted in increased motor cost and decreased cognitive cost only for the STR task. Results show that the motor and cognitive cost of dual-task walking depends heavily on the type and perceived complexity of the cognitive task being performed. Cognitive cost for the STR task was low irrespective of walking speed, suggesting that at preferred-speed individuals prioritize complex cognitive tasks requiring higher attentional and processing resources over walking. While performing VMRT task, individuals preferred to prioritize more complex walking task over VMRT task resulting in lesser motor cost and increased cognitive cost for VMRT task. Furthermore, slow walking can assist in diverting greater attention towards complex cognitive tasks, improving its performance while walking. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Decoding Task and Stimulus Representations in Face-responsive Cortex

PubMed Central

Kliemann, Dorit; Jacoby, Nir; Anzellotti, Stefano; Saxe, Rebecca R.

2017-01-01

Faces provide rich social information about others’ stable traits (e.g., age) and fleeting states of mind (e.g., emotional expression). While some of these facial aspects may be processed automatically, observers can also deliberately attend to some features while ignoring others. It remains unclear how internal goals (e.g., task context) influence the representational geometry of variable and stable facial aspects in face-responsive cortex. We investigated neural response patterns related to decoding i) the intention to attend to a facial aspect before its perception, ii) the attended aspect of a face and iii) stimulus properties. We measured neural responses while subjects watched videos of dynamic positive and negative expressions, and judged the age or the expression’s valence. Split-half multivoxel pattern analyses (MVPA) showed that (i) the intention to attend to a specific aspect of a face can be decoded from left fronto-lateral, but not face-responsive regions; (ii) during face perception, the attend aspect (age vs emotion) could be robustly decoded from almost all face-responsive regions; and (iii) a stimulus property (valence), was represented in right posterior superior temporal sulcus and medial prefrontal cortices. The effect of deliberately shifting the focus of attention on representations suggest a powerful influence of top-down signals on cortical representation of social information, varying across cortical regions, likely reflecting neural flexibility to optimally integrate internal goals and dynamic perceptual input. PMID:27978778

Global cortical activity predicts shape of hand during grasping

PubMed Central

Agashe, Harshavardhan A.; Paek, Andrew Y.; Zhang, Yuhang; Contreras-Vidal, José L.

2015-01-01

Recent studies show that the amplitude of cortical field potentials is modulated in the time domain by grasping kinematics. However, it is unknown if these low frequency modulations persist and contain enough information to decode grasp kinematics in macro-scale activity measured at the scalp via electroencephalography (EEG). Further, it is unclear as to whether joint angle velocities or movement synergies are the optimal kinematics spaces to decode. In this offline decoding study, we infer from human EEG, hand joint angular velocities as well as synergistic trajectories as subjects perform natural reach-to-grasp movements. Decoding accuracy, measured as the correlation coefficient (r) between the predicted and actual movement kinematics, was r = 0.49 ± 0.02 across 15 hand joints. Across the first three kinematic synergies, decoding accuracies were r = 0.59 ± 0.04, 0.47 ± 0.06, and 0.32 ± 0.05. The spatial-temporal pattern of EEG channel recruitment showed early involvement of contralateral frontal-central scalp areas followed by later activation of central electrodes over primary sensorimotor cortical areas. Information content in EEG about the grasp type peaked at 250 ms after movement onset. The high decoding accuracies in this study are significant not only as evidence for time-domain modulation in macro-scale brain activity, but for the field of brain-machine interfaces as well. Our decoding strategy, which harnesses the neural “symphony” as opposed to local members of the neural ensemble (as in intracranial approaches), may provide a means of extracting information about motor intent for grasping without the need for penetrating electrodes and suggests that it may be soon possible to develop non-invasive neural interfaces for the control of prosthetic limbs. PMID:25914616

Effects of Single Compared to Dual Task Practice on Learning a Dynamic Balance Task in Young Adults

PubMed Central

Kiss, Rainer; Brueckner, Dennis; Muehlbauer, Thomas

2018-01-01

Background: In everyday life, people engage in situations involving the concurrent processing of motor (balance) and cognitive tasks (i.e., “dual task situations”) that result in performance declines in at least one of the given tasks. The concurrent practice of both the motor and cognitive task may counteract these performance decrements. The purpose of this study was to examine the effects of single task (ST) compared to dual task (DT) practice on learning a dynamic balance task. Methods: Forty-eight young adults were randomly assigned to either a ST (i.e., motor or cognitive task training only) or a DT (i.e., motor-cognitive training) practice condition. The motor task required participants to stand on a platform and keeping the platform as close to horizontal as possible. In the cognitive task, participants were asked to recite serial subtractions of three. For 2 days, participants of the ST groups practiced the motor or cognitive task only, while the participants of the DT group concurrently performed both. Root-mean-square error (RMSE) for the motor and total number of correct calculations for the cognitive task were computed. Results: During practice, all groups improved their respective balance and/or cognitive task performance. With regard to the assessment of learning on day 3, we found significantly smaller RMSE values for the ST motor (d = 1.31) and the DT motor-cognitive (d = 0.76) practice group compared to the ST cognitive practice group but not between the ST motor and the DT motor-cognitive practice group under DT test condition. Further, we detected significantly larger total numbers of correct calculations under DT test condition for the ST cognitive (d = 2.19) and the DT motor-cognitive (d = 1.55) practice group compared to the ST motor practice group but not between the ST cognitive and the DT motor-cognitive practice group. Conclusion: We conclude that ST practice resulted in an effective modulation of the trained domain (i.e., motor or cognitive) while only DT practice resulted in an effective modulation of both domains (i.e., motor and cognitive). Thus, particularly DT practice frees up central resources that were used for an effective modulation of motor and cognitive processing mechanisms. PMID:29593614

Long-term decoding of movement force and direction with a wireless myoelectric implant.

PubMed

Morel, Pierre; Ferrea, Enrico; Taghizadeh-Sarshouri, Bahareh; Audí, Josep Marcel Cardona; Ruff, Roman; Hoffmann, Klaus-Peter; Lewis, Sören; Russold, Michael; Dietl, Hans; Abu-Saleh, Lait; Schroeder, Dietmar; Krautschneider, Wolfgang; Meiners, Thomas; Gail, Alexander

2016-02-01

The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).

Long-term decoding of movement force and direction with a wireless myoelectric implant

NASA Astrophysics Data System (ADS)

Morel, Pierre; Ferrea, Enrico; Taghizadeh-Sarshouri, Bahareh; Marcel Cardona Audí, Josep; Ruff, Roman; Hoffmann, Klaus-Peter; Lewis, Sören; Russold, Michael; Dietl, Hans; Abu-Saleh, Lait; Schroeder, Dietmar; Krautschneider, Wolfgang; Meiners, Thomas; Gail, Alexander

2016-02-01

Objective. The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. Approach. Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. Main results. In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. Significance. These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).

Cognitive and motor dual task gait training improve dual task gait performance after stroke - A randomized controlled pilot trial.

PubMed

Liu, Yan-Ci; Yang, Yea-Ru; Tsai, Yun-An; Wang, Ray-Yau

2017-06-22

This study investigated effects of cognitive and motor dual task gait training on dual task gait performance in stroke. Participants (n = 28) were randomly assigned to cognitive dual task gait training (CDTT), motor dual task gait training (MDTT), or conventional physical therapy (CPT) group. Participants in CDTT or MDTT group practiced the cognitive or motor tasks respectively during walking. Participants in CPT group received strengthening, balance, and gait training. The intervention was 30 min/session, 3 sessions/week for 4 weeks. Three test conditions to evaluate the training effects were single walking, walking while performing cognitive task (serial subtraction), and walking while performing motor task (tray-carrying). Parameters included gait speed, dual task cost of gait speed (DTC-speed), cadence, stride time, and stride length. After CDTT, cognitive-motor dual task gait performance (stride length and DTC-speed) was improved (p = 0.021; p = 0.015). After MDTT, motor dual task gait performance (gait speed, stride length, and DTC-speed) was improved (p = 0.008; p = 0.008; p = 0.008 respectively). It seems that CDTT improved cognitive dual task gait performance and MDTT improved motor dual task gait performance although such improvements did not reach significant group difference. Therefore, different types of dual task gait training can be adopted to enhance different dual task gait performance in stroke.

Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation

PubMed Central

Bajaj, Sahil; Butler, Andrew J.; Drake, Daniel; Dhamala, Mukesh

2015-01-01

Brain areas within the motor system interact directly or indirectly during motor-imagery and motor-execution tasks. These interactions and their functionality can change following stroke and recovery. How brain network interactions reorganize and recover their functionality during recovery and treatment following stroke are not well understood. To contribute to answering these questions, we recorded blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signals from 10 stroke survivors and evaluated dynamical causal modeling (DCM)-based effective connectivity among three motor areas: primary motor cortex (M1), pre-motor cortex (PMC) and supplementary motor area (SMA), during motor-imagery and motor-execution tasks. We compared the connectivity between affected and unaffected hemispheres before and after mental practice and combined mental practice and physical therapy as treatments. The treatment (intervention) period varied in length between 14 to 51 days but all patients received the same dose of 60 h of treatment. Using Bayesian model selection (BMS) approach in the DCM approach, we found that, after intervention, the same network dominated during motor-imagery and motor-execution tasks but modulatory parameters suggested a suppressive influence of SM A on M1 during the motor-imagery task whereas the influence of SM A on M1 was unrestricted during the motor-execution task. We found that the intervention caused a reorganization of the network during both tasks for unaffected as well as for the affected hemisphere. Using Bayesian model averaging (BMA) approach, we found that the intervention improved the regional connectivity among the motor areas during both the tasks. The connectivity between PMC and M1 was stronger in motor-imagery tasks whereas the connectivity from PMC to M1, SM A to M1 dominated in motor-execution tasks. There was significant behavioral improvement (p = 0.001) in sensation and motor movements because of the intervention as reflected by behavioral Fugl-Meyer (FMA) measures, which were significantly correlated (p = 0.05) with a subset of connectivity. These findings suggest that PMC and M1 play a crucial role during motor-imagery as well as during motor-execution task. In addition, M1 causes more exchange of causal information among motor areas during a motor-execution task than during a motor-imagery task due to its interaction with SM A. This study expands our understanding of motor network involved during two different tasks, which are commonly used during rehabilitation following stroke. A clear understanding of the effective connectivity networks leads to a better treatment in helping stroke survivors regain motor ability. PMID:26236627

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.

PubMed

Micera, Silvestro; Rossini, Paolo M; Rigosa, Jacopo; Citi, Luca; Carpaneto, Jacopo; Raspopovic, Stanisa; Tombini, Mario; Cipriani, Christian; Assenza, Giovanni; Carrozza, Maria C; Hoffmann, Klaus-Peter; Yoshida, Ken; Navarro, Xavier; Dario, Paolo

2011-09-05

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

Somatosensory responses in a human motor cortex

PubMed Central

Donoghue, John P.; Hochberg, Leigh R.

2013-01-01

Somatic sensory signals provide a major source of feedback to motor cortex. Changes in somatosensory systems after stroke or injury could profoundly influence brain computer interfaces (BCI) being developed to create new output signals from motor cortex activity patterns. We had the unique opportunity to study the responses of hand/arm area neurons in primary motor cortex to passive joint manipulation in a person with a long-standing brain stem stroke but intact sensory pathways. Neurons responded to passive manipulation of the contralateral shoulder, elbow, or wrist as predicted from prior studies of intact primates. Thus fundamental properties and organization were preserved despite arm/hand paralysis and damage to cortical outputs. The same neurons were engaged by attempted arm actions. These results indicate that intact sensory pathways retain the potential to influence primary motor cortex firing rates years after cortical outputs are interrupted and may contribute to online decoding of motor intentions for BCI applications. PMID:23343902

Over-focused? The relation between patients' inclination for conscious control and single- and dual-task motor performance after stroke.

PubMed

Denneman, R P M; Kal, E C; Houdijk, H; Kamp, J van der

2018-05-01

Many stroke patients are inclined to consciously control their movements. This is thought to negatively affect patients' motor performance, as it disrupts movement automaticity. However, it has also been argued that conscious control may sometimes benefit motor performance, depending on the task or patientś motor or cognitive capacity. To assess whether stroke patients' inclination for conscious control is associated with motor performance, and explore whether the putative association differs as a function of task (single- vs dual) or patientś motor and cognitive capacity. Univariate and multivariate linear regression analysis were used to assess associations between patients' disposition to conscious control (i.e., Conscious Motor Processing subscale of Movement-Specific Reinvestment Scale; MSRS-CMP) and single-task (Timed-up-and-go test; TuG) and motor dual-task costs (TuG while tone counting; motor DTC%). We determined whether these associations were influenced by patients' walking speed (i.e., 10-m-walk test) and cognitive capacity (i.e., working memory, attention, executive function). Seventy-eight clinical stroke patients (<6 months post-stroke) participated. Patients' conscious control inclination was not associated with single-task TuG performance. However, patients with a strong inclination for conscious control showed higher motor DTC%. These associations were irrespective of patients' motor and cognitive abilities. Patients' disposition for conscious control was not associated with single task motor performance, but was associated with higher motor dual task costs, regardless of patients' motor or cognitive abilities. Therapists should be aware that patients' conscious control inclination can influence their dual-task performance while moving. Longitudinal studies are required to test whether reducing patients' disposition for conscious control would improve dual-tasking post-stroke. Copyright © 2018 Elsevier B.V. All rights reserved.

Decoding Face Information in Time, Frequency and Space from Direct Intracranial Recordings of the Human Brain

PubMed Central

Oya, Hiroyuki; Howard, Matthew A.; Adolphs, Ralph

2008-01-01

Faces are processed by a neural system with distributed anatomical components, but the roles of these components remain unclear. A dominant theory of face perception postulates independent representations of invariant aspects of faces (e.g., identity) in ventral temporal cortex including the fusiform gyrus, and changeable aspects of faces (e.g., emotion) in lateral temporal cortex including the superior temporal sulcus. Here we recorded neuronal activity directly from the cortical surface in 9 neurosurgical subjects undergoing epilepsy monitoring while they viewed static and dynamic facial expressions. Applying novel decoding analyses to the power spectrogram of electrocorticograms (ECoG) from over 100 contacts in ventral and lateral temporal cortex, we found better representation of both invariant and changeable aspects of faces in ventral than lateral temporal cortex. Critical information for discriminating faces from geometric patterns was carried by power modulations between 50 to 150 Hz. For both static and dynamic face stimuli, we obtained a higher decoding performance in ventral than lateral temporal cortex. For discriminating fearful from happy expressions, critical information was carried by power modulation between 60–150 Hz and below 30 Hz, and again better decoded in ventral than lateral temporal cortex. Task-relevant attention improved decoding accuracy more than10% across a wide frequency range in ventral but not at all in lateral temporal cortex. Spatial searchlight decoding showed that decoding performance was highest around the middle fusiform gyrus. Finally, we found that the right hemisphere, in general, showed superior decoding to the left hemisphere. Taken together, our results challenge the dominant model for independent face representation of invariant and changeable aspects: information about both face attributes was better decoded from a single region in the middle fusiform gyrus. PMID:19065268

Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.

PubMed

Khan, Muhammad Jawad; Hong, Keum-Shik

2017-01-01

In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.

Distributed Coding/Decoding Complexity in Video Sensor Networks

PubMed Central

Cordeiro, Paulo J.; Assunção, Pedro

2012-01-01

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality. PMID:22736972

Distributed coding/decoding complexity in video sensor networks.

PubMed

Cordeiro, Paulo J; Assunção, Pedro

2012-01-01

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality.

Linkage between Free Exploratory Movements and Subjective Tactile Ratings.

PubMed

Yokosaka, Takumi; Kuroki, Scinob; Watanabe, Junji; Nishida, Shinya

2017-01-01

We actively move our hands and eyes when exploring the external world and gaining information about object's attributes. Previous studies showing that how we touch might be related to how we felt led us to consider whether we could decode observers' subjective tactile experiences only by analyzing their exploratory movements without explicitly asking how they perceived. However, in those studies, explicit judgment tasks were performed about specific tactile attributes that were prearranged by experimenters. Here, we systematically investigated whether exploratory movements can explain tactile ratings even when participants do not need to judge any tactile attributes. While measuring both hand and eye movements, we asked participants to touch materials freely without judging any specific tactile attributes (free-touch task) or to evaluate one of four tactile attributes (roughness, hardness, slipperiness, and temperature). We found that tactile ratings in the judgment tasks correlated with exploratory movements even in the free-touch task and that eye movements as well as hand movements correlated with tactile ratings. These results might open up the possibility of decoding tactile experiences by exploratory movements.

Automatic detection and decoding of honey bee waggle dances.

PubMed

Wario, Fernando; Wild, Benjamin; Rojas, Raúl; Landgraf, Tim

2017-01-01

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07%. The decoded waggle orientation has an average error of -2.92° (± 7.37°), well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.

Decoding Articulatory Features from fMRI Responses in Dorsal Speech Regions.

PubMed

Correia, Joao M; Jansma, Bernadette M B; Bonte, Milene

2015-11-11

The brain's circuitry for perceiving and producing speech may show a notable level of overlap that is crucial for normal development and behavior. The extent to which sensorimotor integration plays a role in speech perception remains highly controversial, however. Methodological constraints related to experimental designs and analysis methods have so far prevented the disentanglement of neural responses to acoustic versus articulatory speech features. Using a passive listening paradigm and multivariate decoding of single-trial fMRI responses to spoken syllables, we investigated brain-based generalization of articulatory features (place and manner of articulation, and voicing) beyond their acoustic (surface) form in adult human listeners. For example, we trained a classifier to discriminate place of articulation within stop syllables (e.g., /pa/ vs /ta/) and tested whether this training generalizes to fricatives (e.g., /fa/ vs /sa/). This novel approach revealed generalization of place and manner of articulation at multiple cortical levels within the dorsal auditory pathway, including auditory, sensorimotor, motor, and somatosensory regions, suggesting the representation of sensorimotor information. Additionally, generalization of voicing included the right anterior superior temporal sulcus associated with the perception of human voices as well as somatosensory regions bilaterally. Our findings highlight the close connection between brain systems for speech perception and production, and in particular, indicate the availability of articulatory codes during passive speech perception. Sensorimotor integration is central to verbal communication and provides a link between auditory signals of speech perception and motor programs of speech production. It remains highly controversial, however, to what extent the brain's speech perception system actively uses articulatory (motor), in addition to acoustic/phonetic, representations. In this study, we examine the role of articulatory representations during passive listening using carefully controlled stimuli (spoken syllables) in combination with multivariate fMRI decoding. Our approach enabled us to disentangle brain responses to acoustic and articulatory speech properties. In particular, it revealed articulatory-specific brain responses of speech at multiple cortical levels, including auditory, sensorimotor, and motor regions, suggesting the representation of sensorimotor information during passive speech perception. Copyright © 2015 the authors 0270-6474/15/3515015-11$15.00/0.

Brain oscillatory signatures of motor tasks

PubMed Central

Birbaumer, Niels

2015-01-01

Noninvasive brain-computer-interfaces (BCI) coupled with prosthetic devices were recently introduced in the rehabilitation of chronic stroke and other disorders of the motor system. These BCI systems and motor rehabilitation in general involve several motor tasks for training. This study investigates the neurophysiological bases of an EEG-oscillation-driven BCI combined with a neuroprosthetic device to define the specific oscillatory signature of the BCI task. Controlling movements of a hand robotic orthosis with motor imagery of the same movement generates sensorimotor rhythm oscillation changes and involves three elements of tasks also used in stroke motor rehabilitation: passive and active movement, motor imagery, and motor intention. We recorded EEG while nine healthy participants performed five different motor tasks consisting of closing and opening of the hand as follows: 1) motor imagery without any external feedback and without overt hand movement, 2) motor imagery that moves the orthosis proportional to the produced brain oscillation change with online proprioceptive and visual feedback of the hand moving through a neuroprosthetic device (BCI condition), 3) passive and 4) active movement of the hand with feedback (seeing and feeling the hand moving), and 5) rest. During the BCI condition, participants received contingent online feedback of the decrease of power of the sensorimotor rhythm, which induced orthosis movement and therefore proprioceptive and visual information from the moving hand. We analyzed brain activity during the five conditions using time-frequency domain bootstrap-based statistical comparisons and Morlet transforms. Activity during rest was used as a reference. Significant contralateral and ipsilateral event-related desynchronization of sensorimotor rhythm was present during all motor tasks, largest in contralateral-postcentral, medio-central, and ipsilateral-precentral areas identifying the ipsilateral precentral cortex as an integral part of motor regulation. Changes in task-specific frequency power compared with rest were similar between motor tasks, and only significant differences in the time course and some narrow specific frequency bands were observed between motor tasks. We identified EEG features representing active and passive proprioception (with and without muscle contraction) and active intention and passive involvement (with and without voluntary effort) differentiating brain oscillations during motor tasks that could substantially support the design of novel motor BCI-based rehabilitation therapies. The BCI task induced significantly different brain activity compared with the other motor tasks, indicating neural processes unique to the use of body actuators control in a BCI context. PMID:25810484

Analysis of Time-Dependent Brain Network on Active and MI Tasks for Chronic Stroke Patients

PubMed Central

Chang, Won Hyuk; Kim, Yun-Hee; Lee, Seong-Whan; Kwon, Gyu Hyun

2015-01-01

Several researchers have analyzed brain activities by investigating brain networks. However, there is a lack of the research on the temporal characteristics of the brain network during a stroke by EEG and the comparative studies between motor execution and imagery, which became known to have similar motor functions and pathways. In this study, we proposed the possibility of temporal characteristics on the brain networks of a stroke. We analyzed the temporal properties of the brain networks for nine chronic stroke patients by the active and motor imagery tasks by EEG. High beta band has a specific role in the brain network during motor tasks. In the high beta band, for the active task, there were significant characteristics of centrality and small-worldness on bilateral primary motor cortices at the initial motor execution. The degree centrality significantly increased on the contralateral primary motor cortex, and local efficiency increased on the ipsilateral primary motor cortex. These results indicate that the ipsilateral primary motor cortex constructed a powerful subnetwork by influencing the linked channels as compensatory effect, although the contralateral primary motor cortex organized an inefficient network by using the connected channels due to lesions. For the MI task, degree centrality and local efficiency significantly decreased on the somatosensory area at the initial motor imagery. Then, there were significant correlations between the properties of brain networks and motor function on the contralateral primary motor cortex and somatosensory area for each motor execution/imagery task. Our results represented that the active and MI tasks have different mechanisms of motor acts. Based on these results, we indicated the possibility of customized rehabilitation according to different motor tasks. We expect these results to help in the construction of the customized rehabilitation system depending on motor tasks by understanding temporal functional characteristics on brain network for a stroke. PMID:26656269

Cognitive-motor interference during fine and gross motor tasks in children with Developmental Coordination Disorder (DCD).

PubMed

Schott, Nadja; El-Rajab, Inaam; Klotzbier, Thomas

2016-10-01

While typically developing children produce relatively automatized postural control processes, children with DCD seem to exhibit an automatization deficit. Dual tasks with various cognitive loads seem to be an effective way to assess the automatic deficit hypothesis. The aims of the study were: (1) to examine the effect of a concurrent cognitive task on fine and gross motor tasks in children with DCD, and (2) to determine whether the effect varied with different difficulty levels of the concurrent task. We examined dual-task performance (Trail-Making-Test, Trail-Walking-Test) in 20 children with DCD and 39 typically developing children. Based on the idea of the Trail-Making-Test, participants walked along a fixed pathway, following a prescribed path, delineated by target markers of (1) increasing sequential numbers, and (2) increasing sequential numbers and letters. The motor and cognitive dual-task effects (DTE) were calculated for each task. Regardless of the cognitive task, children with DCD performed equally well in fine and gross motor tasks, and were slower in the dual task conditions than under single task-conditions, compared with children without DCD. Increased cognitive task complexity resulted in slow trail walking as well as slower trail tracing. The motor interference for the gross motor tasks was least for the simplest conditions and greatest for the complex conditions and was more pronounced in children with DCD. Cognitive interference was low irrespective of the motor task. Children with DCD show a different approach to allocation of cognitive resources, and have difficulties making motor skills automatic. The latter notion is consistent with impaired cerebellar function and the "automatization deficit hypothesis", suggesting that any deficit in the automatization process will appear if conscious monitoring of the motor skill is made more difficult by integrating another task requiring attentional resources. Copyright © 2016 Elsevier Ltd. All rights reserved.

For love or money? What motivates people to know the minds of others?

PubMed

Harkness, Kate L; Jacobson, Jill A; Sinclair, Brooke; Chan, Emilie; Sabbagh, Mark A

2012-01-01

Mood affects social cognition and "theory of mind", such that people in a persistent negative mood (i.e., dysphoria) have enhanced abilities at making subtle judgements about others' mental states. Theorists have argued that this hypersensitivity to subtle social cues may have adaptive significance in terms of solving interpersonal problems and/or minimising social risk. We tested whether increasing the social salience of a theory of mind task would preferentially increase dyspshoric individuals' performance on the task. Forty-four dysphoric and 51 non-dysphoric undergraduate women participated in a theory of mind decoding task following one of three motivational manipulations: (i) social motivation (ii) monetary motivation, or (iii) no motivation. Social motivation was associated with the greatest accuracy of mental state decoding for the dysphoric group, whereas the non-dysphoric group showed the highest accuracy in the monetary motivation condition. These results suggest that dysphoric individuals may be especially, and preferentially, motivated to understand the mental states of others.

Language Model Combination and Adaptation Using Weighted Finite State Transducers

NASA Technical Reports Server (NTRS)

Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.

2010-01-01

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences

PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields.

PubMed

Agarwal, Rahul; Thakor, Nitish V; Sarma, Sridevi V; Massaquoi, Steve G

2015-06-24

The premotor cortex (PM) is known to be a site of visuo-somatosensory integration for the production of movement. We sought to better understand the ventral PM (PMv) by modeling its signal encoding in greater detail. Neuronal firing data was obtained from 110 PMv neurons in two male rhesus macaques executing four reach-grasp-manipulate tasks. We found that in the large majority of neurons (∼90%) the firing patterns across the four tasks could be explained by assuming that a high-dimensional position/configuration trajectory-like signal evolving ∼250 ms before movement was encoded within a multidimensional Gaussian field (MGF). Our findings are consistent with the possibility that PMv neurons process a visually specified reference command for the intended arm/hand position trajectory with respect to a proprioceptively or visually sensed initial configuration. The estimated MGF were (hyper) disc-like, such that each neuron's firing modulated strongly only with commands that evolved along a single direction within position/configuration space. Thus, many neurons appeared to be tuned to slices of this input signal space that as a collection appeared to well cover the space. The MGF encoding models appear to be consistent with the arm-referent, bell-shaped, visual target tuning curves and target selectivity patterns observed in PMV visual-motor neurons. These findings suggest that PMv may implement a lookup table-like mechanism that helps translate intended movement trajectory into time-varying patterns of activation in motor cortex and spinal cord. MGFs provide an improved nonlinear framework for potentially decoding visually specified, intended multijoint arm/hand trajectories well in advance of movement. Copyright © 2015 the authors 0270-6474/15/359508-18$15.00/0.

Transfer of training between distinct motor tasks after stroke: implications for task-specific approaches to upper-extremity neurorehabilitation.

PubMed

Schaefer, Sydney Y; Patterson, Chavelle B; Lang, Catherine E

2013-09-01

Although task-specific training is emerging as a viable approach for recovering motor function after stroke, there is little evidence for whether the effects of such training transfer to other functional motor tasks not directly practiced in therapy. The purpose of the current study was to test whether training on one motor task in individuals with chronic hemiparesis poststroke would transfer to untrained tasks that were either spatiotemporally similar or different. In all, 11 participants with chronic mild to moderate hemiparesis following stroke completed 5 days of supervised massed practice of a feeding task with their affected side. Performance on the feeding task, along with 2 other untrained functional upper-extremity motor tasks (sorting, dressing) was assessed before and after training. Performance of all 3 tasks improved significantly after training exclusively on 1 motor task. The amount of improvement in the untrained tasks was comparable and was not dependent on the degree of similarity to the trained task. Because the number and type of tasks that can be practiced are often limited within standard stroke rehabilitation, results from this study will be useful for designing task-specific training plans to maximize therapy benefits.

Walking in School-Aged Children in a Dual-Task Paradigm Is Related to Age But Not to Cognition, Motor Behavior, Injuries, or Psychosocial Functioning

PubMed Central

Hagmann-von Arx, Priska; Manicolo, Olivia; Lemola, Sakari; Grob, Alexander

2016-01-01

Age-dependent gait characteristics and associations with cognition, motor behavior, injuries, and psychosocial functioning were investigated in 138 typically developing children aged 6.7–13.2 years (M = 10.0 years). Gait velocity, normalized velocity, and variability were measured using the walkway system GAITRite without an additional task (single task) and while performing a motor or cognitive task (dual task). Assessment of children’s cognition included tests for intelligence and executive functions; parents reported on their child’s motor behavior, injuries, and psychosocial functioning. Gait variability (an index of gait regularity) decreased with increasing age in both single- and dual-task walking. Dual-task gait decrements were stronger when children walked in the motor compared to the cognitive dual-task condition and decreased with increasing age in both dual-task conditions. Gait alterations from single- to dual-task conditions were not related to children’s cognition, motor behavior, injuries, or psychosocial functioning. PMID:27014158

Performance in complex motor tasks deteriorates in hyperthermic humans.

PubMed

Piil, Jacob F; Lundbye-Jensen, Jesper; Trangmar, Steven J; Nybo, Lars

2017-01-01

Heat stress, leading to elevations in whole-body temperature, has a marked impact on both physical performance and cognition in ecological settings. Lab experiments confirm this for physically demanding activities, whereas observations are inconsistent for tasks involving cognitive processing of information or decision-making prior to responding. We hypothesized that divergences could relate to task complexity and developed a protocol consisting of 1) simple motor task [TARGET_pinch], 2) complex motor task [Visuo-motor tracking], 3) simple math task [MATH_type], 4) combined motor-math task [MATH_pinch]. Furthermore, visuo-motor tracking performance was assessed both in a separate- and a multipart protocol (complex motor tasks alternating with the three other tasks). Following familiarization, each of the 10 male subjects completed separate and multipart protocols in randomized order in the heat (40°C) or control condition (20°C) with testing at baseline (seated rest) and similar seated position, following exercise-induced hyperthermia (core temperature ∼ 39.5°C in the heat and 38.2°C in control condition). All task scores were unaffected by control exercise or passive heat exposure, but visuo-motor tracking performance was reduced by 10.7 ± 6.5% following exercise-induced hyperthermia when integrated in the multipart protocol and 4.4 ± 5.7% when tested separately (both P < 0.05 ). TARGET_pinch precision declined by 2.6 ± 1.3% ( P < 0.05 ), while no significant changes were observed for the math tasks. These results indicate that heat per se has little impact on simple motor or cognitive test performance, but complex motor performance is impaired by hyperthermia and especially so when multiple tasks are combined.

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

NASA Astrophysics Data System (ADS)

Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

2018-04-01

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

Phonological and Cognitive Correlates of Word-Reading Acquisition under Two Different Instructional Approaches in Greek.

ERIC Educational Resources Information Center

Papadopoulos, Timothy C.

2001-01-01

Examines the relationship between phonological and cognitive tasks with beginning reading acquisition. Uses two teaching techniques for tasks given first-grade students in Cyprus (n=50) and Greece (n=50). Reports differences were revealed in word-decoding accuracy, Greek students showed a higher linguistic ability, and successive processing and…

Discussion on LDPC Codes and Uplink Coding

NASA Technical Reports Server (NTRS)

Andrews, Ken; Divsalar, Dariush; Dolinar, Sam; Moision, Bruce; Hamkins, Jon; Pollara, Fabrizio

2007-01-01

This slide presentation reviews the progress that the workgroup on Low-Density Parity-Check (LDPC) for space link coding. The workgroup is tasked with developing and recommending new error correcting codes for near-Earth, Lunar, and deep space applications. Included in the presentation is a summary of the technical progress of the workgroup. Charts that show the LDPC decoder sensitivity to symbol scaling errors are reviewed, as well as a chart showing the performance of several frame synchronizer algorithms compared to that of some good codes and LDPC decoder tests at ESTL. Also reviewed is a study on Coding, Modulation, and Link Protocol (CMLP), and the recommended codes. A design for the Pseudo-Randomizer with LDPC Decoder and CRC is also reviewed. A chart that summarizes the three proposed coding systems is also presented.

Reaction Decoder Tool (RDT): extracting features from chemical reactions.

PubMed

Rahman, Syed Asad; Torrance, Gilliean; Baldacci, Lorenzo; Martínez Cuesta, Sergio; Fenninger, Franz; Gopal, Nimish; Choudhary, Saket; May, John W; Holliday, Gemma L; Steinbeck, Christoph; Thornton, Janet M

2016-07-01

Extracting chemical features like Atom-Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features. This software is implemented in Java, supported on Windows, Linux and Mac OSX, and freely available at https://github.com/asad/ReactionDecoder : asad@ebi.ac.uk or s9asad@gmail.com. © The Author 2016. Published by Oxford University Press.

Linear methods for reducing EMG contamination in peripheral nerve motor decodes.

PubMed

Kagan, Zachary B; Wendelken, Suzanne; Page, David M; Davis, Tyler; Hutchinson, Douglas T; Clark, Gregory A; Warren, David J

2016-08-01

Signals recorded from the peripheral nervous system (PNS) with high channel count penetrating microelectrode arrays, such as the Utah Slanted Electrode Array (USEA), often have electromyographic (EMG) signals contaminating the neural signal. This common-mode signal source may prevent single neural units from successfully being detected, thus hindering motor decode algorithms. Reducing this EMG contamination may lead to more accurate motor decode performance. A virtual reference (VR), created by a weighted linear combination of signals from a subset of all available channels, can be used to reduce this EMG contamination. Four methods of determining individual channel weights and six different methods of selecting subsets of channels were investigated (24 different VR types in total). The methods of determining individual channel weights were equal weighting, regression-based weighting, and two different proximity-based weightings. The subsets of channels were selected by a radius-based criteria, such that a channel was included if it was within a particular radius of inclusion from the target channel. These six radii of inclusion were 1.5, 2.9, 3.2, 5, 8.4, and 12.8 electrode-distances; the 12.8 electrode radius includes all USEA electrodes. We found that application of a VR improves the detectability of neural events via increasing the SNR, but we found no statistically meaningful difference amongst the VR types we examined. The computational complexity of implementation varies with respect to the method of determining channel weights and the number of channels in a subset, but does not correlate with VR performance. Hence, we examined the computational costs of calculating and applying the VR and based on these criteria, we recommend an equal weighting method of assigning weights with a 3.2 electrode-distance radius of inclusion. Further, we found empirically that application of the recommended VR will require less than 1 ms for 33.3 ms of data from one USEA.

Where one hand meets the other: limb-specific and action-dependent movement plans decoded from preparatory signals in single human frontoparietal brain areas.

PubMed

Gallivan, Jason P; McLean, D Adam; Flanagan, J Randall; Culham, Jody C

2013-01-30

Planning object-directed hand actions requires successful integration of the movement goal with the acting limb. Exactly where and how this sensorimotor integration occurs in the brain has been studied extensively with neurophysiological recordings in nonhuman primates, yet to date, because of limitations of non-invasive methodologies, the ability to examine the same types of planning-related signals in humans has been challenging. Here we show, using a multivoxel pattern analysis of functional MRI (fMRI) data, that the preparatory activity patterns in several frontoparietal brain regions can be used to predict both the limb used and hand action performed in an upcoming movement. Participants performed an event-related delayed movement task whereby they planned and executed grasp or reach actions with either their left or right hand toward a single target object. We found that, although the majority of frontoparietal areas represented hand actions (grasping vs reaching) for the contralateral limb, several areas additionally coded hand actions for the ipsilateral limb. Notable among these were subregions within the posterior parietal cortex (PPC), dorsal premotor cortex (PMd), ventral premotor cortex, dorsolateral prefrontal cortex, presupplementary motor area, and motor cortex, a region more traditionally implicated in contralateral movement generation. Additional analyses suggest that hand actions are represented independently of the intended limb in PPC and PMd. In addition to providing a unique mapping of limb-specific and action-dependent intention-related signals across the human cortical motor system, these findings uncover a much stronger representation of the ipsilateral limb than expected from previous fMRI findings.

Cognitive performance under motor demands - On the influence of task difficulty and postural control.

PubMed

Liebherr, Magnus; Weiland-Breckle, Hanna; Grewe, Tanja; Schumacher, Petra B

2018-04-01

We often walk around when we have to think about something, but suddenly stop when we are confronted with a demanding cognitive task, such as calculating 1540*24. While previous neurophysiological research investigated cognitive and motor performance separately, findings that combine both are rare. To get a deeper understanding of the influence of motor demands as well as the difficulty of a simultaneously performed cognitive task, we investigated 20 healthy individuals. Participants performed two cognitive tasks with different levels of difficulty while sitting or standing on one leg. In addition to behavioral data, we recorded the electroencephalogram from 26Ag/AgCI scalp electrodes. The critical time-windows, predefined by visual inspection, yielded an early (200-300 ms, P2) and a subsequent positivity (350-500 ms, P3). Statistical analysis of the early time window registered a motor × cognition interaction. Resolution of this interaction revealed an effect of the cognitive task in the one-legged stance motor condition, with a more pronounced positivity for the difficult task. No significant differences between cognitive tasks emerged for the simple motor condition. The time-window between 350 and 500 ms registered main effects of the motor task and a trend for the cognitive task. While the influence of cognitive task difficulty (in the P3) is in accordance with previous studies, the motor task effect is specific to one-legged stance (cf. no effects for running in previous research). The motor-cognition interaction found in the P2 indicates that the more difficult motor task (one-legged stance) facilitates cognitive task performance. Copyright © 2018 Elsevier B.V. All rights reserved.

Economic decision-making compared with an equivalent motor task.

PubMed

Wu, Shih-Wei; Delgado, Mauricio R; Maloney, Laurence T

2009-04-14

There is considerable evidence that human economic decision-making deviates from the predictions of expected utility theory (EUT) and that human performance conforms to EUT in many perceptual and motor decision tasks. It is possible that these results reflect a real difference in decision-making in the 2 domains but it is also possible that the observed discrepancy simply reflects typical differences in experimental design. We developed a motor task that is mathematically equivalent to choosing between lotteries and used it to compare how the same subject chose between classical economic lotteries and the same lotteries presented in equivalent motor form. In experiment 1, we found that subjects are more risk seeking in deciding between motor lotteries. In experiment 2, we used cumulative prospect theory to model choice and separately estimated the probability weighting functions and the value functions for each subject carrying out each task. We found no patterned differences in how subjects represented outcome value in the motor and the classical tasks. However, the probability weighting functions for motor and classical tasks were markedly and significantly different. Those for the classical task showed a typical tendency to overweight small probabilities and underweight large probabilities, and those for the motor task showed the opposite pattern of probability distortion. This outcome also accounts for the increased risk-seeking observed in the motor tasks of experiment 1. We conclude that the same subject distorts probability, but not value, differently in making identical decisions in motor and classical form.

Decoding word and category-specific spatiotemporal representations from MEG and EEG

PubMed Central

Chan, Alexander M.; Halgren, Eric; Marinkovic, Ksenija; Cash, Sydney S.

2010-01-01

The organization and localization of lexico-semantic information in the brain has been debated for many years. Specifically, lesion and imaging studies have attempted to map the brain areas representing living versus non-living objects, however, results remain variable. This may be due, in part, to the fact that the univariate statistical mapping analyses used to detect these brain areas are typically insensitive to subtle, but widespread, effects. Decoding techniques, on the other hand, allow for a powerful multivariate analysis of multichannel neural data. In this study, we utilize machine-learning algorithms to first demonstrate that semantic category, as well as individual words, can be decoded from EEG and MEG recordings of subjects performing a language task. Mean accuracies of 76% (chance = 50%) and 83% (chance = 20%) were obtained for the decoding of living vs. non-living category or individual words respectively. Furthermore, we utilize this decoding analysis to demonstrate that the representations of words and semantic category are highly distributed both spatially and temporally. In particular, bilateral anterior temporal, bilateral inferior frontal, and left inferior temporal-occipital sensors are most important for discrimination. Successful intersubject and intermodality decoding shows that semantic representations between stimulus modalities and individuals are reasonably consistent. These results suggest that both word and category-specific information are present in extracranially recorded neural activity and that these representations may be more distributed, both spatially and temporally, than previous studies suggest. PMID:21040796

Relationship between binocular vision, visual acuity, and fine motor skills.

PubMed

O'Connor, Anna R; Birch, Eileen E; Anderson, Susan; Draper, Hayley

2010-12-01

The aims of this study were to analyze the relationship between the performance on fine motor skills tasks and peripheral and bifoveal sensory fusion, phasic and tonic motor fusion, the level of visual acuity (VA) in the poorer seeing eye, and the interocular VA difference. Subjects aged 12 to 28 years with a range of levels of binocular vision and VA performed three tasks: Purdue pegboard (number of pegs placed in 30 s), bead threading task (with two sizes of bead to increase the difficulty, time taken to thread a fixed number of beads), and a water pouring task (accuracy and time to pour a fixed quantity into five glass cylinders). Ophthalmic measures included peripheral (Worth 4 dot) and bifoveal (4 prism diopter) sensory fusion, phasic (prism bar) and tonic (Risley rotary prism) motor fusion ranges, and monocular VA. One hundred twenty-one subjects with a mean age of 18.8 years were tested; 18.2% had a manifest strabismus. Performance on fine motor skills tasks was significantly better in subjects with sensory and motor fusion compared with those without for most tasks, with significant differences between those with and without all measures of fusion on the pegboard and bead task. Both the acuity in the poorer seeing eye (highest r value of all motor tasks = 0.43) and the interocular acuity difference were statistically significantly related to performance on the motor skill tasks. Both sensory and motor fusion and good VA in both eyes are of benefit in the performance of fine motor skills tasks, with the presence of some binocular vision being beneficial compared with no fusion on certain sensorimotor tasks. This evidence supports the need to maximize fusion and VA outcomes.

Working Memory Training Improves Dual-Task Performance on Motor Tasks.

PubMed

Kimura, Takehide; Kaneko, Fuminari; Nagahata, Keita; Shibata, Eriko; Aoki, Nobuhiro

2017-01-01

The authors investigated whether working memory training improves motor-motor dual-task performance consisted of upper and lower limb tasks. The upper limb task was a simple reaction task and the lower limb task was an isometric knee extension task. 45 participants (age = 21.8 ± 1.6 years) were classified into a working memory training group (WM-TRG), dual-task training group, or control group. The training duration was 2 weeks (15 min, 4 times/week). Our results indicated that working memory capacity increased significantly only in the WM-TRG. Dual-task performance improved in the WM-TRG and dual-task training group. Our study provides the novel insight that working memory training improves dual-task performance without specific training on the target motor task.

Automatic detection and decoding of honey bee waggle dances

PubMed Central

Wild, Benjamin; Rojas, Raúl; Landgraf, Tim

2017-01-01

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer’s movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07%. The decoded waggle orientation has an average error of -2.92° (± 7.37°), well within the range of human error. To evaluate and exemplify the system’s performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance. PMID:29236712

Transfer of training between distinct motor tasks after stroke: Implications for task- specific approaches to upper extremity neurorehabilitation

PubMed Central

Schaefer, Sydney Y.; Patterson, Chavelle B.; Lang, Catherine E.

2013-01-01

Background Although task-specific training is emerging as a viable approach for recovering motor function after stroke, there is little evidence for whether the effects of such training transfer to other functional motor tasks not directly practiced in therapy. Objective The purpose of the current study was to test whether training on one motor task would transfer to untrained tasks that were either spatiotemporally similar or different in individuals with chronic hemiparesis post-stroke. Methods Eleven participants with chronic mild-to-moderate hemiparesis following stroke completed five days of supervised massed practice of a feeding task with their affected side. Performance on the feeding task, along with two other untrained functional upper extremity motor tasks (sorting, dressing) was assessed before and after training. Results Performance of all three tasks improved significantly after training exclusively on one motor task. The amount of improvement in the untrained tasks was comparable, and was not dependent on the degree of similarity to the trained task. Conclusions Because the number and type of tasks that can be practiced are often limited within standard stroke rehabilitation, results from this study will be useful for designing task-specific training plans to maximize therapy benefits. PMID:23549521

The attentional blink is related to phonemic decoding, but not sight-word recognition, in typically reading adults.

PubMed

Tyson-Parry, Maree M; Sailah, Jessica; Boyes, Mark E; Badcock, Nicholas A

2015-10-01

This research investigated the relationship between the attentional blink (AB) and reading in typical adults. The AB is a deficit in the processing of the second of two rapidly presented targets when it occurs in close temporal proximity to the first target. Specifically, this experiment examined whether the AB was related to both phonological and sight-word reading abilities, and whether the relationship was mediated by accuracy on a single-target rapid serial visual processing task (single-target accuracy). Undergraduate university students completed a battery of tests measuring reading ability, non-verbal intelligence, and rapid automatised naming, in addition to rapid serial visual presentation tasks in which they were required to identify either two (AB task) or one (single target task) target/s (outlined shapes: circle, square, diamond, cross, and triangle) in a stream of random-dot distractors. The duration of the AB was related to phonological reading (n=41, β=-0.43): participants who exhibited longer ABs had poorer phonemic decoding skills. The AB was not related to sight-word reading. Single-target accuracy did not mediate the relationship between the AB and reading, but was significantly related to AB depth (non-linear fit, R(2)=.50): depth reflects the maximal cost in T2 reporting accuracy in the AB. The differential relationship between the AB and phonological versus sight-word reading implicates common resources used for phonemic decoding and target consolidation, which may be involved in cognitive control. The relationship between single-target accuracy and the AB is discussed in terms of cognitive preparation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Remapping residual coordination for controlling assistive devices and recovering motor functions

PubMed Central

Pierella, Camilla; Abdollahi, Farnaz; Farshchiansadegh, Ali; Pedersen, Jessica; Thorp, Elias; Mussa-Ivaldi, Ferdinando A.; Casadio, Maura

2015-01-01

The concept of human motor redundancy attracted much attention since the early studies of motor control, as it highlights the ability of the motor system to generate a great variety of movements to achieve any single well-defined goal. The abundance of degrees of freedom in the human body may be a fundamental resource in the learning and remapping problems that are encountered in human–machine interfaces (HMIs) developments. The HMI can act at different levels decoding brain signals or body signals to control an external device. The transformation from neural signals to device commands is the core of research on brain-machine interfaces (BMIs). However, while BMIs bypass completely the final path of the motor system, body-machine interfaces (BoMIs) take advantage of motor skills that are still available to the user and have the potential to enhance these skills through their consistent use. BoMIs empower people with severe motor disabilities with the possibility to control external devices, and they concurrently offer the opportunity to focus on achieving rehabilitative goals. In this study we describe a theoretical paradigm for the use of a BoMI in rehabilitation. The proposed BoMI remaps the user’s residual upper body mobility to the two coordinates of a cursor on a computer screen. This mapping is obtained by principal component analysis (PCA). We hypothesize that the BoMI can be specifically programmed to engage the users in functional exercises aimed at partial recovery of motor skills, while simultaneously controlling the cursor and carrying out functional tasks, e.g. playing games. Specifically, PCA allows us to select not only the subspace that is most comfortable for the user to act upon, but also the degrees of freedom and coordination patterns that the user has more difficulty engaging. In this article, we describe a family of map modifications that can be made to change the motor behavior of the user. Depending on the characteristics of the impairment of each high-level spinal cord injury (SCI) survivor, we can make modifications to restore a higher level of symmetric mobility (left versus right), or to increase the strength and range of motion of the upper body that was spared by the injury. Results showed that this approach restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom in the participants involved in the control of the interface. This is a proof of concept that our BoMI may be used concurrently to control assistive devices and reach specific rehabilitative goals. Engaging the users in functional and entertaining tasks while practicing the interface and changing the map in the proposed ways is a novel approach to rehabilitation treatments facilitated by portable and low-cost technologies. PMID:26341935

Remapping residual coordination for controlling assistive devices and recovering motor functions.

PubMed

Pierella, Camilla; Abdollahi, Farnaz; Farshchiansadegh, Ali; Pedersen, Jessica; Thorp, Elias B; Mussa-Ivaldi, Ferdinando A; Casadio, Maura

2015-12-01

The concept of human motor redundancy attracted much attention since the early studies of motor control, as it highlights the ability of the motor system to generate a great variety of movements to achieve any well-defined goal. The abundance of degrees of freedom in the human body may be a fundamental resource in the learning and remapping problems that are encountered in human-machine interfaces (HMIs) developments. The HMI can act at different levels decoding brain signals or body signals to control an external device. The transformation from neural signals to device commands is the core of research on brain-machine interfaces (BMIs). However, while BMIs bypass completely the final path of the motor system, body-machine interfaces (BoMIs) take advantage of motor skills that are still available to the user and have the potential to enhance these skills through their consistent use. BoMIs empower people with severe motor disabilities with the possibility to control external devices, and they concurrently offer the opportunity to focus on achieving rehabilitative goals. In this study we describe a theoretical paradigm for the use of a BoMI in rehabilitation. The proposed BoMI remaps the user's residual upper body mobility to the two coordinates of a cursor on a computer screen. This mapping is obtained by principal component analysis (PCA). We hypothesize that the BoMI can be specifically programmed to engage the users in functional exercises aimed at partial recovery of motor skills, while simultaneously controlling the cursor and carrying out functional tasks, e.g. playing games. Specifically, PCA allows us to select not only the subspace that is most comfortable for the user to act upon, but also the degrees of freedom and coordination patterns that the user has more difficulty engaging. In this article, we describe a family of map modifications that can be made to change the motor behavior of the user. Depending on the characteristics of the impairment of each high-level spinal cord injury (SCI) survivor, we can make modifications to restore a higher level of symmetric mobility (left versus right), or to increase the strength and range of motion of the upper body that was spared by the injury. Results showed that this approach restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom in the participants involved in the control of the interface. This is a proof of concept that our BoMI may be used concurrently to control assistive devices and reach specific rehabilitative goals. Engaging the users in functional and entertaining tasks while practicing the interface and changing the map in the proposed ways is a novel approach to rehabilitation treatments facilitated by portable and low-cost technologies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Task-dependent enhancement of facial expression and identity representations in human cortex.

PubMed

Dobs, Katharina; Schultz, Johannes; Bülthoff, Isabelle; Gardner, Justin L

2018-05-15

What cortical mechanisms allow humans to easily discern the expression or identity of a face? Subjects detected changes in expression or identity of a stream of dynamic faces while we measured BOLD responses from topographically and functionally defined areas throughout the visual hierarchy. Responses in dorsal areas increased during the expression task, whereas responses in ventral areas increased during the identity task, consistent with previous studies. Similar to ventral areas, early visual areas showed increased activity during the identity task. If visual responses are weighted by perceptual mechanisms according to their magnitude, these increased responses would lead to improved attentional selection of the task-appropriate facial aspect. Alternatively, increased responses could be a signature of a sensitivity enhancement mechanism that improves representations of the attended facial aspect. Consistent with the latter sensitivity enhancement mechanism, attending to expression led to enhanced decoding of exemplars of expression both in early visual and dorsal areas relative to attending identity. Similarly, decoding identity exemplars when attending to identity was improved in dorsal and ventral areas. We conclude that attending to expression or identity of dynamic faces is associated with increased selectivity in representations consistent with sensitivity enhancement. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

The LAC Test: A New Look at Auditory Conceptualization and Literacy Development K-12.

ERIC Educational Resources Information Center

Lindamood, Charles; And Others

The Lindamood Auditory Conceptualization (LAC) Test was constructed with the recognition that the process of decoding involves an integration of the auditory, visual, and motor senses. Requiring the manipulation of colored blocks to indicate conceptualization of test patterns spoken by the examiner, subtest 1 entails coding of identity, number,…

Neuroprosthetic Decoder Training as Imitation Learning.

PubMed

Merel, Josh; Carlson, David; Paninski, Liam; Cunningham, John P

2016-05-01

Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.

Reading and writing skills in young adults with spina bifida and hydrocephalus.

PubMed

Barnes, Marcia; Dennis, Maureen; Hetherington, Ross

2004-09-01

Reading and writing were studied in 31 young adults with spina bifida and hydrocephalus (SBH). Like children with this condition, young adults with SBH had better word decoding than reading comprehension, and, compared to population means, had lower scores on a test of writing fluency. Reading comprehension was predicted by word decoding and listening comprehension. Writing was predicted by fine motor finger function, verbal intelligence, and short-term and working memory. These findings are consistent with cognitive models of reading and writing. Writing, but not reading, was related to highest level of education achieved and writing fluency predicted several aspects of functional independence. Reading comprehension and writing remain deficient in adults with SBH and have consequences for educational attainments and functional independence.

Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording

PubMed Central

Eliseyev, Andrey; Aksenova, Tetiana

2016-01-01

In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demonstrated that the proposed methods combined the prediction accuracy of the algorithms of the PLS family and trajectory smoothness of the Kalman Filter. In addition, the prediction delay is significantly lower for the proposed algorithms than for the Kalman Filter approach. The proposed methods could be applied in a wide range of applications beyond neuroscience. PMID:27196417

Learning redundant motor tasks with and without overlapping dimensions: facilitation and interference effects.

PubMed

Ranganathan, Rajiv; Wieser, Jon; Mosier, Kristine M; Mussa-Ivaldi, Ferdinando A; Scheidt, Robert A

2014-06-11

Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills. Copyright © 2014 the authors 0270-6474/14/348289-11$15.00/0.

Speech Motor Sequence Learning: Effect of Parkinson Disease and Normal Aging on Dual-Task Performance.

PubMed

Whitfield, Jason A; Goberman, Alexander M

2017-06-22

Everyday communication is carried out concurrently with other tasks. Therefore, determining how dual tasks interfere with newly learned speech motor skills can offer insight into the cognitive mechanisms underlying speech motor learning in Parkinson disease (PD). The current investigation examines a recently learned speech motor sequence under dual-task conditions. A previously learned sequence of 6 monosyllabic nonwords was examined using a dual-task paradigm. Participants repeated the sequence while concurrently performing a visuomotor task, and performance on both tasks was measured in single- and dual-task conditions. The younger adult group exhibited little to no dual-task interference on the accuracy and duration of the sequence. The older adult group exhibited variability in dual-task costs, with the group as a whole exhibiting an intermediate, though significant, amount of dual-task interference. The PD group exhibited the largest degree of bidirectional dual-task interference among all the groups. These data suggest that PD affects the later stages of speech motor learning, as the dual-task condition interfered with production of the recently learned sequence beyond the effect of normal aging. Because the basal ganglia is critical for the later stages of motor sequence learning, the observed deficits may result from the underlying neural dysfunction associated with PD.

Interrelations between three fine motor skills in young adults.

PubMed

Lorås, Håvard; Sigmundsson, Hermundur

2012-08-01

Motor skills are typically considered to be highly specific, although some researchers have attempted to identify evidence for general motor aptitude. The present study tested these contentions by assessing the extent of relationship between fine motor tasks, using correlations between selected performance measures for three fine motor skills. University students ages 18 to 35 years (N = 305; 147 men, 158 women) completed three fine motor tasks with both right and left hands (placing pegs, posting coins, and placing bricks). Performance was assessed by time to complete each individual task. The intercorrelations between the three tasks were generally low and at a level that can be expected by chance (r < or = .3), indicating that performance was quite specific to the individual skills rather than attributable to a general ability. As a further test for evidence for a general motor ability, the dimensionality of the data set was analyzed using a principal component analysis on the correlation matrix. A three-factor solution explaining approximately 80% of the total variance in performance on the fine motor tasks was identified, where each factor could be associated with each fine motor task. These findings provide further support for the high specificity in fine motor skills and against the existence of a general aptitude for motor ability.

Men are more accurate than women in aiming at targets in both near space and extrapersonal space.

PubMed

Sykes Tottenham, Laurie; Saucier, Deborah M; Elias, Lorin J; Gutwin, Carl

2005-08-01

Men excel at motor tasks requiring aiming accuracy whereas women excel at different tasks requiring fine motor skill. However, these tasks are confounded with proximity to the body, as fine motor tasks are performed proximally and aiming tasks are directed at distal targets. As such, it is not known whether the male advantage on tasks requiring aiming accuracy is because men have better aim or is better in the proximal domain in which the task is usually presented. 18 men (M age = 20.6 yr., SD = 3.0) and 20 women (M age = 18.7 yr., SD = 0.9) performed 2 tasks of extrapersonal aiming accuracy (>2 m away), 2 tasks of aiming accuracy performed in near space (< 1 m from them), and a task of fine motor skill. Men outperformed women on both the extrapersonal aiming tasks, and women outperformed men on the task of fine motor skill. However, a male advantage was observed for one of the aiming tasks performed in near space, suggesting that the male advantage for aiming accuracy does not result from proximity.

Flight simulation using a Brain-Computer Interface: A pilot, pilot study.

PubMed

Kryger, Michael; Wester, Brock; Pohlmeyer, Eric A; Rich, Matthew; John, Brendan; Beaty, James; McLoughlin, Michael; Boninger, Michael; Tyler-Kabara, Elizabeth C

2017-01-01

As Brain-Computer Interface (BCI) systems advance for uses such as robotic arm control it is postulated that the control paradigms could apply to other scenarios, such as control of video games, wheelchair movement or even flight. The purpose of this pilot study was to determine whether our BCI system, which involves decoding the signals of two 96-microelectrode arrays implanted into the motor cortex of a subject, could also be used to control an aircraft in a flight simulator environment. The study involved six sessions in which various parameters were modified in order to achieve the best flight control, including plane type, view, control paradigm, gains, and limits. Successful flight was determined qualitatively by evaluating the subject's ability to perform requested maneuvers, maintain flight paths, and avoid control losses such as dives, spins and crashes. By the end of the study, it was found that the subject could successfully control an aircraft. The subject could use both the jet and propeller plane with different views, adopting an intuitive control paradigm. From the subject's perspective, this was one of the most exciting and entertaining experiments she had performed in two years of research. In conclusion, this study provides a proof-of-concept that traditional motor cortex signals combined with a decoding paradigm can be used to control systems besides a robotic arm for which the decoder was developed. Aside from possible functional benefits, it also shows the potential for a new recreational activity for individuals with disabilities who are able to master BCI control. Copyright © 2016 Elsevier Inc. All rights reserved.

Combining d-cycloserine with motor training does not result in improved general motor learning in neurologically intact people or in people with stroke

PubMed Central

Cherry, Kendra M.; Lenze, Eric J.

2014-01-01

Neurological rehabilitation involving motor training has resulted in clinically meaningful improvements in function but is unable to eliminate many of the impairments associated with neurological injury. Thus there is a growing need for interventions that facilitate motor learning during rehabilitation therapy, to optimize recovery. d-Cycloserine (DCS), a partial N-methyl-d-aspartate (NMDA) receptor agonist that enhances neurotransmission throughout the central nervous system (Ressler KJ, Rothbaum BO, Tannenbaum L, Anderson P, Graap K, Zimand E, Hodges L, Davis M. Arch Gen Psychiatry 61: 1136–1144, 2004), has been shown to facilitate declarative and emotional learning. We therefore tested whether combining DCS with motor training facilitates motor learning after stroke in a series of two experiments. Forty-one healthy adults participated in experiment I, and twenty adults with stroke participated in experiment II of this two-session, double-blind study. Session one consisted of baseline assessment, subject randomization, and oral administration of DCS or placebo (250 mg). Subjects then participated in training on a balancing task, a simulated feeding task, and a cognitive task. Subjects returned 1–3 days later for posttest assessment. We found that all subjects had improved performance from pretest to posttest on the balancing task, the simulated feeding task, and the cognitive task. Subjects who were given DCS before motor training, however, did not show enhanced learning on the balancing task, the simulated feeding task, or the associative recognition task compared with subjects given placebo. Moreover, training on the balancing task did not generalize to a similar, untrained balance task. Our findings suggest that DCS does not enhance motor learning or motor skill generalization in neurologically intact adults or in adults with stroke. PMID:24671538

Predicting explorative motor learning using decision-making and motor noise.

PubMed

Chen, Xiuli; Mohr, Kieran; Galea, Joseph M

2017-04-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

Predicting explorative motor learning using decision-making and motor noise

PubMed Central

Galea, Joseph M.

2017-01-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning. PMID:28437451

Cognitive-Motor Interference on Upper Extremity Motor Performance in a Robot-Assisted Planar Reaching Task Among Patients With Stroke.

PubMed

Shin, Joon-Ho; Park, Gyulee; Cho, Duk Youn

2017-04-01

To explore motor performance on 2 different cognitive tasks during robotic rehabilitation in which motor performance was longitudinally assessed. Prospective study. Rehabilitation hospital. Patients (N=22) with chronic stroke and upper extremity impairment. A total of 640 repetitions of robot-assisted planar reaching, 5 times a week for 4 weeks. Longitudinal robotic evaluations regarding motor performance included smoothness, mean velocity, path error, and reach error by the type of cognitive task. Dual-task effects (DTEs) of motor performance were computed to analyze the effect of the cognitive task on dual-task interference. Cognitive task type influenced smoothness (P=.006), the DTEs of smoothness (P=.002), and the DTEs of reach error (P=.052). Robotic rehabilitation improved smoothness (P=.007) and reach error (P=.078), while stroke severity affected smoothness (P=.01), reach error (P<.001), and path error (P=.01). Robotic rehabilitation or severity did not affect the DTEs of motor performance. The results provide evidence for the effect of cognitive-motor interference on upper extremity performance among participants with stroke using a robotic-guided rehabilitation system. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A quantitative meta-analysis and review of motor learning in the human brain

PubMed Central

Hardwick, Robert M.; Rottschy, Claudia; Miall, R. Chris; Eickhoff, Simon B.

2013-01-01

Neuroimaging studies have improved our understanding of which brain structures are involved in motor learning. Despite this, questions remain regarding the areas that contribute consistently across paradigms with different task demands. For instance, sensorimotor tasks focus on learning novel movement kinematics and dynamics, while serial response time task (SRTT) variants focus on sequence learning. These differing task demands are likely to elicit quantifiably different patterns of neural activity on top of a potentially consistent core network. The current study identified consistent activations across 70 motor learning experiments using activation likelihood estimation (ALE) meta-analysis. A global analysis of all tasks revealed a bilateral cortical–subcortical network consistently underlying motor learning across tasks. Converging activations were revealed in the dorsal premotor cortex, supplementary motor cortex, primary motor cortex, primary somatosensory cortex, superior parietal lobule, thalamus, putamen and cerebellum. These activations were broadly consistent across task specific analyses that separated sensorimotor tasks and SRTT variants. Contrast analysis indicated that activity in the basal ganglia and cerebellum was significantly stronger for sensorimotor tasks, while activity in cortical structures and the thalamus was significantly stronger for SRTT variants. Additional conjunction analyses then indicated that the left dorsal premotor cortex was activated across all analyses considered, even when controlling for potential motor confounds. The highly consistent activation of the left dorsal premotor cortex suggests it is a critical node in the motor learning network. PMID:23194819

Theory of mind in remitted bipolar disorder: Younger patients struggle in tasks of higher ecological validity.

PubMed

Feyerabend, Julia; Lüttke, Stefan; Grosse-Wentrup, Fabienne; Wolter, Sibylla; Hautzinger, Martin; Wolkenstein, Larissa

2018-04-15

To date, research concerning Theory of Mind (ToM) in remitted bipolar disorder (rBD) has yielded inconclusive results. This may be a result of methodological shortcomings and the failure to consider relevant third variables. Furthermore, studies using ecologically valid stimuli are rare. This study examines ToM in rBD patients, using ecologically valid stimuli. Additionally, the effects of sad mood induction (MI) as well as of age and gender are considered. The sample comprises N = 44 rBD patients (rBDPs) and N = 40 healthy controls (HCs). ToM decoding is assessed using the Cambridge Mindreading Face-Voice-Battery (CAM) and ToM reasoning using the Movie for the Assessment of Social Cognition (MASC). Both tasks were divided into two parts to conduct one part with and one without MI. While across the whole sample there was no evidence that rBDPs and HCs differed in ToM decoding or reasoning, in the younger subsample (age < 45) rBDPs performed worse than HCs in ToM decoding. While MI negatively influenced reasoning in both groups, gender had no effect. Most patients in this study had a high level of social functioning, limiting the generalizability of the results. As important social steps have to be undertaken before middle-age, the decoding deficits in younger rBDPs might be of particular importance not only for social functioning but also for the course of illness. Furthermore, this age-related deficit may explain the inconclusive findings that have been reported so far. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatial co-adaptation of cortical control columns in a micro-ECoG brain-computer interface

NASA Astrophysics Data System (ADS)

Rouse, A. G.; Williams, J. J.; Wheeler, J. J.; Moran, D. W.

2016-10-01

Objective. Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used. Approach. The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 μm diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored. Main results. Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme. Significance. Our results clearly show that neural activity under a BCI recording electrode (which we define as a ‘cortical control column’) readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.

Motor performance of tongue with a computer-integrated system under different levels of background physical exertion

PubMed Central

Huo, Xueliang; Johnson-Long, Ashley N.; Ghovanloo, Maysam; Shinohara, Minoru

2015-01-01

The purpose of this study was to compare the motor performance of tongue, using Tongue Drive System, to hand operation for relatively complex tasks under different levels of background physical exertion. Thirteen young able-bodied adults performed tasks that tested the accuracy and variability in tracking a sinusoidal waveform, and the performance in playing two video games that require accurate and rapid movements with cognitive processing using tongue and hand under two levels of background physical exertion. Results show additional background physical activity did not influence rapid and accurate displacement motor performance, but compromised the slow waveform tracking and shooting performances in both hand and tongue. Slow waveform tracking performance by the tongue was compromised with an additional motor or cognitive task, but with an additional motor task only for the hand. Practitioner Summary We investigated the influence of task complexity and background physical exertion on the motor performance of tongue and hand. Results indicate the task performance degrades with an additional concurrent task or physical exertion due to the limited attentional resources available for handling both the motor task and background exertion. PMID:24003900

Load type influences motor unit recruitment in biceps brachii during a sustained contraction.

PubMed

Baudry, Stéphane; Rudroff, Thorsten; Pierpoint, Lauren A; Enoka, Roger M

2009-09-01

Twenty subjects participated in four experiments designed to compare time to task failure and motor-unit recruitment threshold during contractions sustained at 15% of maximum as the elbow flexor muscles either supported an inertial load (position task) or exerted an equivalent constant torque against a rigid restraint (force task). Subcutaneous branched bipolar electrodes were used to record single motor unit activity from the biceps brachii muscle during ramp contractions performed before and at 50 and 90% of the time to failure for the position task during both fatiguing contractions. The time to task failure was briefer for the position task than for the force task (P=0.0002). Thirty and 29 motor units were isolated during the force and position tasks, respectively. The recruitment threshold declined by 48 and 30% (P=0.0001) during the position task for motor units with an initial recruitment threshold below and above the target force, respectively, whereas no significant change in recruitment threshold was observed during the force task. Changes in recruitment threshold were associated with a decrease in the mean discharge rate (-16%), an increase in discharge rate variability (+40%), and a prolongation of the first two interspike intervals (+29 and +13%). These data indicate that there were faster changes in motor unit recruitment and rate coding during the position task than the force task despite a similar net muscle torque during both tasks. Moreover, the results suggest that the differential synaptic input observed during the position task influences most of the motor unit pool.

Decoding English Alphabet Letters Using EEG Phase Information

PubMed Central

Wang, YiYan; Wang, Pingxiao; Yu, Yuguo

2018-01-01

Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition. PMID:29467615

Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMER.

PubMed

Ferreira, Miguel; Roma, Nuno; Russo, Luis M S

2014-05-30

HMMER is a commonly used bioinformatics tool based on Hidden Markov Models (HMMs) to analyze and process biological sequences. One of its main homology engines is based on the Viterbi decoding algorithm, which was already highly parallelized and optimized using Farrar's striped processing pattern with Intel SSE2 instruction set extension. A new SIMD vectorization of the Viterbi decoding algorithm is proposed, based on an SSE2 inter-task parallelization approach similar to the DNA alignment algorithm proposed by Rognes. Besides this alternative vectorization scheme, the proposed implementation also introduces a new partitioning of the Markov model that allows a significantly more efficient exploitation of the cache locality. Such optimization, together with an improved loading of the emission scores, allows the achievement of a constant processing throughput, regardless of the innermost-cache size and of the dimension of the considered model. The proposed optimized vectorization of the Viterbi decoding algorithm was extensively evaluated and compared with the HMMER3 decoder to process DNA and protein datasets, proving to be a rather competitive alternative implementation. Being always faster than the already highly optimized ViterbiFilter implementation of HMMER3, the proposed Cache-Oblivious Parallel SIMD Viterbi (COPS) implementation provides a constant throughput and offers a processing speedup as high as two times faster, depending on the model's size.

Differences in Visuo-Motor Control in Skilled vs. Novice Martial Arts Athletes during Sustained and Transient Attention Tasks: A Motor-Related Cortical Potential Study

PubMed Central

Sanchez-Lopez, Javier; Fernandez, Thalia; Silva-Pereyra, Juan; Martinez Mesa, Juan A.; Di Russo, Francesco

2014-01-01

Cognitive and motor processes are essential for optimal athletic performance. Individuals trained in different skills and sports may have specialized cognitive abilities and motor strategies related to the characteristics of the activity and the effects of training and expertise. Most studies have investigated differences in motor-related cortical potential (MRCP) during self-paced tasks in athletes but not in stimulus-related tasks. The aim of the present study was to identify the differences in performance and MRCP between skilled and novice martial arts athletes during two different types of tasks: a sustained attention task and a transient attention task. Behavioral and electrophysiological data from twenty-two martial arts athletes were obtained while they performed a continuous performance task (CPT) to measure sustained attention and a cued continuous performance task (c-CPT) to measure transient attention. MRCP components were analyzed and compared between groups. Electrophysiological data in the CPT task indicated larger prefrontal positive activity and greater posterior negativity distribution prior to a motor response in the skilled athletes, while novices showed a significantly larger response-related P3 after a motor response in centro-parietal areas. A different effect occurred in the c-CPT task in which the novice athletes showed strong prefrontal positive activity before a motor response and a large response-related P3, while in skilled athletes, the prefrontal activity was absent. We propose that during the CPT, skilled athletes were able to allocate two different but related processes simultaneously according to CPT demand, which requires controlled attention and controlled motor responses. On the other hand, in the c-CPT, skilled athletes showed better cue facilitation, which permitted a major economy of resources and “automatic” or less controlled responses to relevant stimuli. In conclusion, the present data suggest that motor expertise enhances neural flexibility and allows better adaptation of cognitive control to the requested task. PMID:24621480

Differences in visuo-motor control in skilled vs. novice martial arts athletes during sustained and transient attention tasks: a motor-related cortical potential study.

PubMed

Sanchez-Lopez, Javier; Fernandez, Thalia; Silva-Pereyra, Juan; Martinez Mesa, Juan A; Di Russo, Francesco

2014-01-01

Cognitive and motor processes are essential for optimal athletic performance. Individuals trained in different skills and sports may have specialized cognitive abilities and motor strategies related to the characteristics of the activity and the effects of training and expertise. Most studies have investigated differences in motor-related cortical potential (MRCP) during self-paced tasks in athletes but not in stimulus-related tasks. The aim of the present study was to identify the differences in performance and MRCP between skilled and novice martial arts athletes during two different types of tasks: a sustained attention task and a transient attention task. Behavioral and electrophysiological data from twenty-two martial arts athletes were obtained while they performed a continuous performance task (CPT) to measure sustained attention and a cued continuous performance task (c-CPT) to measure transient attention. MRCP components were analyzed and compared between groups. Electrophysiological data in the CPT task indicated larger prefrontal positive activity and greater posterior negativity distribution prior to a motor response in the skilled athletes, while novices showed a significantly larger response-related P3 after a motor response in centro-parietal areas. A different effect occurred in the c-CPT task in which the novice athletes showed strong prefrontal positive activity before a motor response and a large response-related P3, while in skilled athletes, the prefrontal activity was absent. We propose that during the CPT, skilled athletes were able to allocate two different but related processes simultaneously according to CPT demand, which requires controlled attention and controlled motor responses. On the other hand, in the c-CPT, skilled athletes showed better cue facilitation, which permitted a major economy of resources and "automatic" or less controlled responses to relevant stimuli. In conclusion, the present data suggest that motor expertise enhances neural flexibility and allows better adaptation of cognitive control to the requested task.

Motor-cognitive dual-task performance: effects of a concurrent motor task on distinct components of visual processing capacity.

PubMed

Künstler, E C S; Finke, K; Günther, A; Klingner, C; Witte, O; Bublak, P

2018-01-01

Dual tasking, or the simultaneous execution of two continuous tasks, is frequently associated with a performance decline that can be explained within a capacity sharing framework. In this study, we assessed the effects of a concurrent motor task on the efficiency of visual information uptake based on the 'theory of visual attention' (TVA). TVA provides parameter estimates reflecting distinct components of visual processing capacity: perceptual threshold, visual processing speed, and visual short-term memory (VSTM) storage capacity. Moreover, goodness-of-fit values and bootstrapping estimates were derived to test whether the TVA-model is validly applicable also under dual task conditions, and whether the robustness of parameter estimates is comparable in single- and dual-task conditions. 24 subjects of middle to higher age performed a continuous tapping task, and a visual processing task (whole report of briefly presented letter arrays) under both single- and dual-task conditions. Results suggest a decline of both visual processing capacity and VSTM storage capacity under dual-task conditions, while the perceptual threshold remained unaffected by a concurrent motor task. In addition, goodness-of-fit values and bootstrapping estimates support the notion that participants processed the visual task in a qualitatively comparable, although quantitatively less efficient way under dual-task conditions. The results support a capacity sharing account of motor-cognitive dual tasking and suggest that even performing a relatively simple motor task relies on central attentional capacity that is necessary for efficient visual information uptake.

Recognition of schematic facial displays of emotion in parents of children with autism.

PubMed

Palermo, Mark T; Pasqualetti, Patrizio; Barbati, Giulia; Intelligente, Fabio; Rossini, Paolo Maria

2006-07-01

Performance on an emotional labeling task in response to schematic facial patterns representing five basic emotions without the concurrent presentation of a verbal category was investigated in 40 parents of children with autism and 40 matched controls. 'Autism fathers' performed worse than 'autism mothers', who performed worse than controls in decoding displays representing sadness or disgust. This indicates the need to include facial expression decoding tasks in genetic research of autism. In addition, emotional expression interactions between parents and their children with autism, particularly through play, where affect and prosody are 'physiologically' exaggerated, may stimulate development of social competence. Future studies could benefit from a combination of stimuli including photographs and schematic drawings, with and without associated verbal categories. This may allow the subdivision of patients and relatives on the basis of the amount of information needed to understand and process social-emotionally relevant information.

Restoration of fMRI Decodability Does Not Imply Latent Working Memory States

PubMed Central

Schneegans, Sebastian; Bays, Paul M.

2018-01-01

Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retrocueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state. PMID:28820674

Decoding communities in networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Decoding communities in networks.

PubMed

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Hand grips strength effect on motor function in human brain using fMRI: a pilot study

NASA Astrophysics Data System (ADS)

Ismail, S. S.; Mohamad, M.; Syazarina, S. O.; Nafisah, W. Y.

2014-11-01

Several methods of motor tasks for fMRI scanning have been evolving from simple to more complex tasks. Motor tasks on upper extremity were applied in order to excite the increscent of motor activation on contralesional and ipsilateral hemispheres in brain. The main objective of this study is to study the different conditions for motor tasks on upper extremity that affected the brain activation. Ten healthy right handed with normal vision (3 male and 7 female, age range=20-30 years, mean=24.6 years, SD=2.21) participated in this study. Prior to the scanning, participants were trained on hand grip tasks using rubber ball and pressure gauge tool outside the scanner. During fMRI session, a block design with 30-s task blocks and alternating 30-s rest periods was employed while participants viewed a computer screen via a back projection-mirror system and instructed to follow the instruction by gripping their hand with normal and strong grips using a rubber ball. Statistical Parametric mapping (SPM8) software was used to determine the brain activation. Both tasks activated the primary motor (M1), supplementary motor area (SMA), dorsal and ventral of premotor cortex area (PMA) in left hemisphere while in right hemisphere the area of primary motor (M1) somatosensory was activated. However, the comparison between both tasks revealed that the strong hand grip showed the higher activation at M1, PMA and SMA on left hemisphere and also the area of SMA on right hemisphere. Both conditions of motor tasks could provide insights the functional organization on human brain.

Motor demands impact speed of information processing in Autism Spectrum Disorders

PubMed Central

Kenworthy, Lauren; Yerys, Benjamin E.; Weinblatt, Rachel; Abrams, Danielle N.; Wallace, Gregory L.

2015-01-01

Objective The apparent contradiction between preserved or even enhanced perceptual processing speed on inspection time tasks in autism spectrum disorders (ASD) and impaired performance on complex processing speed tasks that require motor output (e.g. Wechsler Processing Speed Index) has not yet been systematically investigated. This study investigates whether adding motor output demands to an inspection time task impairs ASD performance compared to that of typically developing control (TDC) children. Method The performance of children with ASD (n=28; mean FSIQ=115) and TDC (n=25; mean FSIQ=122) children was compared on processing speed tasks with increasing motor demand. Correlations were run between ASD task performance and Autism Diagnostic Observation Schedule (ADOS) Communication scores. Results Performance by the ASD and TDC groups on a simple perceptual processing speed task with minimal motor demand was equivalent, though it diverged (ASD worse than TDC) on two tasks with the same stimuli, but increased motor output demands. ASD performance on the moderate but not the high speeded motor output demand task was negatively correlated with ADOS communication symptoms. Conclusions These data address the apparent contradiction between preserved inspection time in the context of slowed “processing speed” in ASD. They show that processing speed is preserved when motor demands are minimized, but that increased motor output demands interfere with the ability to act on perceptual processing of simple stimuli. Reducing motor demands (e.g. through the use of computers) may increase the capacity of people with ASD to demonstrate good perceptual processing in a variety of educational, vocational and social settings. PMID:23937483

Aging and Concurrent Task Performance: Cognitive Demand and Motor Control

ERIC Educational Resources Information Center

Albinet, Cedric; Tomporowski, Phillip D.; Beasman, Kathryn

2006-01-01

A motor task that requires fine control of upper limb movements and a cognitive task that requires executive processing--first performing them separately and then concurrently--was performed by 18 young and 18 older adults. The motor task required participants to tap alternatively on two targets, the sizes of which varied systematically. The…

‘Inner voices’: the cerebral representation of emotional voice cues described in literary texts

PubMed Central

Kreifelts, Benjamin; Gößling-Arnold, Christina; Wertheimer, Jürgen; Wildgruber, Dirk

2014-01-01

While non-verbal affective voice cues are generally recognized as a crucial behavioral guide in any day-to-day conversation their role as a powerful source of information may extend well beyond close-up personal interactions and include other modes of communication such as written discourse or literature as well. Building on the assumption that similarities between the different ‘modes’ of voice cues may not only be limited to their functional role but may also include cerebral mechanisms engaged in the decoding process, the present functional magnetic resonance imaging study aimed at exploring brain responses associated with processing emotional voice signals described in literary texts. Emphasis was placed on evaluating ‘voice’ sensitive as well as task- and emotion-related modulations of brain activation frequently associated with the decoding of acoustic vocal cues. Obtained findings suggest that several similarities emerge with respect to the perception of acoustic voice signals: results identify the superior temporal, lateral and medial frontal cortex as well as the posterior cingulate cortex and cerebellum to contribute to the decoding process, with similarities to acoustic voice perception reflected in a ‘voice’-cue preference of temporal voice areas as well as an emotion-related modulation of the medial frontal cortex and a task-modulated response of the lateral frontal cortex. PMID:24396008

Evidence of motor-control difficulties in children with attention deficit hyperactivity disorder, explored through a hierarchical motor-systems perspective.

PubMed

Macoun, Sarah J; Kerns, Kimberly A

2016-01-01

Attention deficit hyperactivity disorder (ADHD) may reflect a disorder of neural systems that regulate motor control. The current study investigates motor dysfunction in children with ADHD using a hierarchical motor-systems perspective where frontal-striatal/"medial" brain systems are viewed as regulating parietal/"lateral" brain systems in a top down manner, to inhibit automatic environmentally driven responses in favor of goal-directed behavior. It was hypothesized that due to frontal-striatal hypoactivation, children with ADHD would have difficulty with higher order motor control tasks felt to be dependent on these systems, yet have preserved general motor function. A total of 63 children-ADHD and matched controls-completed experimental motor tasks that required maintenance of internal motor representations and the ability to inhibit visually driven responses. Children also completed a measure of motor inhibition, and a portion of the sample completed general motor function tasks. On motor tasks that required them to maintain internal motor representations and to inhibit automatic motor responses, children with ADHD had significantly greater difficulty than controls, yet on measures of general motor dexterity, their performance was comparable. Children with ADHD displayed significantly greater intraindividual (subject) variability than controls. Intraindividual variability (IIV) contributed to variations in performance across the motor tasks, but did not account for all of the variance on all tasks. These findings suggest that children with ADHD may be more controlled by external stimuli than by internally represented information, possibly due to dysfunction of the medial motor system. However, it is likely that children with ADHD also display general motor-execution problems (as evidenced by IIV findings), suggesting that atypicalities may extend to both medial and lateral motor systems. Findings are interpreted within the context of contemporary theories regarding motor dysfunction in ADHD, and implications for understanding externalizing behaviors in ADHD are discussed.

From "rest" to language task: Task activation selects and prunes from broader resting-state network.

PubMed

Doucet, Gaelle E; He, Xiaosong; Sperling, Michael R; Sharan, Ashwini; Tracy, Joseph I

2017-05-01

Resting-state networks (RSNs) show spatial patterns generally consistent with networks revealed during cognitive tasks. However, the exact degree of overlap between these networks has not been clearly quantified. Such an investigation shows promise for decoding altered functional connectivity (FC) related to abnormal language functioning in clinical populations such as temporal lobe epilepsy (TLE). In this context, we investigated the network configurations during a language task and during resting state using FC. Twenty-four healthy controls, 24 right and 24 left TLE patients completed a verb generation (VG) task and a resting-state fMRI scan. We compared the language network revealed by the VG task with three FC-based networks (seeding the left inferior frontal cortex (IFC)/Broca): two from the task (ON, OFF blocks) and one from the resting state. We found that, for both left TLE patients and controls, the RSN recruited regions bilaterally, whereas both VG-on and VG-off conditions produced more left-lateralized FC networks, matching more closely with the activated language network. TLE brings with it variability in both task-dependent and task-independent networks, reflective of atypical language organization. Overall, our findings suggest that our RSN captured bilateral activity, reflecting a set of prepotent language regions. We propose that this relationship can be best understood by the notion of pruning or winnowing down of the larger language-ready RSN to carry out specific task demands. Our data suggest that multiple types of network analyses may be needed to decode the association between language deficits and the underlying functional mechanisms altered by disease. Hum Brain Mapp 38:2540-2552, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Subthalamic nucleus stimulation selectively improves motor and visual memory performance in Parkinson's disease.

PubMed

Mollion, Hélène; Dominey, Peter Ford; Broussolle, Emmanuel; Ventre-Dominey, Jocelyne

2011-09-01

Although the treatment of Parkinson's disease via subthalamic stimulation yields remarkable improvements in motor symptoms, its effects on memory function are less clear. In this context, we previously demonstrated dissociable effects of levodopa therapy on parkinsonian performance in spatial and nonspatial visual working memory. Here we used the same protocol with an additional, purely motor task to investigate visual memory and motor performance in 2 groups of patients with Parkinson's disease with or without subthalamic stimulation. In each stimulation condition, subjects performed a simple motor task and 3 successive cognitive tasks: 1 conditional color-response association task and 2 visual (spatial and nonspatial) working memory tasks. The Parkinson's groups were compared with a control group of age-matched healthy subjects. Our principal results demonstrated that (1) in the motor task, stimulated patients were significantly improved with respect to nonstimulated patients and did not differ significantly from healthy controls, and (2) in the cognitive tasks, stimulated patients were significantly improved with respect to nonstimulated patients, but both remained significantly impaired when compared with healthy controls. These results demonstrate selective effects of subthalamic stimulation on parkinsonian disorders of motor and visual memory functions, with clear motor improvement for stimulated patients and a partial improvement for their visual memory processing. Copyright © 2011 Movement Disorder Society.

Deep learning with convolutional neural networks for EEG decoding and visualization

PubMed Central

Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

2017-01-01

Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc. PMID:28782865

Neuroprosthetic Decoder Training as Imitation Learning

PubMed Central

Merel, Josh; Paninski, Liam; Cunningham, John P.

2016-01-01

Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user’s intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user’s intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector. PMID:27191387

The influence of a real job on upper limb performance in motor skill tests: which abilities are transferred?

PubMed

Giangiardi, Vivian Farahte; Alouche, Sandra Regina; de Freitas, Sandra Maria Sbeghen Ferreira; Pires, Raquel Simoni; Padula, Rosimeire Simprini

2018-06-01

To investigate whether the specificities of real jobs create distinctions in the performance of workers in different motor tests for the upper limbs, 24 participants were divided into two groups according to their specific job: fine and repetitive tasks and general tasks. Both groups reproduced tasks related to aiming movements, handling and strength of the upper limbs. There were no significant differences between groups in the dexterity and performance of aiming movements. However, the general tasks group had higher grip strength than the repetitive tasks group, demonstrating differences according to job specificity. The results suggest that a particular motor skill in a specific job cannot improve performance in other tasks with the same motor requirements. The transfer of the fine and gross motor skills from previous experience in a job-specific task is the basis for allocating training and guidance to workers.

Movement Interferes with Visuospatial Working Memory during the Encoding: An ERP Study

PubMed Central

Gunduz Can, Rumeysa; Schack, Thomas; Koester, Dirk

2017-01-01

The present study focuses on the functional interactions of cognition and manual action control. Particularly, we investigated the neurophysiological correlates of the dual-task costs of a manual-motor task (requiring grasping an object, holding it, and subsequently placing it on a target) for working memory (WM) domains (verbal and visuospatial) and processes (encoding and retrieval). Thirty participants were tested in a cognitive-motor dual-task paradigm, in which a single block (a verbal or visuospatial WM task) was compared with a dual block (concurrent performance of a WM task and a motor task). Event-related potentials (ERPs) were analyzed separately for the encoding and retrieval processes of verbal and visuospatial WM domains both in single and dual blocks. The behavioral analyses show that the motor task interfered with WM and decreased the memory performance. The performance decrease was larger for the visuospatial task compared with the verbal task, i.e., domain-specific memory costs were obtained. The ERP analyses show the domain-specific interference also at the neurophysiological level, which is further process-specific to encoding. That is, comparing the patterns of WM-related ERPs in the single block and dual block, we showed that visuospatial ERPs changed only for the encoding process when a motor task was performed at the same time. Generally, the present study provides evidence for domain- and process-specific interactions of a prepared manual-motor movement with WM (visuospatial domain during the encoding process). This study, therefore, provides an initial neurophysiological characterization of functional interactions of WM and manual actions in a cognitive-motor dual-task setting, and contributes to a better understanding of the neuro-cognitive mechanisms of motor action control. PMID:28611714

Dual Motor-Cognitive Virtual Reality Training Impacts Dual-Task Performance in Freezing of Gait.

PubMed

Killane, Isabelle; Fearon, Conor; Newman, Louise; McDonnell, Conor; Waechter, Saskia M; Sons, Kristian; Lynch, Timothy; Reilly, Richard B

2015-11-01

Freezing of gait (FOG), an episodic gait disturbance characterized by the inability to generate effective stepping, occurs in more than half of Parkinson's disease patients. It is associated with both executive dysfunction and attention and becomes most evident during dual tasking (performing two tasks simultaneously). This study examined the effect of dual motor-cognitive virtual reality training on dual-task performance in FOG. Twenty community dwelling participants with Parkinson's disease (13 with FOG, 7 without FOG) participated in a pre-assessment, eight 20-minute intervention sessions, and a post-assessment. The intervention consisted of a virtual reality maze (DFKI, Germany) through which participants navigated by stepping-in-place on a balance board (Nintendo, Japan) under time pressure. This was combined with a cognitive task (Stroop test), which repeatedly divided participants' attention. The primary outcome measures were pre- and post-intervention differences in motor (stepping time, symmetry, rhythmicity) and cognitive (accuracy, reaction time) performance during single- and dual-tasks. Both assessments consisted of 1) a single cognitive task 2) a single motor task, and 3) a dual motor-cognitive task. Following the intervention, there was significant improvement in dual-task cognitive and motor parameters (stepping time and rhythmicity), dual-task effect for those with FOG and a noteworthy improvement in FOG episodes. These improvements were less significant for those without FOG. This is the first study to show benefit of a dual motor-cognitive approach on dual-task performance in FOG. Advances in such virtual reality interventions for home use could substantially improve the quality of life for patients who experience FOG.

Motor-cognitive dual-task deficits in individuals with early-mid stage Huntington disease.

PubMed

Fritz, Nora E; Hamana, Katy; Kelson, Mark; Rosser, Anne; Busse, Monica; Quinn, Lori

2016-09-01

Huntington disease (HD) results in a range of cognitive and motor impairments that progress throughout the disease stages; however, little research has evaluated specific dual-task abilities in this population, and the degree to which they may be related to functional ability. The purpose of this study was to a) examine simple and complex motor-cognitive dual-task performance in individuals with HD, b) determine relationships between dual-task walking ability and disease-specific measures of motor, cognitive and functional ability, and c) examine the relationship of dual-task measures to falls in individuals with HD. Thirty-two individuals with HD were evaluated for simple and complex dual-task ability using the Walking While Talking Test. Demographics and disease-specific measures of motor, cognitive and functional ability were also obtained. Individuals with HD had impairments in simple and complex dual-task ability. Simple dual-task walking was correlated to disease-specific motor scores as well as cognitive performance, but complex dual-task walking was correlated with total functional capacity, as well as a range of cognitive measures. Number of prospective falls was moderately-strongly correlated to dual-task measures. Our results suggest that individuals with HD have impairments in cognitive-motor dual-task ability that are related to disease progression and specifically functional ability. Dual-task measures appear to evaluate a unique construct in individuals with early to mid-stage HD, and may have value in improving the prediction of falls risk in this population. Copyright © 2016 Elsevier B.V. All rights reserved.

Sleep benefits consolidation of visuo-motor adaptation learning in older adults.

PubMed

Mantua, Janna; Baran, Bengi; Spencer, Rebecca M C

2016-02-01

Sleep is beneficial for performance across a range of memory tasks in young adults, but whether memories are similarly consolidated in older adults is less clear. Performance benefits have been observed following sleep in older adults for declarative learning tasks, but this benefit may be reduced for non-declarative, motor skill learning tasks. To date, studies of sleep-dependent consolidation of motor learning in older adults are limited to motor sequence tasks. To examine whether reduced sleep-dependent consolidation in older adults is generalizable to other forms of motor skill learning, we examined performance changes over intervals of sleep and wake in young (n = 62) and older adults (n = 61) using a mirror-tracing task, which assesses visuo-motor adaptation learning. Participants learned the task either in the morning or in evening, and performance was assessed following a 12-h interval containing overnight sleep or daytime wake. Contrary to our prediction, both young adults and older adults exhibited sleep-dependent gains in visuo-motor adaptation. There was a correlation between performance improvement over sleep and percent of the night in non-REM stage 2 sleep. These results indicate that motor skill consolidation remains intact with increasing age although this relationship may be limited to specific forms of motor skill learning.

On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.

PubMed

Xu, Zhiming; So, Rosa Q; Toe, Kyaw Kyar; Ang, Kai Keng; Guan, Cuntai

2014-01-01

This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subject's self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.

Volumetric Analysis of Regional Variability in the Cerebellum of Children with Dyslexia

PubMed Central

Stuebing, Karla; Juranek, Jenifer; Fletcher, Jack M.

2013-01-01

Cerebellar deficits and subsequent impairment in procedural learning may contribute to both motor difficulties and reading impairment in dyslexia. We used quantitative magnetic resonance imaging to investigate the role of regional variation in cerebellar anatomy in children with single-word decoding impairments (N=23), children with impairment in fluency alone (N=8), and typically developing children (N=16). Children with decoding impairments (dyslexia) demonstrated no statistically significant differences in overall grey and white matter volumes or cerebellar asymmetry; however, reduced volume in the anterior lobe of the cerebellum relative to typically developing children was observed. These results implicate cerebellar involvement in dyslexia and establish an important foundation for future research on the connectivity of the cerebellum and cortical regions typically associated with reading impairment. PMID:23828023

Volumetric analysis of regional variability in the cerebellum of children with dyslexia.

PubMed

Fernandez, Vindia G; Stuebing, Karla; Juranek, Jenifer; Fletcher, Jack M

2013-12-01

Cerebellar deficits and subsequent impairment in procedural learning may contribute to both motor difficulties and reading impairment in dyslexia. We used quantitative magnetic resonance imaging to investigate the role of regional variation in cerebellar anatomy in children with single-word decoding impairments (N = 23), children with impairment in fluency alone (N = 8), and typically developing children (N = 16). Children with decoding impairments (dyslexia) demonstrated no statistically significant differences in overall grey and white matter volumes or cerebellar asymmetry; however, reduced volume in the anterior lobe of the cerebellum relative to typically developing children was observed. These results implicate cerebellar involvement in dyslexia and establish an important foundation for future research on the connectivity of the cerebellum and cortical regions typically associated with reading impairment.

An integrated approach to improving noisy speech perception

NASA Astrophysics Data System (ADS)

Koval, Serguei; Stolbov, Mikhail; Smirnova, Natalia; Khitrov, Mikhail

2002-05-01

For a number of practical purposes and tasks, experts have to decode speech recordings of very poor quality. A combination of techniques is proposed to improve intelligibility and quality of distorted speech messages and thus facilitate their comprehension. Along with the application of noise cancellation and speech signal enhancement techniques removing and/or reducing various kinds of distortions and interference (primarily unmasking and normalization in time and frequency fields), the approach incorporates optimal listener expert tactics based on selective listening, nonstandard binaural listening, accounting for short-term and long-term human ear adaptation to noisy speech, as well as some methods of speech signal enhancement to support speech decoding during listening. The approach integrating the suggested techniques ensures high-quality ultimate results and has successfully been applied by Speech Technology Center experts and by numerous other users, mainly forensic institutions, to perform noisy speech records decoding for courts, law enforcement and emergency services, accident investigation bodies, etc.

Development of closed-loop neural interface technology in a rat model: combining motor cortex operant conditioning with visual cortex microstimulation.

PubMed

Marzullo, Timothy Charles; Lehmkuhle, Mark J; Gage, Gregory J; Kipke, Daryl R

2010-04-01

Closed-loop neural interface technology that combines neural ensemble decoding with simultaneous electrical microstimulation feedback is hypothesized to improve deep brain stimulation techniques, neuromotor prosthetic applications, and epilepsy treatment. Here we describe our iterative results in a rat model of a sensory and motor neurophysiological feedback control system. Three rats were chronically implanted with microelectrode arrays in both the motor and visual cortices. The rats were subsequently trained over a period of weeks to modulate their motor cortex ensemble unit activity upon delivery of intra-cortical microstimulation (ICMS) of the visual cortex in order to receive a food reward. Rats were given continuous feedback via visual cortex ICMS during the response periods that was representative of the motor cortex ensemble dynamics. Analysis revealed that the feedback provided the animals with indicators of the behavioral trials. At the hardware level, this preparation provides a tractable test model for improving the technology of closed-loop neural devices.

A scalable population code for time in the striatum.

PubMed

Mello, Gustavo B M; Soares, Sofia; Paton, Joseph J

2015-05-04

To guide behavior and learn from its consequences, the brain must represent time over many scales. Yet, the neural signals used to encode time in the seconds-to-minute range are not known. The striatum is a major input area of the basal ganglia associated with learning and motor function. Previous studies have also shown that the striatum is necessary for normal timing behavior. To address how striatal signals might be involved in timing, we recorded from striatal neurons in rats performing an interval timing task. We found that neurons fired at delays spanning tens of seconds and that this pattern of responding reflected the interaction between time and the animals' ongoing sensorimotor state. Surprisingly, cells rescaled responses in time when intervals changed, indicating that striatal populations encoded relative time. Moreover, time estimates decoded from activity predicted timing behavior as animals adjusted to new intervals, and disrupting striatal function led to a decrease in timing performance. These results suggest that striatal activity forms a scalable population code for time, providing timing signals that animals use to guide their actions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Decoding of intended saccade direction in an oculomotor brain-computer interface

NASA Astrophysics Data System (ADS)

Jia, Nan; Brincat, Scott L.; Salazar-Gómez, Andrés F.; Panko, Mikhail; Guenther, Frank H.; Miller, Earl K.

2017-08-01

Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from the hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for ‘point and click’ computer operation as used in most current AAC systems.

Decoding Grasping Movements from the Parieto-Frontal Reaching Circuit in the Nonhuman Primate.

PubMed

Nelissen, Koen; Fiave, Prosper Agbesi; Vanduffel, Wim

2018-04-01

Prehension movements typically include a reaching phase, guiding the hand toward the object, and a grip phase, shaping the hand around it. The dominant view posits that these components rely upon largely independent parieto-frontal circuits: a dorso-medial circuit involved in reaching and a dorso-lateral circuit involved in grasping. However, mounting evidence suggests a more complex arrangement, with dorso-medial areas contributing to both reaching and grasping. To investigate the role of the dorso-medial reaching circuit in grasping, we trained monkeys to reach-and-grasp different objects in the dark and determined if hand configurations could be decoded from functional magnetic resonance imaging (MRI) responses obtained from the reaching and grasping circuits. Indicative of their established role in grasping, object-specific grasp decoding was found in anterior intraparietal (AIP) area, inferior parietal lobule area PFG and ventral premotor region F5 of the lateral grasping circuit, and primary motor cortex. Importantly, the medial reaching circuit also conveyed robust grasp-specific information, as evidenced by significant decoding in parietal reach regions (particular V6A) and dorsal premotor region F2. These data support the proposed role of dorso-medial "reach" regions in controlling aspects of grasping and demonstrate the value of complementing univariate with more sensitive multivariate analyses of functional MRI (fMRI) data in uncovering information coding in the brain.

Avionics Simulation, Development and Software Engineering

NASA Technical Reports Server (NTRS)

Francis, Ronald C.; Settle, Gray; Tobbe, Patrick A.; Kissel, Ralph; Glaese, John; Blanche, Jim; Wallace, L. D.

2001-01-01

This monthly report summarizes the work performed under contract NAS8-00114 for Marshall Space Flight Center in the following tasks: 1) Purchase Order No. H-32831D, Task Order 001A, GPB Program Software Oversight; 2) Purchase Order No. H-32832D, Task Order 002, ISS EXPRESS Racks Software Support; 3) Purchase Order No. H-32833D, Task Order 003, SSRMS Math Model Integration; 4) Purchase Order No. H-32834D, Task Order 004, GPB Program Hardware Oversight; 5) Purchase Order No. H-32835D, Task Order 005, Electrodynamic Tether Operations and Control Analysis; 6) Purchase Order No. H-32837D, Task Order 007, SRB Command Receiver/Decoder; and 7) Purchase Order No. H-32838D, Task Order 008, AVGS/DART SW and Simulation Support

Hand digit control in children: motor overflow in multi-finger pressing force vector space during maximum voluntary force production.

PubMed

Shim, Jae Kun; Karol, Sohit; Hsu, Jeffrey; de Oliveira, Marcio Alves

2008-04-01

The aim of this study was to investigate the contralateral motor overflow in children during single-finger and multi-finger maximum force production tasks. Forty-five right handed children, 5-11 years of age produced maximum isometric pressing force in flexion or extension with single fingers or all four fingers of their right hand. The forces produced by individual fingers of the right and left hands were recorded and analyzed in four-dimensional finger force vector space. The results showed that increases in task (right) hand finger forces were linearly associated with non-task (left) hand finger forces. The ratio of the non-task hand finger force magnitude to the corresponding task hand finger force magnitude, termed motor overflow magnitude (MOM), was greater in extension than flexion. The index finger flexion task showed the smallest MOM values. The similarity between the directions of task hand and non-task hand finger force vectors in four-dimensional finger force vector space, termed motor overflow direction (MOD), was the greatest for index and smallest for little finger tasks. MOM of a four-finger task was greater than the sum of MOMs of single-finger tasks, and this phenomenon was termed motor overflow surplus. Contrary to previous studies, no single-finger or four-finger tasks showed significant changes of MOM or MOD with the age of children. We conclude that the contralateral motor overflow in children during finger maximum force production tasks is dependent upon the task fingers and the magnitude and direction of task finger forces.

Temporal Integration Windows in Neural Processing and Perception Aligned to Saccadic Eye Movements.

PubMed

Wutz, Andreas; Muschter, Evelyn; van Koningsbruggen, Martijn G; Weisz, Nathan; Melcher, David

2016-07-11

When processing dynamic input, the brain balances the opposing needs of temporal integration and sensitivity to change. We hypothesized that the visual system might resolve this challenge by aligning integration windows to the onset of newly arriving sensory samples. In a series of experiments, human participants observed the same sequence of two displays separated by a brief blank delay when performing either an integration or segregation task. First, using magneto-encephalography (MEG), we found a shift in the stimulus-evoked time courses by a 150-ms time window between task signals. After stimulus onset, multivariate pattern analysis (MVPA) decoding of task in occipital-parietal sources remained above chance for almost 1 s, and the task-decoding pattern interacted with task outcome. In the pre-stimulus period, the oscillatory phase in the theta frequency band was informative about both task processing and behavioral outcome for each task separately, suggesting that the post-stimulus effects were caused by a theta-band phase shift. Second, when aligning stimulus presentation to the onset of eye fixations, there was a similar phase shift in behavioral performance according to task demands. In both MEG and behavioral measures, task processing was optimal first for segregation and then integration, with opposite phase in the theta frequency range (3-5 Hz). The best fit to neurophysiological and behavioral data was given by a dampened 3-Hz oscillation from stimulus or eye fixation onset. The alignment of temporal integration windows to input changes found here may serve to actively organize the temporal processing of continuous sensory input. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Motor cortex is required for learning but not executing a motor skill

PubMed Central

Kawai, Risa; Markman, Timothy; Poddar, Rajesh; Ko, Raymond; Fantana, Antoniu; Dhawale, Ashesh; Kampff, Adam R.; Ölveczky, Bence P.

2018-01-01

Motor cortex is widely believed to underlie the acquisition and execution of motor skills, yet its contributions to these processes are not fully understood. One reason is that studies on motor skills often conflate motor cortex’s established role in dexterous control with roles in learning and producing task-specific motor sequences. To dissociate these aspects, we developed a motor task for rats that trains spatiotemporally precise movement patterns without requirements for dexterity. Remarkably, motor cortex lesions had no discernible effect on the acquired skills, which were expressed in their distinct pre-lesion forms on the very first day of post-lesion training. Motor cortex lesions prior to training, however, rendered rats unable to acquire the stereotyped motor sequences required for the task. These results suggest a remarkable capacity of subcortical motor circuits to execute learned skills and a previously unappreciated role for motor cortex in ‘tutoring’ these circuits during learning. PMID:25892304

Low speed phaselock speed control system. [for brushless dc motor

NASA Technical Reports Server (NTRS)

Fulcher, R. W.; Sudey, J. (Inventor)

1975-01-01

A motor speed control system for an electronically commutated brushless dc motor is provided which includes a phaselock loop with bidirectional torque control for locking the frequency output of a high density encoder, responsive to actual speed conditions, to a reference frequency signal, corresponding to the desired speed. The system includes a phase comparator, which produces an output in accordance with the difference in phase between the reference and encoder frequency signals, and an integrator-digital-to-analog converter unit, which converts the comparator output into an analog error signal voltage. Compensation circuitry, including a biasing means, is provided to convert the analog error signal voltage to a bidirectional error signal voltage which is utilized by an absolute value amplifier, rotational decoder, power amplifier-commutators, and an arrangement of commutation circuitry.

Task-specificity of unilateral anodal and dual-M1 tDCS effects on motor learning.

PubMed

Karok, Sophia; Fletcher, David; Witney, Alice G

2017-01-08

Task-specific effects of transcranial direct current stimulation (tDCS) on motor learning were investigated in 30 healthy participants. In a sham-controlled, mixed design, participants trained on 3 different motor tasks (Purdue Pegboard Test, Visuomotor Grip Force Tracking Task and Visuomotor Wrist Rotation Speed Control Task) over 3 consecutive days while receiving either unilateral anodal over the right primary motor cortex (M1), dual-M1 or sham stimulation. Retention sessions were administered 7 and 28 days after the end of training. In the Purdue Pegboard Test, both anodal and dual-M1 stimulation reduced average completion time approximately equally, an improvement driven by online learning effects and maintained for about 1 week. The Visuomotor Grip Force Tracking Task and the Visuomotor Wrist Rotation Speed Control Task were associated with an advantage of dual-M1 tDCS in consolidation processes both between training sessions and when testing at long-term retention; both were maintained for at least 1 month. This study demonstrates that M1-tDCS enhances and sustains motor learning with different electrode montages. Stimulation-induced effects emerged at different learning phases across the tasks, which strongly suggests that the influence of tDCS on motor learning is dynamic with respect to the functional recruitment of the distributed motor system at the time of stimulation. Divergent findings regarding M1-tDCS effects on motor learning may partially be ascribed to task-specific consequences and the effects of offline consolidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Motor command inhibition and the representation of response mode during motor imagery.

PubMed

Scheil, Juliane; Liefooghe, Baptist

2018-05-01

Research on motor imagery proposes that overt actions during motor imagery can be avoided by proactively signaling subthreshold motor commands to the effectors and by invoking motor-command inhibition. A recent study by Rieger, Dahm, and Koch (2017) found evidence in support of motor command inhibition, which indicates that MI cannot be completed on the sole basis of subthreshold motor commands. However, during motor imagery, participants know in advance when a covert response is to be made and it is thus surprising such additional motor-command inhibition is needed. Accordingly, the present study tested whether the demand to perform an action covertly can be proactively integrated by investigating the formation of task-specific action rules during motor imagery. These task-specific action rules relate the decision rules of a task to the mode in which these rules need to be applied (e.g., if smaller than 5, press the left key covertly). To this end, an experiment was designed in which participants had to switch between two numerical judgement tasks and two response modes: covert responding and overt responding. First, we observed markers of motor command inhibition and replicated the findings of Rieger and colleagues. Second, we observed evidence suggesting that task-specific action rules are created for the overt response mode (e.g., if smaller than 5, press the left key). In contrast, for the covert response mode, no task-specific action rules are formed and decision rules do not include mode-specific information (e.g., if smaller than 5, left). Copyright © 2018 Elsevier B.V. All rights reserved.

Adjustments differ among low-threshold motor units during intermittent, isometric contractions.

PubMed

Farina, Dario; Holobar, Ales; Gazzoni, Marco; Zazula, Damjan; Merletti, Roberto; Enoka, Roger M

2009-01-01

We investigated the changes in muscle fiber conduction velocity, recruitment and derecruitment thresholds, and discharge rate of low-threshold motor units during a series of ramp contractions. The aim was to compare the adjustments in motor unit activity relative to the duration that each motor unit was active during the task. Multichannel surface electromyographic (EMG) signals were recorded from the abductor pollicis brevis muscle of eight healthy men during 12-s contractions (n = 25) in which the force increased and decreased linearly from 0 to 10% of the maximum. The maximal force exhibited a modest decline (8.5 +/- 9.3%; P < 0.05) at the end of the task. The discharge times of 73 motor units that were active for 16-98% of the time during the first five contractions were identified throughout the task by decomposition of the EMG signals. Action potential conduction velocity decreased during the task by a greater amount for motor units that were initially active for >70% of the time compared with that of less active motor units. Moreover, recruitment and derecruitment thresholds increased for these most active motor units, whereas the thresholds decreased for the less active motor units. Another 18 motor units were recruited at an average of 171 +/- 32 s after the beginning of the task. The recruitment and derecruitment thresholds of these units decreased during the task, but muscle fiber conduction velocity did not change. These results indicate that low-threshold motor units exhibit individual adjustments in muscle fiber conduction velocity and motor neuron activation that depended on the relative duration of activity during intermittent contractions.

Effects of tDCS on Bimanual Motor Skills: A Brief Review

PubMed Central

Pixa, Nils H.; Pollok, Bettina

2018-01-01

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that allows the modulation of cortical excitability as well as neuroplastic reorganization using a weak constant current applied through the skull on the cerebral cortex. TDCS has been found to improve motor performance in general and motor learning in particular. However, these effects have been reported almost exclusively for unimanual motor tasks such as serial reaction time tasks, adaptation tasks, or visuo-motor tracking. Despite the importance of bimanual actions in most activities of daily living, only few studies have investigated the effects of tDCS on bimanual motor skills. The objectives of this review article are: (i) to provide a concise overview of the few existing studies in this area; and (ii) to discuss the effects of tDCS on bimanual motor skills in healthy volunteers and patients suffering from neurological diseases. Despite considerable variations in stimulation protocols, the bimanual tasks employed, and study designs, the data suggest that tDCS has the potential to enhance bimanual motor skills. The findings imply that the effects of tDCS vary with task demands, such as complexity and the level of expertise of the participating volunteers. Nevertheless, optimized stimulation protocols tailored to bimanual tasks and individual performance considering the underlying neural substrates of task execution are required in order to probe the effectiveness of tDCS in greater detail, thus creating an opportunity to support motor recovery in neuro-rehabilitation. PMID:29670514

Effects of tDCS on Bimanual Motor Skills: A Brief Review.

PubMed

Pixa, Nils H; Pollok, Bettina

2018-01-01

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that allows the modulation of cortical excitability as well as neuroplastic reorganization using a weak constant current applied through the skull on the cerebral cortex. TDCS has been found to improve motor performance in general and motor learning in particular. However, these effects have been reported almost exclusively for unimanual motor tasks such as serial reaction time tasks, adaptation tasks, or visuo-motor tracking. Despite the importance of bimanual actions in most activities of daily living, only few studies have investigated the effects of tDCS on bimanual motor skills. The objectives of this review article are: (i) to provide a concise overview of the few existing studies in this area; and (ii) to discuss the effects of tDCS on bimanual motor skills in healthy volunteers and patients suffering from neurological diseases. Despite considerable variations in stimulation protocols, the bimanual tasks employed, and study designs, the data suggest that tDCS has the potential to enhance bimanual motor skills. The findings imply that the effects of tDCS vary with task demands, such as complexity and the level of expertise of the participating volunteers. Nevertheless, optimized stimulation protocols tailored to bimanual tasks and individual performance considering the underlying neural substrates of task execution are required in order to probe the effectiveness of tDCS in greater detail, thus creating an opportunity to support motor recovery in neuro-rehabilitation.

cTBS disruption of the supplementary motor area perturbs cortical sequence representation but not behavioural performance.

PubMed

Solopchuk, Oleg; Alamia, Andrea; Dricot, Laurence; Duque, Julie; Zénon, Alexandre

2017-12-01

Neuroimaging studies have repeatedly emphasized the role of the supplementary motor area (SMA) in motor sequence learning, but interferential approaches have led to inconsistent findings. Here, we aimed to test the role of the SMA in motor skill learning by combining interferential and neuroimaging techniques. Sixteen subjects were trained on simple finger movement sequences for 4 days. Afterwards, they underwent two neuroimaging sessions, in which they executed both trained and novel sequences. Prior to entering the scanner, the subjects received inhibitory transcranial magnetic stimulation (TMS) over the SMA or a control site. Using multivariate fMRI analysis, we confirmed that motor training enhances the neural representation of motor sequences in the SMA, in accordance with previous findings. However, although SMA inhibition altered sequence representation (i.e. between-sequence decoding accuracy) in this area, behavioural performance remained unimpaired. Our findings question the causal link between the neuroimaging correlate of elementary motor sequence representation in the SMA and sequence generation, calling for a more thorough investigation of the role of this region in performance of learned motor sequences. Copyright © 2017 Elsevier Inc. All rights reserved.

Adaptation and Retention of a Perceptual-Motor Task in Children: Effects of a Single Bout of Intense Endurance Exercise.

PubMed

Ferrer-Uris, Blai; Busquets, Albert; Angulo-Barroso, Rosa

2018-02-01

We assessed the effect of an acute intense exercise bout on the adaptation and consolidation of a visuomotor adaptation task in children. We also sought to assess if exercise and learning task presentation order could affect task consolidation. Thirty-three children were randomly assigned to one of three groups: (a) exercise before the learning task, (b) exercise after the learning task, and (c) only learning task. Baseline performance was assessed by practicing the learning task in a 0° rotation condition. Afterward, a 60° rotation-adaptation set was applied followed by three rotated retention sets after 1 hr, 24 hr, and 7 days. For the exercise groups, exercise was presented before or after the motor adaptation. Results showed no group differences during the motor adaptation while exercise seemed to enhance motor consolidation. Greater consolidation enhancement was found in participants who exercised before the learning task. Our data support the importance of exercise to improve motor-memory consolidation in children.

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Upper limb motor function in young adults with spina bifida and hydrocephalus

PubMed Central

Salman, M. S.; Jewell, D.; Hetherington, R.; Spiegler, B. J.; MacGregor, D. L.; Drake, J. M.; Humphreys, R. P.; Gentili, F.

2011-01-01

Objective The objective of the study was to measure upper limb motor function in young adults with spina bifida meningomyelocele (SBM) and typically developing age peers. Method Participants were 26 young adults with SBM, with a Verbal or Performance IQ score of at least 70 on the Wechsler scales, and 27 age- and gender-matched controls. Four upper limb motor function tasks were performed under four different visual and cognitive challenge conditions. Motor independence was assessed by questionnaire. Results Fewer SBM than control participants obtained perfect posture and rebound scores. The SBM group performed less accurately and was more disrupted by cognitive challenge than controls on limb dysmetria tasks. The SBM group was slower than controls on the diadochokinesis task. Adaptive motor independence was related to one upper limb motor task, arm posture, and upper rather than lower spinal lesions were associated with less motor independence. Conclusions Young adults with SBM have significant limitations in upper limb function and are more disrupted by some challenges while performing upper limb motor tasks. Within the group of young adults with SBM, upper spinal lesions compromise motor independence more than lower spinal lesions. PMID:19672605

Self-paced brain-computer interface control of ambulation in a virtual reality environment.

PubMed

Wang, Po T; King, Christine E; Chui, Luis A; Do, An H; Nenadic, Zoran

2012-10-01

Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogram (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over five sessions, in which they utilized the KMI to control the linear ambulation of an avatar and make ten sequential stops at designated points within the VRE. The average offline training performance across subjects was 77.2 ± 11.0%, ranging from 64.3% (p = 0.001 76) to 94.5% (p = 6.26 × 10(-23)), with chance performance being 50%. The average online performance was 8.5 ± 1.1 (out of 10) successful stops and 303 ± 53 s completion time (perfect = 211 s). All subjects achieved performances significantly different than those of random walk (p < 0.05) in 44 of the 45 online sessions. By using a data-driven machine learning approach to decode users' KMI, this BCI-VRE system enabled intuitive and purposeful self-paced control of ambulation after only 10 minutes training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.

Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.

PubMed

Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

2016-09-01

In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

A rodent brain-machine interface paradigm to study the impact of paraplegia on BMI performance.

PubMed

Bridges, Nathaniel R; Meyers, Michael; Garcia, Jonathan; Shewokis, Patricia A; Moxon, Karen A

2018-05-31

Most brain machine interfaces (BMI) focus on upper body function in non-injured animals, not addressing the lower limb functional needs of those with paraplegia. A need exists for a novel BMI task that engages the lower body and takes advantage of well-established rodent spinal cord injury (SCI) models to study methods to improve BMI performance. A tilt BMI task was designed that randomly applies different types of tilts to a platform, decodes the tilt type applied and rights the platform if the decoder correctly classifies the tilt type. The task was tested on female rats and is relatively natural such that it does not require the animal to learn a new skill. It is self-rewarding such that there is no need for additional rewards, eliminating food or water restriction, which can be especially hard on spinalized rats. Finally, task difficulty can be adjusted by making the tilt parameters. This novel BMI task bilaterally engages the cortex without visual feedback regarding limb position in space and animals learn to improve their performance both pre and post-SCI.Comparison with Existing Methods: Most BMI tasks primarily engage one hemisphere, are upper-body, rely heavily on visual feedback, do not perform investigations in animal models of SCI, and require nonnaturalistic extrinsic motivation such as water rewarding for performance improvement. Our task addresses these gaps. The BMI paradigm presented here will enable researchers to investigate the interaction of plasticity after SCI and plasticity during BMI training on performance. Copyright © 2018. Published by Elsevier B.V.

Task-Dependent Intermuscular Motor Unit Synchronization between Medial and Lateral Vastii Muscles during Dynamic and Isometric Squats.

PubMed

Mohr, Maurice; Nann, Marius; von Tscharner, Vinzenz; Eskofier, Bjoern; Nigg, Benno Maurus

2015-01-01

Motor unit activity is coordinated between many synergistic muscle pairs but the functional role of this coordination for the motor output is unclear. The purpose of this study was to investigate the short-term modality of coordinated motor unit activity-the synchronized discharge of individual motor units across muscles within time intervals of 5ms-for the Vastus Medialis (VM) and Lateralis (VL). Furthermore, we studied the task-dependency of intermuscular motor unit synchronization between VM and VL during static and dynamic squatting tasks to provide insight into its functional role. Sixteen healthy male and female participants completed four tasks: Bipedal squats, single-leg squats, an isometric squat, and single-leg balance. Monopolar surface electromyography (EMG) was used to record motor unit activity of VM and VL. For each task, intermuscular motor unit synchronization was determined using a coherence analysis between the raw EMG signals of VM and VL and compared to a reference coherence calculated from two desynchronized EMG signals. The time shift between VM and VL EMG signals was estimated according to the slope of the coherence phase angle spectrum. For all tasks, except for singe-leg balance, coherence between 15-80Hz significantly exceeded the reference. The corresponding time shift between VM and VL was estimated as 4ms. Coherence between 30-60Hz was highest for the bipedal squat, followed by the single-leg squat and the isometric squat. There is substantial short-term motor unit synchronization between VM and VL. Intermuscular motor unit synchronization is enhanced for contractions during dynamic activities, possibly to facilitate a more accurate control of the joint torque, and reduced during single-leg tasks that require balance control and thus, a more independent muscle function. It is proposed that the central nervous system scales the degree of intermuscular motor unit synchronization according to the requirements of the movement task at hand.

Task-Dependent Intermuscular Motor Unit Synchronization between Medial and Lateral Vastii Muscles during Dynamic and Isometric Squats

PubMed Central

Mohr, Maurice; Nann, Marius; von Tscharner, Vinzenz; Eskofier, Bjoern; Nigg, Benno Maurus

2015-01-01

Purpose Motor unit activity is coordinated between many synergistic muscle pairs but the functional role of this coordination for the motor output is unclear. The purpose of this study was to investigate the short-term modality of coordinated motor unit activity–the synchronized discharge of individual motor units across muscles within time intervals of 5ms–for the Vastus Medialis (VM) and Lateralis (VL). Furthermore, we studied the task-dependency of intermuscular motor unit synchronization between VM and VL during static and dynamic squatting tasks to provide insight into its functional role. Methods Sixteen healthy male and female participants completed four tasks: Bipedal squats, single-leg squats, an isometric squat, and single-leg balance. Monopolar surface electromyography (EMG) was used to record motor unit activity of VM and VL. For each task, intermuscular motor unit synchronization was determined using a coherence analysis between the raw EMG signals of VM and VL and compared to a reference coherence calculated from two desynchronized EMG signals. The time shift between VM and VL EMG signals was estimated according to the slope of the coherence phase angle spectrum. Results For all tasks, except for singe-leg balance, coherence between 15–80Hz significantly exceeded the reference. The corresponding time shift between VM and VL was estimated as 4ms. Coherence between 30–60Hz was highest for the bipedal squat, followed by the single-leg squat and the isometric squat. Conclusion There is substantial short-term motor unit synchronization between VM and VL. Intermuscular motor unit synchronization is enhanced for contractions during dynamic activities, possibly to facilitate a more accurate control of the joint torque, and reduced during single-leg tasks that require balance control and thus, a more independent muscle function. It is proposed that the central nervous system scales the degree of intermuscular motor unit synchronization according to the requirements of the movement task at hand. PMID:26529604

Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMER

PubMed Central

2014-01-01

Background HMMER is a commonly used bioinformatics tool based on Hidden Markov Models (HMMs) to analyze and process biological sequences. One of its main homology engines is based on the Viterbi decoding algorithm, which was already highly parallelized and optimized using Farrar’s striped processing pattern with Intel SSE2 instruction set extension. Results A new SIMD vectorization of the Viterbi decoding algorithm is proposed, based on an SSE2 inter-task parallelization approach similar to the DNA alignment algorithm proposed by Rognes. Besides this alternative vectorization scheme, the proposed implementation also introduces a new partitioning of the Markov model that allows a significantly more efficient exploitation of the cache locality. Such optimization, together with an improved loading of the emission scores, allows the achievement of a constant processing throughput, regardless of the innermost-cache size and of the dimension of the considered model. Conclusions The proposed optimized vectorization of the Viterbi decoding algorithm was extensively evaluated and compared with the HMMER3 decoder to process DNA and protein datasets, proving to be a rather competitive alternative implementation. Being always faster than the already highly optimized ViterbiFilter implementation of HMMER3, the proposed Cache-Oblivious Parallel SIMD Viterbi (COPS) implementation provides a constant throughput and offers a processing speedup as high as two times faster, depending on the model’s size. PMID:24884826

Impact of age, sex, socioeconomic status, and physical activity on associated movements and motor speed in preschool children.

PubMed

Kakebeeke, Tanja H; Zysset, Annina E; Messerli-Bürgy, Nadine; Chaouch, Aziz; Stülb, Kerstin; Leeger-Aschmann, Claudia S; Schmutz, Einat A; Arhab, Amar; Rousson, Valentin; Kriemler, Susi; Munsch, Simone; Puder, Jardena J; Jenni, Oskar G

2018-02-01

Young children generally show contralateral associated movements (CAMs) when they are making an effort to perform a unimanual task. CAM and motor speed are two relevant aspects of motor proficiency in young children. These CAMs decrease over age, while motor speed increases. As both CAM and motor speed are associated with age, we were interested in whether these two parameters are also linked with each other. In this study, three manual dexterity tasks with the dominant and nondominant hands (pegboard, repetitive hand, and repetitive finger tasks) were used to investigate the effect of covariates (age, sex, socioeconomic status, total physical activity) on both motor speed and CAMs in preschool children. There was a significant age effect for both motor speed and CAMs in all tasks when the dominant hand was used. When the nondominant hand was used, the decrease in the intensity of CAMs over age was not consistently significant. The influence of physical activity and socioeconomic status on motor proficiency was small. Furthermore, the correlation between motor speed and CAMs, although significant, was low. Motor speed improved with age over three fine motor tasks in preschool children. Decrease in CAMs was observed but it was not always significant when the nondominant hand was working. Motor speed and CAMs were only weakly associated. We conclude that the excitatory pathways responsible for motor speed and inhibitory pathways responsible for reducing CAMs occupy two different domains in the brain and therefore mostly behave independently of each other.

Motor unit recruitment for dynamic tasks: current understanding and future directions.

PubMed

Hodson-Tole, Emma F; Wakeling, James M

2009-01-01

Skeletal muscle contains many muscle fibres that are functionally grouped into motor units. For any motor task there are many possible combinations of motor units that could be recruited and it has been proposed that a simple rule, the 'size principle', governs the selection of motor units recruited for different contractions. Motor units can be characterised by their different contractile, energetic and fatigue properties and it is important that the selection of motor units recruited for given movements allows units with the appropriate properties to be activated. Here we review what is currently understood about motor unit recruitment patterns, and assess how different recruitment patterns are more or less appropriate for different movement tasks. During natural movements the motor unit recruitment patterns vary (not always holding to the size principle) and it is proposed that motor unit recruitment is likely related to the mechanical function of the muscles. Many factors such as mechanics, sensory feedback, and central control influence recruitment patterns and consequently an integrative approach (rather than reductionist) is required to understand how recruitment is controlled during different movement tasks. Currently, the best way to achieve this is through in vivo studies that relate recruitment to mechanics and behaviour. Various methods for determining motor unit recruitment patterns are discussed, in particular the recent wavelet-analysis approaches that have allowed motor unit recruitment to be assessed during natural movements. Directions for future studies into motor recruitment within and between functional task groups and muscle compartments are suggested.

Supplementary motor area activation in patients with frontal lobe tumors and arteriovenous malformations.

PubMed

Sailor, Janet; Meyerand, M Elizabeth; Moritz, Chad H; Fine, Jason; Nelson, Lindsey; Badie, Behnam; Haughton, Victor M

2003-10-01

Some patients who undergo surgical resection of portions of the supplementary motor area (SMA) have severe postoperative motor and language deficits, whereas others have no deficits. We tested the hypothesis that in some patients with lesions affecting the SMA, the contralateral SMA exhibits some of the activation normally associated with the ipsilateral SMA. Functional MR imaging studies in seven healthy volunteers and 19 patients with frontal lobe tumors or arteriovenous malformations were reviewed retrospectively. The hemisphere in which the SMA activation predominated was tabulated for right and left motor tasks. The relative hemispheric dominance in the SMA for the right and left motor tasks was compared in the healthy and patient groups and with the location of the lesion in the patient group. None of the control subjects performing a right hand motor task activated predominantly the right SMA. Fifty percent of the patients with lesions overlapping the left SMA performing the right motor task activated predominantly the right SMA. Fifty-seven percent of control subjects performing the left hand motor task activated the left SMA predominantly. One hundred percent of patients with lesions overlapping the right frontal SMA performing the left motor task activated the left SMA predominantly. Differences between patients and controls were statistically significant. A lesion that contacts or overlaps the SMA is associated with an increased functional MR imaging response within the contralateral SMA.

The neural correlates of learned motor acuity

PubMed Central

Yang, Juemin; Caffo, Brian; Mazzoni, Pietro; Krakauer, John W.

2014-01-01

We recently defined a component of motor skill learning as “motor acuity,” quantified as a shift in the speed-accuracy trade-off function for a task. These shifts are primarily driven by reductions in movement variability. To determine the neural correlates of improvement in motor acuity, we devised a motor task compatible with magnetic resonance brain imaging that required subjects to make finely controlled wrist movements under visual guidance. Subjects were imaged on day 1 and day 5 while they performed this task and were trained outside the scanner on intervening days 2, 3, and 4. The potential confound of performance changes between days 1 and 5 was avoided by constraining movement time to a fixed duration. After training, subjects showed a marked increase in success rate and a reduction in trial-by-trial variability for the trained task but not for an untrained control task, without changes in mean trajectory. The decrease in variability for the trained task was associated with increased activation in contralateral primary motor and premotor cortical areas and in ipsilateral cerebellum. A global nonlocalizing multivariate analysis confirmed that learning was associated with increased overall brain activation. We suggest that motor acuity is acquired through increases in the number of neurons recruited in contralateral motor cortical areas and in ipsilateral cerebellum, which could reflect increased signal-to-noise ratio in motor output and improved state estimation for feedback corrections, respectively. PMID:24848466

Cerebellum and Integration of Neural Networks in Dual-Task Processing

PubMed Central

Wu, Tao; Liu, Jun; Hallett, Mark; Zheng, Zheng; Chan, Piu

2014-01-01

Performing two tasks simultaneously (dual-task) is common in human daily life. The neural correlates of dual-task processing remain unclear. In the current study, we used a dual motor and counting task with functional MRI (fMRI) to determine whether there are any areas additionally activated for dual-task performance. Moreover, we investigated the functional connectivity of these added activated areas, as well as the training effect on brain activity and connectivity. We found that the right cerebellar vermis, left lobule V of the cerebellar anterior lobe and precuneus are additionally activated for this type of dual-tasking. These cerebellar regions had functional connectivity with extensive motor- and cognitive-related regions. Dual-task training induced less activation in several areas, but increased the functional connectivity between these cerebellar regions and numbers of motor- and cognitive-related areas. Our findings demonstrate that some regions within the cerebellum can be additionally activated with dual-task performance. Their role in dual motor and cognitive task processes is likely to integrate motor and cognitive networks, and may be involved in adjusting these networks to be more efficient in order to perform dual-tasking properly. The connectivity of the precuneus differs from the cerebellar regions. A possible role of the precuneus in dual-task may be monitoring the operation of active brain networks. PMID:23063842

Association between educational status and dual-task performance in young adults.

PubMed

Voos, Mariana Callil; Pimentel Piemonte, Maria Elisa; Castelli, Lilian Zanchetta; Andrade Machado, Mariane Silva; Dos Santos Teixeira, Patrícia Pereira; Caromano, Fátima Aparecida; Ribeiro Do Valle, Luiz Eduardo

2015-04-01

The influence of educational status on perceptual-motor performance has not been investigated. The single- and dual-task performances of 15 Low educated adults (9 men, 6 women; M age=24.1 yr.; 6-9 yr. of education) and 15 Higher educated adults (8 men, 7 women; M age=24.7 yr.; 10-13 yr. of education) were compared. The perceptual task consisted of verbally classifying two figures (equal or different). The motor task consisted of alternating steps from the floor to a stool. Tasks were assessed individually and simultaneously. Two analyses of variance (2 groups×4 blocks) compared the errors and steps. The Low education group committed more errors and had less improvement on the perceptual task than the High education group. During and after the perceptual-motor task performance, errors increased only in the Low education group. Education correlated to perceptual and motor performance. The Low education group showed more errors and less step alternations on the perceptual-motor task compared to the High education group. This difference on the number of errors was also observed after the dual-task, when the perceptual task was performed alone.

Cathodal Transcranial Direct Current Stimulation Over Left Dorsolateral Prefrontal Cortex Area Promotes Implicit Motor Learning in a Golf Putting Task.

PubMed

Zhu, Frank F; Yeung, Andrew Y; Poolton, Jamie M; Lee, Tatia M C; Leung, Gilberto K K; Masters, Rich S W

2015-01-01

Implicit motor learning is characterized by low dependence on working memory and stable performance despite stress, fatigue, or multi-tasking. However, current paradigms for implicit motor learning are based on behavioral interventions that are often task-specific and limited when applied in practice. To investigate whether cathodal transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (DLPFC) area during motor learning suppressed working memory activity and reduced explicit verbal-analytical involvement in movement control, thereby promoting implicit motor learning. Twenty-seven healthy individuals practiced a golf putting task during a Training Phase while receiving either real cathodal tDCS stimulation over the left DLPFC area or sham stimulation. Their performance was assessed during a Test phase on another day. Verbal working memory capacity was assessed before and after the Training Phase, and before the Test Phase. Compared to sham stimulation, real stimulation suppressed verbal working memory activity after the Training Phase, but enhanced golf putting performance during the Training Phase and the Test Phase, especially when participants were required to multi-task. Cathodal tDCS over the left DLPFC may foster implicit motor learning and performance in complex real-life motor tasks that occur during sports, surgery or motor rehabilitation. Copyright © 2015 Elsevier Inc. All rights reserved.

Motor cortical encoding of serial order in a context-recall task.

PubMed

Carpenter, A F; Georgopoulos, A P; Pellizzer, G

1999-03-12

The neural encoding of serial order was studied in the motor cortex of monkeys performing a context-recall memory scanning task. Up to five visual stimuli were presented successively on a circle (list presentation phase), and then one of them (test stimulus) changed color; the monkeys had to make a single motor response toward the stimulus that immediately followed the test stimulus in the list. Correct performance in this task depends on memorization of the serial order of the stimuli during their presentation. It was found that changes in neural activity during the list presentation phase reflected the serial order of the stimuli; the effect on cell activity of the serial order of stimuli during their presentation was at least as strong as the effect of motor direction on cell activity during the execution of the motor response. This establishes the serial order of stimuli in a motor task as an important determinant of motor cortical activity during stimulus presentation and in the absence of changes in peripheral motor events, in contrast to the commonly held view of the motor cortex as just an "upper motor neuron."

Using a generalized linear mixed model approach to explore the role of age, motor proficiency, and cognitive styles in children's reach estimation accuracy.

PubMed

Caçola, Priscila M; Pant, Mohan D

2014-10-01

The purpose was to use a multi-level statistical technique to analyze how children's age, motor proficiency, and cognitive styles interact to affect accuracy on reach estimation tasks via Motor Imagery and Visual Imagery. Results from the Generalized Linear Mixed Model analysis (GLMM) indicated that only the 7-year-old age group had significant random intercepts for both tasks. Motor proficiency predicted accuracy in reach tasks, and cognitive styles (object scale) predicted accuracy in the motor imagery task. GLMM analysis is suitable to explore age and other parameters of development. In this case, it allowed an assessment of motor proficiency interacting with age to shape how children represent, plan, and act on the environment.

Localization of cortical primary motor area of the hand using navigated transcranial magnetic stimulation, BOLD and arterial spin labeling fMRI.

PubMed

Kallioniemi, Elisa; Pitkänen, Minna; Könönen, Mervi; Vanninen, Ritva; Julkunen, Petro

2016-11-01

Although the relationship between neuronavigated transcranial magnetic stimulation (nTMS) and functional magnetic resonance imaging (fMRI) has been widely studied in motor mapping, it is unknown how the motor response type or the choice of motor task affect this relationship. Centers of gravity (CoGs) and response maxima were measured with blood-oxygen-level dependent (BOLD) and arterial spin labeling (ASL) fMRI during motor tasks against nTMS CoGs and response maxima, which were mapped with motor evoked potentials (MEPs) and silent periods (SPs). No differences in motor representations (CoGs and response maxima) were observed in lateral-medial direction (p=0.265). fMRI methods localized the motor representation more posterior than nTMS (p<0.001). This was not affected by the BOLD fMRI motor task (p>0.999) nor nTMS response type (p>0.999). ASL fMRI maxima did not differ from the nTMS nor BOLD fMRI CoGs (p≥0.070), but the ASL CoG was deeper in comparison to other methods (p≤0.042). The BOLD fMRI motor task did not influence the depth of the motor representation (p≥0.745). The median Euclidean distances between the nTMS and fMRI motor representations varied between 7.7mm and 14.5mm and did not differ between the methods (F≤1.23, p≥0.318). The relationship between fMRI and nTMS mapped excitatory (MEP) and inhibitory (SP) responses, and whether the choice of motor task affects this relationship, have not been studied before. The congruence between fMRI and nTMS is good. The choice of nTMS motor response type nor BOLD fMRI motor task had no effect on this relationship. Copyright © 2016 Elsevier B.V. All rights reserved.

Training, Retention, and Transfer of Data Entry Perceptual and Motoric Processes Over Long Retention Intervals

NASA Technical Reports Server (NTRS)

Kole, James A.; Schneider, Vivian I.; Healy, Alice F.; Barshi, Immanuel

2017-01-01

Subjects trained in a standard data entry task, which involved typing numbers (e.g., 5421) using their right hands. At test (6 months post-training), subjects completed the standard task, followed by a left-hand variant (typing with their left hands) that involved the same perceptual, but different motoric, processes as the standard task. At a second test (8 months post-training), subjects completed the standard task, followed by a code variant (translating letters into digits, then typing the digits with their right hands) that involved different perceptual, but the same motoric, processes as the standard task. For each of the three tasks, half the trials were trained numbers (old) and half were new. Repetition priming (faster response times to old than new numbers) was found for each task. Repetition priming for the standard task reflects retention of trained numbers; for the left-hand variant reflects transfer of perceptual processes; and for the code variant reflects transfer of motoric processes. There was thus evidence for both specificity and generalizability of training data entry perceptual and motoric processes over very long retention intervals.

Neural decoding with kernel-based metric learning.

PubMed

Brockmeier, Austin J; Choi, John S; Kriminger, Evan G; Francis, Joseph T; Principe, Jose C

2014-06-01

In studies of the nervous system, the choice of metric for the neural responses is a pivotal assumption. For instance, a well-suited distance metric enables us to gauge the similarity of neural responses to various stimuli and assess the variability of responses to a repeated stimulus-exploratory steps in understanding how the stimuli are encoded neurally. Here we introduce an approach where the metric is tuned for a particular neural decoding task. Neural spike train metrics have been used to quantify the information content carried by the timing of action potentials. While a number of metrics for individual neurons exist, a method to optimally combine single-neuron metrics into multineuron, or population-based, metrics is lacking. We pose the problem of optimizing multineuron metrics and other metrics using centered alignment, a kernel-based dependence measure. The approach is demonstrated on invasively recorded neural data consisting of both spike trains and local field potentials. The experimental paradigm consists of decoding the location of tactile stimulation on the forepaws of anesthetized rats. We show that the optimized metrics highlight the distinguishing dimensions of the neural response, significantly increase the decoding accuracy, and improve nonlinear dimensionality reduction methods for exploratory neural analysis.

Sensory-guided motor tasks benefit from mental training based on serial prediction

PubMed Central

Binder, Ellen; Hagelweide, Klara; Wang, Ling E.; Kornysheva, Katja; Grefkes, Christian; Fink, Gereon R.; Schubotz, Ricarda I.

2017-01-01

Mental strategies have been suggested to constitute a promising approach to improve motor abilities in both healthy subjects and patients. This behavioural effect has been shown to be associated with changes of neural activity in premotor areas, not only during movement execution, but also while performing motor imagery or action observation. However, how well such mental tasks are performed is often difficult to assess, especially in patients. We here used a novel mental training paradigm based on the serial prediction task (SPT) in order to activate premotor circuits in the absence of a motor task. We then tested whether this intervention improves motor-related performance such as sensorimotor transformation. Two groups of healthy young participants underwent a single-blinded five-day cognitive training schedule and were tested in four different motor tests on the day before and after training. One group (N = 22) received the SPT-training and the other one (N = 21) received a control training based on a serial match-to-sample task. The results revealed significant improvements of the SPT-group in a sensorimotor timing task, i.e. synchronization of finger tapping to a visually presented rhythm, as well as improved visuomotor coordination in a sensory-guided pointing task compared to the group that received the control training. However, mental training did not show transfer effects on motor abilities in healthy subjects beyond the trained modalities as evident by non-significant changes in the Jebsen–Taylor handfunctiontest. In summary, the data suggest that mental training based on the serial prediction task effectively engages sensorimotor circuits and thereby improves motor behaviour. PMID:24321273

Poststimulation time interval-dependent effects of motor cortex anodal tDCS on reaction-time task performance.

PubMed

Molero-Chamizo, Andrés; Alameda Bailén, José R; Garrido Béjar, Tamara; García López, Macarena; Jaén Rodríguez, Inmaculada; Gutiérrez Lérida, Carolina; Pérez Panal, Silvia; González Ángel, Gloria; Lemus Corchero, Laura; Ruiz Vega, María J; Nitsche, Michael A; Rivera-Urbina, Guadalupe N

2018-02-01

Anodal transcranial direct current stimulation (tDCS) induces long-term potentiation-like plasticity, which is associated with long-lasting effects on different cognitive, emotional, and motor performances. Specifically, tDCS applied over the motor cortex is considered to improve reaction time in simple and complex tasks. The timing of tDCS relative to task performance could determine the efficacy of tDCS to modulate performance. The aim of this study was to compare the effects of a single session of anodal tDCS (1.5 mA, for 15 min) applied over the left primary motor cortex (M1) versus sham stimulation on performance of a go/no-go simple reaction-time task carried out at three different time points after tDCS-namely, 0, 30, or 60 min after stimulation. Performance zero min after anodal tDCS was improved during the whole course of the task. Performance 30 min after anodal tDCS was improved only in the last block of the reaction-time task. Performance 60 min after anodal tDCS was not significantly different throughout the entire task. These findings suggest that the motor cortex excitability changes induced by tDCS can improve motor responses, and these effects critically depend on the time interval between stimulation and task performance.

Gene Expression Changes in the Motor Cortex Mediating Motor Skill Learning

PubMed Central

Cheung, Vincent C. K.; DeBoer, Caroline; Hanson, Elizabeth; Tunesi, Marta; D'Onofrio, Mara; Arisi, Ivan; Brandi, Rossella; Cattaneo, Antonino; Goosens, Ki A.

2013-01-01

The primary motor cortex (M1) supports motor skill learning, yet little is known about the genes that contribute to motor cortical plasticity. Such knowledge could identify candidate molecules whose targeting might enable a new understanding of motor cortical functions, and provide new drug targets for the treatment of diseases which impair motor function, such as ischemic stroke. Here, we assess changes in the motor-cortical transcriptome across different stages of motor skill acquisition. Adult rats were trained on a gradually acquired appetitive reach and grasp task that required different strategies for successful pellet retrieval, or a sham version of the task in which the rats received pellet reward without needing to develop the reach and grasp skill. Tissue was harvested from the forelimb motor-cortical area either before training commenced, prior to the initial rise in task performance, or at peak performance. Differential classes of gene expression were observed at the time point immediately preceding motor task improvement. Functional clustering revealed that gene expression changes were related to the synapse, development, intracellular signaling, and the fibroblast growth factor (FGF) family, with many modulated genes known to regulate synaptic plasticity, synaptogenesis, and cytoskeletal dynamics. The modulated expression of synaptic genes likely reflects ongoing network reorganization from commencement of training till the point of task improvement, suggesting that motor performance improves only after sufficient modifications in the cortical circuitry have accumulated. The regulated FGF-related genes may together contribute to M1 remodeling through their roles in synaptic growth and maturation. PMID:23637843

Effect of motor imagery in children with unilateral cerebral palsy: fMRI study.

PubMed

Chinier, Eva; N'Guyen, Sylvie; Lignon, Grégoire; Ter Minassian, Aram; Richard, Isabelle; Dinomais, Mickaël

2014-01-01

Motor imagery is considered as a promising therapeutic tool for rehabilitation of motor planning problems in patients with cerebral palsy. However motor planning problems may lead to poor motor imagery ability. The aim of this functional magnetic resonance imaging study was to examine and compare brain activation following motor imagery tasks in patients with hemiplegic cerebral palsy with left or right early brain lesions. We tested also the influence of the side of imagined hand movement. Twenty patients with clinical hemiplegic cerebral palsy (sixteen males, mean age 12 years and 10 months, aged 6 years 10 months to 20 years 10 months) participated in this study. Using block design, brain activations following motor imagery of a simple opening-closing hand movement performed by either the paretic or nonparetic hand was examined. During motor imagery tasks, patients with early right brain damages activated bilateral fronto-parietal network that comprise most of the nodes of the network well described in healthy subjects. Inversely, in patients with left early brain lesion brain activation following motor imagery tasks was reduced, compared to patients with right brain lesions. We found also a weak influence of the side of imagined hand movement. Decreased activations following motor imagery in patients with right unilateral cerebral palsy highlight the dominance of the left hemisphere during motor imagery tasks. This study gives neuronal substrate to propose motor imagery tasks in unilateral cerebral palsy rehabilitation at least for patients with right brain lesions.

Effects of Concurrent Motor, Linguistic, or Cognitive Tasks on Speech Motor Performance

ERIC Educational Resources Information Center

Dromey, Christopher; Benson, April

2003-01-01

This study examined the influence of 3 different types of concurrent tasks on speech motor performance. The goal was to uncover potential differences in speech movements relating to the nature of the secondary task. Twenty young adults repeated sentences either with or without simultaneous distractor activities. These distractions included a motor…

Narrative Ability of Children With Speech Sound Disorders and the Prediction of Later Literacy Skills

PubMed Central

Wellman, Rachel L.; Lewis, Barbara A.; Freebairn, Lisa A.; Avrich, Allison A.; Hansen, Amy J.; Stein, Catherine M.

2012-01-01

Purpose The main purpose of this study was to examine how children with isolated speech sound disorders (SSDs; n = 20), children with combined SSDs and language impairment (LI; n = 20), and typically developing children (n = 20), ages 3;3 (years;months) to 6;6, differ in narrative ability. The second purpose was to determine if early narrative ability predicts school-age (8–12 years) literacy skills. Method This study employed a longitudinal cohort design. The children completed a narrative retelling task before their formal literacy instruction began. The narratives were analyzed and compared for group differences. Performance on these early narratives was then used to predict the children’s reading decoding, reading comprehension, and written language ability at school age. Results Significant group differences were found in children’s (a) ability to answer questions about the story, (b) use of story grammars, and (c) number of correct and irrelevant utterances. Regression analysis demonstrated that measures of story structure and accuracy were the best predictors of the decoding of real words, reading comprehension, and written language. Measures of syntax and lexical diversity were the best predictors of the decoding of nonsense words. Conclusion Combined SSDs and LI, and not isolated SSDs, impact a child’s narrative abilities. Narrative retelling is a useful task for predicting which children may be at risk for later literacy problems. PMID:21969531

Bayer image parallel decoding based on GPU

NASA Astrophysics Data System (ADS)

Hu, Rihui; Xu, Zhiyong; Wei, Yuxing; Sun, Shaohua

2012-11-01

In the photoelectrical tracking system, Bayer image is decompressed in traditional method, which is CPU-based. However, it is too slow when the images become large, for example, 2K×2K×16bit. In order to accelerate the Bayer image decoding, this paper introduces a parallel speedup method for NVIDA's Graphics Processor Unit (GPU) which supports CUDA architecture. The decoding procedure can be divided into three parts: the first is serial part, the second is task-parallelism part, and the last is data-parallelism part including inverse quantization, inverse discrete wavelet transform (IDWT) as well as image post-processing part. For reducing the execution time, the task-parallelism part is optimized by OpenMP techniques. The data-parallelism part could advance its efficiency through executing on the GPU as CUDA parallel program. The optimization techniques include instruction optimization, shared memory access optimization, the access memory coalesced optimization and texture memory optimization. In particular, it can significantly speed up the IDWT by rewriting the 2D (Tow-dimensional) serial IDWT into 1D parallel IDWT. Through experimenting with 1K×1K×16bit Bayer image, data-parallelism part is 10 more times faster than CPU-based implementation. Finally, a CPU+GPU heterogeneous decompression system was designed. The experimental result shows that it could achieve 3 to 5 times speed increase compared to the CPU serial method.

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

The role of rotational hand movements and general motor ability in children’s mental rotation performance

PubMed Central

Jansen, Petra; Kellner, Jan

2015-01-01

Mental rotation of visual images of body parts and abstract shapes can be influenced by simultaneous motor activity. Children in particular have a strong coupling between motor and cognitive processes. We investigated the influence of a rotational hand movement performed by rotating a knob on mental rotation performance in primary school-age children (N = 83; age range: 7.0–8.3 and 9.0–10.11 years). In addition, we assessed the role of motor ability in this relationship. Boys in the 7- to 8-year-old group were faster when mentally and manually rotating in the same direction than in the opposite direction. For girls and older children this effect was not found. A positive relationship was found between motor ability and accuracy on the mental rotation task: stronger motor ability related to improved mental rotation performance. In both age groups, children with more advanced motor abilities were more likely to adopt motor processes to solve mental rotation tasks if the mental rotation task was primed by a motor task. Our evidence supports the idea that an overlap between motor and visual cognitive processes in children is influenced by motor ability. PMID:26236262

The role of rotational hand movements and general motor ability in children's mental rotation performance.

PubMed

Jansen, Petra; Kellner, Jan

2015-01-01

Mental rotation of visual images of body parts and abstract shapes can be influenced by simultaneous motor activity. Children in particular have a strong coupling between motor and cognitive processes. We investigated the influence of a rotational hand movement performed by rotating a knob on mental rotation performance in primary school-age children (N = 83; age range: 7.0-8.3 and 9.0-10.11 years). In addition, we assessed the role of motor ability in this relationship. Boys in the 7- to 8-year-old group were faster when mentally and manually rotating in the same direction than in the opposite direction. For girls and older children this effect was not found. A positive relationship was found between motor ability and accuracy on the mental rotation task: stronger motor ability related to improved mental rotation performance. In both age groups, children with more advanced motor abilities were more likely to adopt motor processes to solve mental rotation tasks if the mental rotation task was primed by a motor task. Our evidence supports the idea that an overlap between motor and visual cognitive processes in children is influenced by motor ability.

Motor cortex is required for learning but not for executing a motor skill.

PubMed

Kawai, Risa; Markman, Timothy; Poddar, Rajesh; Ko, Raymond; Fantana, Antoniu L; Dhawale, Ashesh K; Kampff, Adam R; Ölveczky, Bence P

2015-05-06

Motor cortex is widely believed to underlie the acquisition and execution of motor skills, but its contributions to these processes are not fully understood. One reason is that studies on motor skills often conflate motor cortex's established role in dexterous control with roles in learning and producing task-specific motor sequences. To dissociate these aspects, we developed a motor task for rats that trains spatiotemporally precise movement patterns without requirements for dexterity. Remarkably, motor cortex lesions had no discernible effect on the acquired skills, which were expressed in their distinct pre-lesion forms on the very first day of post-lesion training. Motor cortex lesions prior to training, however, rendered rats unable to acquire the stereotyped motor sequences required for the task. These results suggest a remarkable capacity of subcortical motor circuits to execute learned skills and a previously unappreciated role for motor cortex in "tutoring" these circuits during learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Single Neurons in M1 and Premotor Cortex Directly Reflect Behavioral Interference

PubMed Central

Zach, Neta; Inbar, Dorrit; Grinvald, Yael; Vaadia, Eilon

2012-01-01

Some motor tasks, if learned together, interfere with each other's consolidation and subsequent retention, whereas other tasks do not. Interfering tasks are said to employ the same internal model whereas noninterfering tasks use different models. The division of function among internal models, as well as their possible neural substrates, are not well understood. To investigate these questions, we compared responses of single cells in the primary motor cortex and premotor cortex of primates to interfering and noninterfering tasks. The interfering tasks were visuomotor rotation followed by opposing visuomotor rotation. The noninterfering tasks were visuomotor rotation followed by an arbitrary association task. Learning two noninterfering tasks led to the simultaneous formation of neural activity typical of both tasks, at the level of single neurons. In contrast, and in accordance with behavioral results, after learning two interfering tasks, only the second task was successfully reflected in motor cortical single cell activity. These results support the hypothesis that the representational capacity of motor cortical cells is the basis of behavioral interference and division between internal models. PMID:22427923

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection-Relevance for Neuroscience and Clinical Applications.

PubMed

Kirchner, Elsa A; Kim, Su Kyoung

2018-01-01

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent ( targets ), motor-task irrelevant infrequent ( deviants ), and motor-task irrelevant frequent ( standards ) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention.

Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection—Relevance for Neuroscience and Clinical Applications

PubMed Central

Kirchner, Elsa A.; Kim, Su Kyoung

2018-01-01

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention. PMID:29636660

Abstract Representations of Object-Directed Action in the Left Inferior Parietal Lobule.

PubMed

Chen, Quanjing; Garcea, Frank E; Jacobs, Robert A; Mahon, Bradford Z

2018-06-01

Prior neuroimaging and neuropsychological research indicates that the left inferior parietal lobule in the human brain is a critical substrate for representing object manipulation knowledge. In the present functional MRI study we used multivoxel pattern analyses to test whether action similarity among objects can be decoded in the inferior parietal lobule independent of the task applied to objects (identification or pantomime) and stimulus format in which stimuli are presented (pictures or printed words). Participants pantomimed the use of objects, cued by printed words, or identified pictures of objects. Classifiers were trained and tested across task (e.g., training data: pantomime; testing data: identification), stimulus format (e.g., training data: word format; testing format: picture) and specific objects (e.g., training data: scissors vs. corkscrew; testing data: pliers vs. screwdriver). The only brain region in which action relations among objects could be decoded across task, stimulus format and objects was the inferior parietal lobule. By contrast, medial aspects of the ventral surface of the left temporal lobe represented object function, albeit not at the same level of abstractness as actions in the inferior parietal lobule. These results suggest compulsory access to abstract action information in the inferior parietal lobe even when simply identifying objects.

A Framework to Describe, Analyze and Generate Interactive Motor Behaviors

PubMed Central

Jarrassé, Nathanaël; Charalambous, Themistoklis; Burdet, Etienne

2012-01-01

While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks. PMID:23226231

A framework to describe, analyze and generate interactive motor behaviors.

PubMed

Jarrassé, Nathanaël; Charalambous, Themistoklis; Burdet, Etienne

2012-01-01

While motor interaction between a robot and a human, or between humans, has important implications for society as well as promising applications, little research has been devoted to its investigation. In particular, it is important to understand the different ways two agents can interact and generate suitable interactive behaviors. Towards this end, this paper introduces a framework for the description and implementation of interactive behaviors of two agents performing a joint motor task. A taxonomy of interactive behaviors is introduced, which can classify tasks and cost functions that represent the way each agent interacts. The role of an agent interacting during a motor task can be directly explained from the cost function this agent is minimizing and the task constraints. The novel framework is used to interpret and classify previous works on human-robot motor interaction. Its implementation power is demonstrated by simulating representative interactions of two humans. It also enables us to interpret and explain the role distribution and switching between roles when performing joint motor tasks.

Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context. Experiments and an Information-Theoretic Ambiguity Model

PubMed Central

Grau-Moya, Jordi; Ortega, Pedro A.; Braun, Daniel A.

2016-01-01

A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects’ choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects’ choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain. PMID:27124723

Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context. Experiments and an Information-Theoretic Ambiguity Model.

PubMed

Grau-Moya, Jordi; Ortega, Pedro A; Braun, Daniel A

2016-01-01

A number of recent studies have investigated differences in human choice behavior depending on task framing, especially comparing economic decision-making to choice behavior in equivalent sensorimotor tasks. Here we test whether decision-making under ambiguity exhibits effects of task framing in motor vs. non-motor context. In a first experiment, we designed an experience-based urn task with varying degrees of ambiguity and an equivalent motor task where subjects chose between hitting partially occluded targets. In a second experiment, we controlled for the different stimulus design in the two tasks by introducing an urn task with bar stimuli matching those in the motor task. We found ambiguity attitudes to be mainly influenced by stimulus design. In particular, we found that the same subjects tended to be ambiguity-preferring when choosing between ambiguous bar stimuli, but ambiguity-avoiding when choosing between ambiguous urn sample stimuli. In contrast, subjects' choice pattern was not affected by changing from a target hitting task to a non-motor context when keeping the stimulus design unchanged. In both tasks subjects' choice behavior was continuously modulated by the degree of ambiguity. We show that this modulation of behavior can be explained by an information-theoretic model of ambiguity that generalizes Bayes-optimal decision-making by combining Bayesian inference with robust decision-making under model uncertainty. Our results demonstrate the benefits of information-theoretic models of decision-making under varying degrees of ambiguity for a given context, but also demonstrate the sensitivity of ambiguity attitudes across contexts that theoretical models struggle to explain.

Cognitive-motor interference during functional mobility after stroke: state of the science and implications for future research.

PubMed

Plummer, Prudence; Eskes, Gail; Wallace, Sarah; Giuffrida, Clare; Fraas, Michael; Campbell, Grace; Clifton, Kerrylee; Skidmore, Elizabeth R

2013-12-01

Cognitive-motor interference (CMI) is evident when simultaneous performance of a cognitive task and a motor task results in deterioration in performance in one or both of the tasks, relative to performance of each task separately. The purpose of this review is to present a framework for categorizing patterns of CMI and to examine the specific patterns of CMI evident in published studies comparing single-task and dual-task performance of cognitive and motor tasks during gait and balance activities after stroke. We also examine the literature for associations between patterns of CMI and a history of falls, as well as evidence for the effects of rehabilitation on CMI after stroke. Overall, this review suggests that during gait activities with an added cognitive task, people with stroke are likely to demonstrate significant decrements in motor performance only (cognitive-related motor interference), or decrements in both motor and cognitive performance (mutual interference). In contrast, patterns of CMI were variable among studies examining balance activities. Comparing people poststroke with and without a history of falls, patterns and magnitude of CMI were similar for fallers and nonfallers. Longitudinal studies suggest that conventional rehabilitation has minimal effects on CMI during gait or balance activities. However, early-phase pilot studies suggest that dual-task interventions may reduce CMI during gait performance in community-dwelling stroke survivors. It is our hope that this innovative and critical examination of the existing literature will highlight the limitations in current experimental designs and inform improvements in the design and reporting of dual-task studies in stroke. Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Brain Activation in Motor Sequence Learning Is Related to the Level of Native Cortical Excitability

PubMed Central

Lissek, Silke; Vallana, Guido S.; Güntürkün, Onur; Dinse, Hubert; Tegenthoff, Martin

2013-01-01

Cortical excitability may be subject to changes through training and learning. Motor training can increase cortical excitability in motor cortex, and facilitation of motor cortical excitability has been shown to be positively correlated with improvements in performance in simple motor tasks. Thus cortical excitability may tentatively be considered as a marker of learning and use-dependent plasticity. Previous studies focused on changes in cortical excitability brought about by learning processes, however, the relation between native levels of cortical excitability on the one hand and brain activation and behavioral parameters on the other is as yet unknown. In the present study we investigated the role of differential native motor cortical excitability for learning a motor sequencing task with regard to post-training changes in excitability, behavioral performance and involvement of brain regions. Our motor task required our participants to reproduce and improvise over a pre-learned motor sequence. Over both task conditions, participants with low cortical excitability (CElo) showed significantly higher BOLD activation in task-relevant brain regions than participants with high cortical excitability (CEhi). In contrast, CElo and CEhi groups did not exhibit differences in percentage of correct responses and improvisation level. Moreover, cortical excitability did not change significantly after learning and training in either group, with the exception of a significant decrease in facilitatory excitability in the CEhi group. The present data suggest that the native, unmanipulated level of cortical excitability is related to brain activation intensity, but not to performance quality. The higher BOLD mean signal intensity during the motor task might reflect a compensatory mechanism in CElo participants. PMID:23613956

Age Differences between Children and Young Adults in the Dynamics of Dual-Task Prioritization: Body (Balance) versus Mind (Memory)

ERIC Educational Resources Information Center

Schaefer, Sabine; Krampe, Ralf Th.; Lindenberger, Ulman; Baltes, Paul B.

2008-01-01

Task prioritization can lead to trade-off patterns in dual-task situations. The authors compared dual-task performances in 9- and 11-year-old children and young adults performing a cognitive task and a motor task concurrently. The motor task required balancing on an ankle-disc board. Two cognitive tasks measured working memory and episodic memory…

Lexical processing while deciding what task to perform: reading aloud in the context of the task set paradigm.

PubMed

O'Malley, Shannon; Besner, Derek

2011-12-01

The results of two experiments provide the first direct demonstration that subjects can process a word lexically despite concurrently being engaged in decoding a task cue telling them which of two tasks to perform. These results, taken together with others, point to qualitative differences between the mind's ability to engage in lexical versus sublexical processing during the time they are engaged with other tasks. The emerging picture is one in which some form of resource(s) plays little role during lexical processing whereas the need for some form of resource(s) during sublexical processing serves to bottleneck performance. Copyright © 2011 Elsevier Inc. All rights reserved.

Testing the distinctiveness of visual imagery and motor imagery in a reach paradigm.

PubMed

Gabbard, Carl; Ammar, Diala; Cordova, Alberto

2009-01-01

We examined the distinctiveness of motor imagery (MI) and visual imagery (VI) in the context of perceived reachability. The aim was to explore the notion that the two visual modes have distinctive processing properties tied to the two-visual-system hypothesis. The experiment included an interference tactic whereby participants completed two tasks at the same time: a visual or motor-interference task combined with a MI or VI-reaching task. We expected increased error would occur when the imaged task and the interference task were matched (e.g., MI with the motor task), suggesting an association based on the assumption that the two tasks were in competition for space on the same processing pathway. Alternatively, if there were no differences, dissociation could be inferred. Significant increases in the number of errors were found when the modalities for the imaged (both MI and VI) task and the interference task were matched. Therefore, it appears that MI and VI in the context of perceived reachability recruit different processing mechanisms.

Altered cortical processing of motor inhibition in schizophrenia.

PubMed

Lindberg, Påvel G; Térémetz, Maxime; Charron, Sylvain; Kebir, Oussama; Saby, Agathe; Bendjemaa, Narjes; Lion, Stéphanie; Crépon, Benoît; Gaillard, Raphaël; Oppenheim, Catherine; Krebs, Marie-Odile; Amado, Isabelle

2016-12-01

Inhibition is considered a key mechanism in schizophrenia. Short-latency intracortical inhibition (SICI) in the motor cortex is reduced in schizophrenia and is considered to reflect locally deficient γ-aminobutyric acid (GABA)-ergic modulation. However, it remains unclear how SICI is modulated during motor inhibition and how it relates to neural processing in other cortical areas. Here we studied motor inhibition Stop signal task (SST) in stabilized patients with schizophrenia (N = 28), healthy siblings (N = 21) and healthy controls (n = 31) matched in general cognitive status and educational level. Transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) were used to investigate neural correlates of motor inhibition. SST performance was similar in patients and controls. SICI was modulated by the task as expected in healthy controls and siblings but was reduced in patients with schizophrenia during inhibition despite equivalent motor inhibition performance. fMRI showed greater prefrontal and premotor activation during motor inhibition in schizophrenia. Task-related modulation of SICI was higher in subjects who showed less inhibition-related activity in pre-supplementary motor area (SMA) and cingulate motor area. An exploratory genetic analysis of selected markers of inhibition (GABRB2, GAD1, GRM1, and GRM3) did not explain task-related differences in SICI or cortical activation. In conclusion, this multimodal study provides direct evidence of a task-related deficiency in SICI modulation in schizophrenia likely reflecting deficient GABA-A related processing in motor cortex. Compensatory activation of premotor areas may explain similar motor inhibition in patients despite local deficits in intracortical processing. Task-related modulation of SICI may serve as a useful non-invasive GABAergic marker in development of therapeutic strategies in schizophrenia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Forelimb training drives transient map reorganization in ipsilateral motor cortex

PubMed Central

Pruitt, David T.; Schmid, Ariel N.; Danaphongse, Tanya T.; Flanagan, Kate E.; Morrison, Robert A.; Kilgard, Michael P.; Rennaker, Robert L.; Hays, Seth A.

2016-01-01

Skilled motor training results in reorganization of contralateral motor cortex movement representations. The ipsilateral motor cortex is believed to play a role in skilled motor control, but little is known about how training influences reorganization of ipsilateral motor representations of the trained limb. To determine whether training results in reorganization of ipsilateral motor cortex maps, rats were trained to perform the isometric pull task, an automated motor task that requires skilled forelimb use. After either 3 or 6 months of training, intracortical microstimulation (ICMS) mapping was performed to document motor representations of the trained forelimb in the hemisphere ipsilateral to that limb. Motor training for 3 months resulted in a robust expansion of right forelimb representation in the right motor cortex, demonstrating that skilled motor training drives map plasticity ipsilateral to the trained limb. After 6 months of training, the right forelimb representation in the right motor cortex was significantly smaller than the representation observed in rats trained for 3 months and similar to untrained controls, consistent with a normalization of motor cortex maps. Forelimb map area was not correlated with performance on the trained task, suggesting that task performance is maintained despite normalization of cortical maps. This study provides new insights into how the ipsilateral cortex changes in response to skilled learning and may inform rehabilitative strategies to enhance cortical plasticity to support recovery after brain injury. PMID:27392641

Forelimb training drives transient map reorganization in ipsilateral motor cortex.

PubMed

Pruitt, David T; Schmid, Ariel N; Danaphongse, Tanya T; Flanagan, Kate E; Morrison, Robert A; Kilgard, Michael P; Rennaker, Robert L; Hays, Seth A

2016-10-15

Skilled motor training results in reorganization of contralateral motor cortex movement representations. The ipsilateral motor cortex is believed to play a role in skilled motor control, but little is known about how training influences reorganization of ipsilateral motor representations of the trained limb. To determine whether training results in reorganization of ipsilateral motor cortex maps, rats were trained to perform the isometric pull task, an automated motor task that requires skilled forelimb use. After either 3 or 6 months of training, intracortical microstimulation (ICMS) mapping was performed to document motor representations of the trained forelimb in the hemisphere ipsilateral to that limb. Motor training for 3 months resulted in a robust expansion of right forelimb representation in the right motor cortex, demonstrating that skilled motor training drives map plasticity ipsilateral to the trained limb. After 6 months of training, the right forelimb representation in the right motor cortex was significantly smaller than the representation observed in rats trained for 3 months and similar to untrained controls, consistent with a normalization of motor cortex maps. Forelimb map area was not correlated with performance on the trained task, suggesting that task performance is maintained despite normalization of cortical maps. This study provides new insights into how the ipsilateral cortex changes in response to skilled learning and may inform rehabilitative strategies to enhance cortical plasticity to support recovery after brain injury. Copyright © 2016 Elsevier B.V. All rights reserved.

An online brain-machine interface using decoding of movement direction from the human electrocorticogram

NASA Astrophysics Data System (ADS)

Milekovic, Tomislav; Fischer, Jörg; Pistohl, Tobias; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Rickert, Jörn; Ball, Tonio; Mehring, Carsten

2012-08-01

A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

"Decoding versus comprehension": Brain responses underlying reading comprehension in children with autism.

PubMed

Bednarz, Haley M; Maximo, Jose O; Murdaugh, Donna L; O'Kelley, Sarah; Kana, Rajesh K

2017-06-01

Despite intact decoding ability, deficits in reading comprehension are relatively common in children with autism spectrum disorders (ASD). However, few neuroimaging studies have tested the neural bases of this specific profile of reading deficit in ASD. This fMRI study examined activation and synchronization of the brain's reading network in children with ASD with specific reading comprehension deficits during a word similarities task. Thirteen typically developing children and 18 children with ASD performed the task in the MRI scanner. No statistically significant group differences in functional activation were observed; however, children with ASD showed decreased functional connectivity between the left inferior frontal gyrus (LIFG) and the left inferior occipital gyrus (LIOG). In addition, reading comprehension ability significantly positively predicted functional connectivity between the LIFG and left thalamus (LTHAL) among all subjects. The results of this study provide evidence for altered recruitment of reading-related neural resources in ASD children and suggest specific weaknesses in top-down modulation of semantic processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Methods for Assessment of Memory Reactivation.

PubMed

Liu, Shizhao; Grosmark, Andres D; Chen, Zhe

2018-04-13

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Emotion recognition in body dysmorphic disorder: application of the Reading the Mind in the Eyes Task.

PubMed

Buhlmann, Ulrike; Winter, Anna; Kathmann, Norbert

2013-03-01

Body dysmorphic disorder (BDD) is characterized by perceived appearance-related defects, often tied to aspects of the face or head (e.g., acne). Deficits in decoding emotional expressions have been examined in several psychological disorders including BDD. Previous research indicates that BDD is associated with impaired facial emotion recognition, particularly in situations that involve the BDD sufferer him/herself. The purpose of this study was to further evaluate the ability to read other people's emotions among 31 individuals with BDD, and 31 mentally healthy controls. We applied the Reading the Mind in the Eyes task, in which participants are presented with a series of pairs of eyes, one at a time, and are asked to identify the emotion that describes the stimulus best. The groups did not differ with respect to decoding other people's emotions by looking into their eyes. Findings are discussed in light of previous research examining emotion recognition in BDD. Copyright © 2013. Published by Elsevier Ltd.

Nondeclarative learning in children with specific language impairment: predicting regularities in the visuomotor, phonological, and cognitive domains.

PubMed

Mayor-Dubois, C; Zesiger, P; Van der Linden, M; Roulet-Perez, E

2014-01-01

Ullman (2004) suggested that Specific Language Impairment (SLI) results from a general procedural learning deficit. In order to test this hypothesis, we investigated children with SLI via procedural learning tasks exploring the verbal, motor, and cognitive domains. Results showed that compared with a Control Group, the children with SLI (a) were unable to learn a phonotactic learning task, (b) were able but less efficiently to learn a motor learning task and (c) succeeded in a cognitive learning task. Regarding the motor learning task (Serial Reaction Time Task), reaction times were longer and learning slower than in controls. The learning effect was not significant in children with an associated Developmental Coordination Disorder (DCD), and future studies should consider comorbid motor impairment in order to clarify whether impairments are related to the motor rather than the language disorder. Our results indicate that a phonotactic learning but not a cognitive procedural deficit underlies SLI, thus challenging Ullmans' general procedural deficit hypothesis, like a few other recent studies.

Prediction of Imagined Single-Joint Movements in a Person with High Level Tetraplegia

PubMed Central

Simeral, John D.; Donoghue, John P.; Hochberg, Leigh R.; Kirsch, Robert F.

2013-01-01

Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements while SUA, multi-spike trains (MSP), multi-unit activity (MUA), and local field potential time (LFPrms) and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20 – 60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints. PMID:22851229

Cortical Correlates of Fitts’ Law

PubMed Central

Ifft, Peter J.; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2011-01-01

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control. PMID:22275888

Quantitative Motor Performance and Sleep Benefit in Parkinson Disease

PubMed Central

van Gilst, Merel M.; van Mierlo, Petra; Bloem, Bastiaan R.; Overeem, Sebastiaan

2015-01-01

Study Objectives: Many people with Parkinson disease experience “sleep benefit”: temporarily improved mobility upon awakening. Here we used quantitative motor tasks to assess the influence of sleep on motor functioning in Parkinson disease. Design: Eighteen Parkinson patients with and 20 without subjective sleep benefit and 20 healthy controls participated. Before and directly after a regular night sleep and an afternoon nap, subjects performed the timed pegboard dexterity task and quantified finger tapping task. Subjective ratings of motor functioning and mood/vigilange were included. Sleep was monitored using polysomnography. Results: On both tasks, patients were overall slower than healthy controls (night: F2,55 = 16.938, P < 0.001; nap: F2,55 = 15.331, P < 0.001). On the pegboard task, there was a small overall effect of night sleep (F1,55 = 9.695, P = 0.003); both patients and controls were on average slightly slower in the morning. However, in both tasks there was no sleep*group interaction for nighttime sleep nor for afternoon nap. There was a modest correlation between the score on the pegboard task and self-rated motor symptoms among patients (rho = 0.233, P = 0.004). No correlations in task performance and mood/vigilance or sleep time/efficiency were found. Conclusions: A positive effect of sleep on motor function is commonly reported by Parkinson patients. Here we show that the subjective experience of sleep benefit is not paralleled by an actual improvement in motor functioning. Sleep benefit therefore appears to be a subjective phenomenon and not a Parkinson-specific reduction in symptoms. Citation: van Gilst MM, van Mierlo P, Bloem BR, Overeem S. Quantitative Motor Performance and Sleep Benefit in Parkinson Disease. SLEEP 2015;38(10):1567–1573. PMID:25902811

What Do Eye Gaze Metrics Tell Us about Motor Imagery?

PubMed

Poiroux, Elodie; Cavaro-Ménard, Christine; Leruez, Stéphanie; Lemée, Jean Michel; Richard, Isabelle; Dinomais, Mickael

2015-01-01

Many of the brain structures involved in performing real movements also have increased activity during imagined movements or during motor observation, and this could be the neural substrate underlying the effects of motor imagery in motor learning or motor rehabilitation. In the absence of any objective physiological method of measurement, it is currently impossible to be sure that the patient is indeed performing the task as instructed. Eye gaze recording during a motor imagery task could be a possible way to "spy" on the activity an individual is really engaged in. The aim of the present study was to compare the pattern of eye movement metrics during motor observation, visual and kinesthetic motor imagery (VI, KI), target fixation, and mental calculation. Twenty-two healthy subjects (16 females and 6 males), were required to perform tests in five conditions using imagery in the Box and Block Test tasks following the procedure described by Liepert et al. Eye movements were analysed by a non-invasive oculometric measure (SMI RED250 system). Two parameters describing gaze pattern were calculated: the index of ocular mobility (saccade duration over saccade + fixation duration) and the number of midline crossings (i.e. the number of times the subjects gaze crossed the midline of the screen when performing the different tasks). Both parameters were significantly different between visual imagery and kinesthesic imagery, visual imagery and mental calculation, and visual imagery and target fixation. For the first time we were able to show that eye movement patterns are different during VI and KI tasks. Our results suggest gaze metric parameters could be used as an objective unobtrusive approach to assess engagement in a motor imagery task. Further studies should define how oculomotor parameters could be used as an indicator of the rehabilitation task a patient is engaged in.

Fine and gross motor skills: The effects on skill-focused dual-tasks.

PubMed

Raisbeck, Louisa D; Diekfuss, Jed A

2015-10-01

Dual-task methodology often directs participants' attention towards a gross motor skill involved in the execution of a skill, but researchers have not investigated the comparative effects of attention on fine motor skill tasks. Furthermore, there is limited information about participants' subjective perception of workload with respect to task performance. To examine this, the current study administered the NASA-Task Load Index following a simulated shooting dual-task. The task required participants to stand 15 feet from a projector screen which depicted virtual targets and fire a modified Glock 17 handgun equipped with an infrared laser. Participants performed the primary shooting task alone (control), or were also instructed to focus their attention on a gross motor skill relevant to task execution (gross skill-focused) and a fine motor skill relevant to task execution (fine skill-focused). Results revealed that workload was significantly greater during the fine skill-focused task for both skill levels, but performance was only affected for the lesser-skilled participants. Shooting performance for the lesser-skilled participants was greater during the gross skill-focused condition compared to the fine skill-focused condition. Correlational analyses also demonstrated a significant negative relationship between shooting performance and workload during the gross skill-focused task for the higher-skilled participants. A discussion of the relationship between skill type, workload, skill level, and performance in dual-task paradigms is presented. Copyright © 2015 Elsevier B.V. All rights reserved.

Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study.

PubMed

Gatti, R; Tettamanti, A; Gough, P M; Riboldi, E; Marinoni, L; Buccino, G

2013-04-12

Both motor imagery and action observation have been shown to play a role in learning or re-learning complex motor tasks. According to a well accepted view they share a common neurophysiological basis in the mirror neuron system. Neurons within this system discharge when individuals perform a specific action and when they look at another individual performing the same or a motorically related action. In the present paper, after a short review of literature on the role of action observation and motor imagery in motor learning, we report the results of a kinematics study where we directly compared motor imagery and action observation in learning a novel complex motor task. This involved movement of the right hand and foot in the same angular direction (in-phase movement), while at the same time moving the left hand and foot in an opposite angular direction (anti-phase movement), all at a frequency of 1Hz. Motor learning was assessed through kinematics recording of wrists and ankles. The results showed that action observation is better than motor imagery as a strategy for learning a novel complex motor task, at least in the fast early phase of motor learning. We forward that these results may have important implications in educational activities, sport training and neurorehabilitation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Normalized Index of Synergy for Evaluating the Coordination of Motor Commands

PubMed Central

Togo, Shunta; Imamizu, Hiroshi

2015-01-01

Humans perform various motor tasks by coordinating the redundant motor elements in their bodies. The coordination of motor outputs is produced by motor commands, as well properties of the musculoskeletal system. The aim of this study was to dissociate the coordination of motor commands from motor outputs. First, we conducted simulation experiments where the total elbow torque was generated by a model of a simple human right and left elbow with redundant muscles. The results demonstrated that muscle tension with signal-dependent noise formed a coordinated structure of trial-to-trial variability of muscle tension. Therefore, the removal of signal-dependent noise effects was required to evaluate the coordination of motor commands. We proposed a method to evaluate the coordination of motor commands, which removed signal-dependent noise from the measured variability of muscle tension. We used uncontrolled manifold analysis to calculate a normalized index of synergy. Simulation experiments confirmed that the proposed method could appropriately represent the coordinated structure of the variability of motor commands. We also conducted experiments in which subjects performed the same task as in the simulation experiments. The normalized index of synergy revealed that the subjects coordinated their motor commands to achieve the task. Finally, the normalized index of synergy was applied to a motor learning task to determine the utility of the proposed method. We hypothesized that a large part of the change in the coordination of motor outputs through learning was because of changes in motor commands. In a motor learning task, subjects tracked a target trajectory of the total torque. The change in the coordination of muscle tension through learning was dominated by that of motor commands, which supported the hypothesis. We conclude that the normalized index of synergy can be used to evaluate the coordination of motor commands independently from the properties of the musculoskeletal system. PMID:26474043

Classification of EEG signals to identify variations in attention during motor task execution.

PubMed

Aliakbaryhosseinabadi, Susan; Kamavuako, Ernest Nlandu; Jiang, Ning; Farina, Dario; Mrachacz-Kersting, Natalie

2017-06-01

Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user's attention drift due to internal or external factors is essential for high detection accuracy. An auditory oddball task was applied to divert the users' attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution. Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes located over the motor cortex. Motor cortex channels had a higher accuracy in comparison to other channels in the global discrimination of attention level. Previous methods have used the attention to a task to drive external devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift while performing a motor task. It is possible to explore user's attention variation when performing motor tasks in synchronous BCI systems with time-frequency features. This is the first step towards an adaptive real-time BCI with an integrated function to reveal attention shifts from the motor task. Copyright © 2017 Elsevier B.V. All rights reserved.

Discerning measures of conscious brain processes associated with superior early motor performance: Capacity, coactivation, and character.

PubMed

van Duijn, Tina; Buszard, Tim; Hoskens, Merel C J; Masters, Rich S W

2017-01-01

This study explored the relationship between working memory (WM) capacity, corticocortical communication (EEG coherence), and propensity for conscious control of movement during the performance of a complex far-aiming task. We were specifically interested in the role of these variables in predicting motor performance by novices. Forty-eight participants completed (a) an assessment of WM capacity (an adapted Rotation Span task), (b) a questionnaire that assessed the propensity to consciously control movement (the Movement Specific Reinvestment Scale), and (c) a hockey push-pass task. The hockey push-pass task was performed in a single task (movement only) condition and a combined task (movement plus decision) condition. Electroencephalography (EEG) was used to examine brain activity during the single task. WM capacity best predicted single task performance. WM capacity in combination with T8-Fz coherence (between the visuospatial and motor regions of the brain) best predicted combined task performance. We discuss the implied roles of visuospatial information processing capacity, neural coactivation, and propensity for conscious processing during performance of complex motor tasks. © 2017 Elsevier B.V. All rights reserved.

Disentangling fine motor skills' relations to academic achievement: the relative contributions of visual-spatial integration and visual-motor coordination.

PubMed

Carlson, Abby G; Rowe, Ellen; Curby, Timothy W

2013-01-01

Recent research has established a connection between children's fine motor skills and their academic performance. Previous research has focused on fine motor skills measured prior to elementary school, while the present sample included children ages 5-18 years old, making it possible to examine whether this link remains relevant throughout childhood and adolescence. Furthermore, the majority of research linking fine motor skills and academic achievement has not determined which specific components of fine motor skill are driving this relation. The few studies that have looked at associations of separate fine motor tasks with achievement suggest that copying tasks that tap visual-spatial integration skills are most closely related to achievement. The present study examined two separate elements of fine motor skills--visual-motor coordination and visual-spatial integration--and their associations with various measures of academic achievement. Visual-motor coordination was measured using tracing tasks, while visual-spatial integration was measured using copy-a-figure tasks. After controlling for gender, socioeconomic status, IQ, and visual-motor coordination, and visual-spatial integration explained significant variance in children's math and written expression achievement. Knowing that visual-spatial integration skills are associated with these two achievement domains suggests potential avenues for targeted math and writing interventions for children of all ages.

Psychosocial Modulators of Motor Learning in Parkinson’s Disease

PubMed Central

Zemankova, Petra; Lungu, Ovidiu; Bares, Martin

2016-01-01

Using the remarkable overlap between brain circuits affected in Parkinson’s disease (PD) and those underlying motor sequence learning, we may improve the effectiveness of motor rehabilitation interventions by identifying motor learning facilitators in PD. For instance, additional sensory stimulation and task cueing enhanced motor learning in people with PD, whereas exercising using musical rhythms or console computer games improved gait and balance, and reduced some motor symptoms, in addition to increasing task enjoyment. Yet, despite these advances, important knowledge gaps remain. Most studies investigating motor learning in PD used laboratory-specific tasks and equipment, with little resemblance to real life situations. Thus, it is unknown whether similar results could be achieved in more ecological setups and whether individual’s task engagement could further improve motor learning capacity. Moreover, the role of social interaction in motor skill learning process has not yet been investigated in PD and the role of mind-set and self-regulatory mechanisms have been sporadically examined. Here, we review evidence suggesting that these psychosocial factors may be important modulators of motor learning in PD. We propose their incorporation in future research, given that it could lead to development of improved non-pharmacological interventions aimed to preserve or restore motor function in PD. PMID:26973495

Obsessive-compulsive disorder: a disorder of pessimal (non-functional) motor behavior.

PubMed

Zor, R; Keren, H; Hermesh, H; Szechtman, H; Mort, J; Eilam, D

2009-10-01

To determine whether in addition to repetitiveness, the motor rituals of patients with obsessive-compulsive disorder (OCD) involve reduced functionality due to numerous and measurable acts that are irrelevant and unnecessary for task completion. Comparing motor rituals of OCD patients with behavior of non-patient control individuals who were instructed to perform the same motor task. Obsessive-compulsive disorder behavior comprises abundant acts that were not performed by the controls. These acts seem unnecessary or even irrelevant for the task that the patients were performing, and therefore are termed 'non-functional'. Non-functional acts comprise some 60% of OCD motor behavior. Moreover, OCD behavior consists of short chains of functional acts bounded by long chains of non-functional acts. The abundance of irrelevant or unnecessary acts in OCD motor rituals represents reduced functionality in terms of task completion, typifying OCD rituals as pessimal behavior (antonym of optimal behavior).

Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys

PubMed Central

Ifft, Peter J.; Shokur, Solaiman; Li, Zheng; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2014-01-01

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to severely paralyzed patients. However, previous BMIs enabled only single arm functionality, and control of bimanual movements was a major challenge. Here, we developed and tested a bimanual BMI that enabled rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374–497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a 5th order unscented Kalman filter (UKF). The UKF is well-suited for BMI decoding because it accounts for both characteristics of reaching movements and their representation by cortical neurons. The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals’ performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. PMID:24197735

Bioinspired sensory systems for local flow characterization

NASA Astrophysics Data System (ADS)

Colvert, Brendan; Chen, Kevin; Kanso, Eva

2016-11-01

Empirical evidence suggests that many aquatic organisms sense differential hydrodynamic signals.This sensory information is decoded to extract relevant flow properties. This task is challenging because it relies on local and partial measurements, whereas classical flow characterization methods depend on an external observer to reconstruct global flow fields. Here, we introduce a mathematical model in which a bioinspired sensory array measuring differences in local flow velocities characterizes the flow type and intensity. We linearize the flow field around the sensory array and express the velocity gradient tensor in terms of frame-independent parameters. We develop decoding algorithms that allow the sensory system to characterize the local flow and discuss the conditions under which this is possible. We apply this framework to the canonical problem of a circular cylinder in uniform flow, finding excellent agreement between sensed and actual properties. Our results imply that combining suitable velocity sensors with physics-based methods for decoding sensory measurements leads to a powerful approach for understanding and developing underwater sensory systems.

Unsupervised learning of facial emotion decoding skills.

PubMed

Huelle, Jan O; Sack, Benjamin; Broer, Katja; Komlewa, Irina; Anders, Silke

2014-01-01

Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practice without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear, and sadness) was shown in each clip. Although no external information about the correctness of the participant's response or the sender's true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple stimuli described in previous studies and practice effects often observed in cognitive tasks.

Unsupervised learning of facial emotion decoding skills

PubMed Central

Huelle, Jan O.; Sack, Benjamin; Broer, Katja; Komlewa, Irina; Anders, Silke

2013-01-01

Research on the mechanisms underlying human facial emotion recognition has long focussed on genetically determined neural algorithms and often neglected the question of how these algorithms might be tuned by social learning. Here we show that facial emotion decoding skills can be significantly and sustainably improved by practice without an external teaching signal. Participants saw video clips of dynamic facial expressions of five different women and were asked to decide which of four possible emotions (anger, disgust, fear, and sadness) was shown in each clip. Although no external information about the correctness of the participant’s response or the sender’s true affective state was provided, participants showed a significant increase of facial emotion recognition accuracy both within and across two training sessions two days to several weeks apart. We discuss several similarities and differences between the unsupervised improvement of facial decoding skills observed in the current study, unsupervised perceptual learning of simple visual stimuli described in previous studies and practice effects often observed in cognitive tasks. PMID:24578686

Neural signatures of attention: insights from decoding population activity patterns.

PubMed

Sapountzis, Panagiotis; Gregoriou, Georgia G

2018-01-01

Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis. Recent studies have employed machine-learning algorithms in attention and other cognitive tasks to decode the information content of distributed activity patterns across neuronal ensembles on a single trial basis. Here, we review results from studies that have used pattern-classification decoding approaches to explore the population representation of cognitive functions. These studies have offered significant insights into population coding mechanisms. Moreover, we discuss how such advances can aid the development of cognitive brain-computer interfaces.

Developments in brain-machine interfaces from the perspective of robotics.

PubMed

Kim, Hyun K; Park, Shinsuk; Srinivasan, Mandayam A

2009-04-01

Many patients suffer from the loss of motor skills, resulting from traumatic brain and spinal cord injuries, stroke, and many other disabling conditions. Thanks to technological advances in measuring and decoding the electrical activity of cortical neurons, brain-machine interfaces (BMI) have become a promising technology that can aid paralyzed individuals. In recent studies on BMI, robotic manipulators have demonstrated their potential as neuroprostheses. Restoring motor skills through robot manipulators controlled by brain signals may improve the quality of life of people with disability. This article reviews current robotic technologies that are relevant to BMI and suggests strategies that could improve the effectiveness of a brain-operated neuroprosthesis through robotics.

Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

NASA Astrophysics Data System (ADS)

Laubach, Mark; Wessberg, Johan; Nicolelis, Miguel A. L.

2000-06-01

When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

Quantitative Motor Performance and Sleep Benefit in Parkinson Disease.

PubMed

van Gilst, Merel M; van Mierlo, Petra; Bloem, Bastiaan R; Overeem, Sebastiaan

2015-10-01

Many people with Parkinson disease experience "sleep benefit": temporarily improved mobility upon awakening. Here we used quantitative motor tasks to assess the influence of sleep on motor functioning in Parkinson disease. Eighteen Parkinson patients with and 20 without subjective sleep benefit and 20 healthy controls participated. Before and directly after a regular night sleep and an afternoon nap, subjects performed the timed pegboard dexterity task and quantified finger tapping task. Subjective ratings of motor functioning and mood/vigilange were included. Sleep was monitored using polysomnography. On both tasks, patients were overall slower than healthy controls (night: F2,55 = 16.938, P < 0.001; nap: F2,55 = 15.331, P < 0.001). On the pegboard task, there was a small overall effect of night sleep (F1,55 = 9.695, P = 0.003); both patients and controls were on average slightly slower in the morning. However, in both tasks there was no sleep*group interaction for nighttime sleep nor for afternoon nap. There was a modest correlation between the score on the pegboard task and self-rated motor symptoms among patients (rho = 0.233, P = 0.004). No correlations in task performance and mood/vigilance or sleep time/efficiency were found. A positive effect of sleep on motor function is commonly reported by Parkinson patients. Here we show that the subjective experience of sleep benefit is not paralleled by an actual improvement in motor functioning. Sleep benefit therefore appears to be a subjective phenomenon and not a Parkinson-specific reduction in symptoms. © 2015 Associated Professional Sleep Societies, LLC.

Evolution of brain-computer interfaces: going beyond classic motor physiology

PubMed Central

Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W.

2010-01-01

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future. PMID:19569892

Decoding human swallowing via electroencephalography: a state-of-the-art review

PubMed Central

Jestrović, Iva; Coyle, James L.

2015-01-01

Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG. PMID:26372528

Motor Timing Deficits in Sequential Movements in Parkinson Disease Are Related to Action Planning: A Motor Imagery Study

PubMed Central

Avanzino, Laura; Pelosin, Elisa; Martino, Davide; Abbruzzese, Giovanni

2013-01-01

Timing of sequential movements is altered in Parkinson disease (PD). Whether timing deficits in internally generated sequential movements in PD depends also on difficulties in motor planning, rather than merely on a defective ability to materially perform the planned movement is still undefined. To unveil this issue, we adopted a modified version of an established test for motor timing, i.e. the synchronization–continuation paradigm, by introducing a motor imagery task. Motor imagery is thought to involve mainly processes of movement preparation, with reduced involvement of end-stage movement execution-related processes. Fourteen patients with PD and twelve matched healthy volunteers were asked to tap in synchrony with a metronome cue (SYNC) and then, when the tone stopped, to keep tapping, trying to maintain the same rhythm (CONT-EXE) or to imagine tapping at the same rhythm, rather than actually performing it (CONT-MI). We tested both a sub-second and a supra-second inter-stimulus interval between the cues. Performance was recorded using a sensor-engineered glove and analyzed measuring the temporal error and the interval reproduction accuracy index. PD patients were less accurate than healthy subjects in the supra-second time reproduction task when performing both continuation tasks (CONT-MI and CONT-EXE), whereas no difference was detected in the synchronization task and on all tasks involving a sub-second interval. Our findings suggest that PD patients exhibit a selective deficit in motor timing for sequential movements that are separated by a supra-second interval and that this deficit may be explained by a defect of motor planning. Further, we propose that difficulties in motor planning are of a sufficient degree of severity in PD to affect also the motor performance in the supra-second time reproduction task. PMID:24086534

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment

PubMed Central

Faller, Josef; Scherer, Reinhold; Friedrich, Elisabeth V. C.; Costa, Ursula; Opisso, Eloy; Medina, Josep; Müller-Putz, Gernot R.

2014-01-01

Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks (“SMR-AdBCI”) have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (“Auto-AdBCI”) could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%). PMID:25368546

Core stability exercise is as effective as task-oriented motor training in improving motor proficiency in children with developmental coordination disorder: a randomized controlled pilot study.

PubMed

Au, Mei K; Chan, Wai M; Lee, Lin; Chen, Tracy Mk; Chau, Rosanna Mw; Pang, Marco Yc

2014-10-01

To compare the effectiveness of a core stability program with a task-oriented motor training program in improving motor proficiency in children with developmental coordination disorder (DCD). Randomized controlled pilot trial. Outpatient unit in a hospital. Twenty-two children diagnosed with DCD aged 6-9 years were randomly allocated to the core stability program or the task-oriented motor program. Both groups underwent their respective face-to-face training session once per week for eight consecutive weeks. They were also instructed to carry out home exercises on a daily basis during the intervention period. Short Form of the Bruininks-Oseretsky Test of Motor Proficiency (Second Edition) and Sensory Organization Test at pre- and post-intervention. Intention-to-treat analysis revealed no significant between-group difference in the change of motor proficiency standard score (P=0.717), and composite equilibrium score derived from the Sensory Organization Test (P=0.100). Further analysis showed significant improvement in motor proficiency in both the core stability (mean change (SD)=6.3(5.4); p=0.008) and task-oriented training groups (mean change(SD)=5.1(4.0); P=0.007). The composite equilibrium score was significantly increased in the task-oriented training group (mean change (SD)=6.0(5.5); P=0.009), but not in the core stability group (mean change(SD) =0.0(9.6); P=0.812). In the task-oriented training group, compliance with the home program was positively correlated with change in motor proficiency (ρ=0.680, P=0.030) and composite equilibrium score (ρ=0.638, P=0.047). The core stability exercise program is as effective as task-oriented training in improving motor proficiency among children with DCD. © The Author(s) 2014.

From Children to Adults: Motor Performance across the Life-Span

PubMed Central

Leversen, Jonas S. R.; Haga, Monika; Sigmundsson, Hermundur

2012-01-01

The life-span approach to development provides a theoretical framework to examine the general principles of life-long development. This study aims to investigate motor performance across the life span. It also aims to investigate if the correlations between motor tasks increase with aging. A cross-sectional design was used to describe the effects of aging on motor performance across age groups representing individuals from childhood to young adult to old age. Five different motor tasks were used to study changes in motor performance within 338 participants (7–79 yrs). Results showed that motor performance increases from childhood (7–9) to young adulthood (19–25) and decreases from young adulthood (19–25) to old age (66–80). These results are mirroring results from cognitive research. Correlation increased with increasing age between two fine motor tasks and two gross motor tasks. We suggest that the findings might be explained, in part, by the structural changes that have been reported to occur in the developing and aging brain and that the theory of Neural Darwinism can be used as a framework to explain why these changes occur. PMID:22719958

Impaired motor inhibition in adults who stutter - evidence from speech-free stop-signal reaction time tasks.

PubMed

Markett, Sebastian; Bleek, Benjamin; Reuter, Martin; Prüss, Holger; Richardt, Kirsten; Müller, Thilo; Yaruss, J Scott; Montag, Christian

2016-10-01

Idiopathic stuttering is a fluency disorder characterized by impairments during speech production. Deficits in the motor control circuits of the basal ganglia have been implicated in idiopathic stuttering but it is unclear how these impairments relate to the disorder. Previous work has indicated a possible deficiency in motor inhibition in children who stutter. To extend these findings to adults, we designed two experiments to probe executive motor control in people who stutter using manual reaction time tasks that do not rely on speech production. We used two versions of the stop-signal reaction time task, a measure for inhibitory motor control that has been shown to rely on the basal ganglia circuits. We show increased stop-signal reaction times in two independent samples of adults who stutter compared to age- and sex-matched control groups. Additional measures involved simple reaction time measurements and a task-switching task where no group difference was detected. Results indicate a deficiency in inhibitory motor control in people who stutter in a task that does not rely on overt speech production and cannot be explained by general deficits in executive control or speeded motor execution. This finding establishes the stop-signal reaction time as a possible target for future experimental and neuroimaging studies on fluency disorders and is a further step towards unraveling the contribution of motor control deficits to idiopathic stuttering. Copyright © 2016 Elsevier Ltd. All rights reserved.

Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance.

PubMed

Baxter, Bryan S; Edelman, Bradley J; Nesbitt, Nicholas; He, Bin

Transcranial direct current stimulation (tDCS) has been used to alter the excitability of neurons within the cerebral cortex. Improvements in motor learning have been found in multiple studies when tDCS was applied to the motor cortex before or during task learning. The motor cortex is also active during the performance of motor imagination, a cognitive task during which a person imagines, but does not execute, a movement. Motor imagery can be used with noninvasive brain computer interfaces (BCIs) to control virtual objects in up to three dimensions, but to master control of such devices requires long training times. To evaluate the effect of high-definition tDCS on the performance and underlying electrophysiology of motor imagery based BCI. We utilize high-definition tDCS to investigate the effect of stimulation on motor imagery-based BCI performance across and within sessions over multiple training days. We report a decreased time-to-hit with anodal stimulation both within and across sessions. We also found differing electrophysiological changes of the stimulated sensorimotor cortex during online BCI task performance for left vs. right trials. Cathodal stimulation led to a decrease in alpha and beta band power during task performance compared to sham stimulation for right hand imagination trials. These results suggest that unilateral tDCS over the sensorimotor motor cortex differentially affects cortical areas based on task specific neural activation. Copyright © 2016 Elsevier Inc. All rights reserved.

Mental workload and motor performance dynamics during practice of reaching movements under various levels of task difficulty.

PubMed

Shuggi, Isabelle M; Oh, Hyuk; Shewokis, Patricia A; Gentili, Rodolphe J

2017-09-30

The assessment of mental workload can inform attentional resource allocation during task performance that is essential for understanding the underlying principles of human cognitive-motor behavior. While many studies have focused on mental workload in relation to human performance, a modest body of work has examined it in a motor practice/learning context without considering individual variability. Thus, this work aimed to examine mental workload by employing the NASA TLX as well as the changes in motor performance resulting from the practice of a novel reaching task. Two groups of participants practiced a reaching task at a high and low nominal difficulty during which a group-level analysis assessed the mental workload, motor performance and motor improvement dynamics. A secondary cluster analysis was also conducted to identify specific individual patterns of cognitive-motor responses. Overall, both group- and cluster-level analyses revealed that: (i) all participants improved their performance throughout motor practice, and (ii) an increase in mental workload was associated with a reduction of the quality of motor performance along with a slower rate of motor improvement. The results are discussed in the context of the optimal challenge point framework and in particular it is proposed that under the experimental conditions employed here, functional task difficulty: (i) would possibly depend on an individuals' information processing capabilities, and (ii) could be indexed by the level of mental workload which, when excessively heightened can decrease the quality of performance and more generally result in delayed motor improvements. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Rewards-driven control of robot arm by decoding EEG signals.

PubMed

Tanwani, Ajay Kumar; del R Millan, Jose; Billard, Aude

2014-01-01

Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80% accuracy of reaching the actual goal.

Perfect quantum multiple-unicast network coding protocol

NASA Astrophysics Data System (ADS)

Li, Dan-Dan; Gao, Fei; Qin, Su-Juan; Wen, Qiao-Yan

2018-01-01

In order to realize long-distance and large-scale quantum communication, it is natural to utilize quantum repeater. For a general quantum multiple-unicast network, it is still puzzling how to complete communication tasks perfectly with less resources such as registers. In this paper, we solve this problem. By applying quantum repeaters to multiple-unicast communication problem, we give encoding-decoding schemes for source nodes, internal ones and target ones, respectively. Source-target nodes share EPR pairs by using our encoding-decoding schemes over quantum multiple-unicast network. Furthermore, quantum communication can be accomplished perfectly via teleportation. Compared with existed schemes, our schemes can reduce resource consumption and realize long-distance transmission of quantum information.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

PubMed

Wu, Howard G; Miyamoto, Yohsuke R; Gonzalez Castro, Luis Nicolas; Ölveczky, Bence P; Smith, Maurice A

2014-02-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

PubMed Central

Wu, Howard G; Miyamoto, Yohsuke R; Castro, Luis Nicolas Gonzalez; Ölveczky, Bence P; Smith, Maurice A

2015-01-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning. PMID:24413700

The Effect of Self-Regulated and Experimenter-Imposed Practice Schedules on Motor Learning for Tasks of Varying Difficulty

ERIC Educational Resources Information Center

Keetch, Katherine M.; Lee, Timothy D.

2007-01-01

Research suggests that allowing individuals to control their own practice schedule has a positive effect on motor learning. In this experiment we examined the effect of task difficulty and self-regulated practice strategies on motor learning. The task was to move a mouse-operated cursor through pattern arrays that differed in two levels of…

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

PubMed

Friedenberg, David A; Bouton, Chad E; Annetta, Nicholas V; Skomrock, Nicholas; Mingming Zhang; Schwemmer, Michael; Bockbrader, Marcia A; Mysiw, W Jerry; Rezai, Ali R; Bresler, Herbert S; Sharma, Gaurav

2016-08-01

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

Motor-commands decoding using peripheral nerve signals: a review

NASA Astrophysics Data System (ADS)

Hong, Keum-Shik; Aziz, Nida; Ghafoor, Usman

2018-06-01

During the last few decades, substantial scientific and technological efforts have been focused on the development of neuroprostheses. The major emphasis has been on techniques for connecting the human nervous system with a robotic prosthesis via natural-feeling interfaces. The peripheral nerves provide access to highly processed and segregated neural command signals from the brain that can in principle be used to determine user intent and control muscles. If these signals could be used, they might allow near-natural and intuitive control of prosthetic limbs with multiple degrees of freedom. This review summarizes the history of neuroprosthetic interfaces and their ability to record from and stimulate peripheral nerves. We also discuss the types of interfaces available and their applications, the kinds of peripheral nerve signals that are used, and the algorithms used to decode them. Finally, we explore the prospects for future development in this area.

Abnormal functional motor lateralization in healthy siblings of patients with schizophrenia.

PubMed

Altamura, Mario; Fazio, Leonardo; De Salvia, Michela; Petito, Annamaria; Blasi, Giuseppe; Taurisano, Paolo; Romano, Raffaella; Gelao, Barbara; Bellomo, Antonello; Bertolino, Alessandro

2012-07-30

Earlier neuroimaging studies of motor function in schizophrenia have demonstrated reduced functional lateralization in the motor network during motor tasks. Here, we used event-related functional magnetic resonance imaging during a visually guided motor task in 18 clinically unaffected siblings of patients with schizophrenia and 24 matched controls to investigate if abnormal functional lateralization is related to genetic risk for this brain disorder. Whereas activity associated with motor task performance was mainly contralateral with only a marginal ipsilateral component in healthy participants, unaffected siblings had strong bilateral activity with significantly greater response in ipsilateral and contralateral premotor areas as well as in contralateral subcortical motor regions relative to controls. Reduced lateralization in siblings was also identified with a measure of laterality quotient. These findings suggest that abnormal functional lateralization of motor circuitry is related to genetic risk of schizophrenia. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Behavioral decoding of working memory items inside and outside the focus of attention.

PubMed

Mallett, Remington; Lewis-Peacock, Jarrod A

2018-03-31

How we attend to our thoughts affects how we attend to our environment. Holding information in working memory can automatically bias visual attention toward matching information. By observing attentional biases on reaction times to visual search during a memory delay, it is possible to reconstruct the source of that bias using machine learning techniques and thereby behaviorally decode the content of working memory. Can this be done when more than one item is held in working memory? There is some evidence that multiple items can simultaneously bias attention, but the effects have been inconsistent. One explanation may be that items are stored in different states depending on the current task demands. Recent models propose functionally distinct states of representation for items inside versus outside the focus of attention. Here, we use behavioral decoding to evaluate whether multiple memory items-including temporarily irrelevant items outside the focus of attention-exert biases on visual attention. Only the single item in the focus of attention was decodable. The other item showed a brief attentional bias that dissipated until it returned to the focus of attention. These results support the idea of dynamic, flexible states of working memory across time and priority. © 2018 New York Academy of Sciences.

Development of motor speed and associated movements from 5 to 18 years.

PubMed

Gasser, Theo; Rousson, Valentin; Caflisch, Jon; Jenni, Oskar G

2010-03-01

To study the development of motor speed and associated movements in participants aged 5 to 18 years for age, sex, and laterality. Ten motor tasks of the Zurich Neuromotor Assessment (repetitive and alternating movements of hands and feet, repetitive and sequential finger movements, the pegboard, static and dynamic balance, diadochokinesis) were administered to 593 right-handed participants (286 males, 307 females). A strong improvement with age was observed in motor speed from age 5 to 10, followed by a levelling-off between 12 and 18 years. Simple tasks and the pegboard matured early and complex tasks later. Simple tasks showed no associated movements beyond early childhood; in complex tasks associated movements persisted until early adulthood. The two sexes differed only marginally in speed, but markedly in associated movements. A significant laterality (p<0.001) in speed was found for all tasks except for static balance; the pegboard was most lateralized, and sequential finger movements least. Associated movements were lateralized only for a few complex tasks. We also noted a substantial interindividual variability. Motor speed and associated movements improve strongly in childhood, weakly in adolescence, and are both of developmental relevance. Because they correlate weakly, they provide complementary information.

Transcranial direct current stimulation (tDCS) to the supplementary motor area (SMA) influences performance on motor tasks.

PubMed

Hupfeld, K E; Ketcham, C J; Schneider, H D

2017-03-01

The supplementary motor area (SMA) is believed to be highly involved in the planning and execution of both simple and complex motor tasks. This study aimed to examine the role of the SMA in planning the movements required to complete reaction time, balance, and pegboard tasks using anodal transcranial direct current stimulation (tDCS), which passes a weak electrical current between two electrodes, in order to modulate neuronal activity. Twenty healthy adults were counterbalanced to receive either tDCS (experimental condition) or no tDCS (control condition) for 3 days. During administration of tDCS, participants performed a balance task significantly faster than controls. After tDCS, subjects significantly improved their simple and choice reaction time. These results demonstrate that the SMA is highly involved in planning and executing fine and gross motor skill tasks and that tDCS is an effective modality for increasing SMA-related performance on these tasks. The findings may be generalizable and therefore indicate implications for future interventions using tDCS as a therapeutic tool.

Task-dependent activation of distinct fast and slow(er) motor pathways during motor imagery.

PubMed

Keller, Martin; Taube, Wolfgang; Lauber, Benedikt

2018-02-22

Motor imagery and actual movements share overlapping activation of brain areas but little is known about task-specific activation of distinct motor pathways during mental simulation of movements. For real contractions, it was demonstrated that the slow(er) motor pathways are activated differently in ballistic compared to tonic contractions but it is unknown if this also holds true for imagined contractions. The aim of the present study was to assess the activity of fast and slow(er) motor pathways during mentally simulated movements of ballistic and tonic contractions. H-reflexes were conditioned with transcranial magnetic stimulation at different interstimulus intervals to assess the excitability of fast and slow(er) motor pathways during a) the execution of tonic and ballistic contractions, b) motor imagery of these contraction types, and c) at rest. In contrast to the fast motor pathways, the slow(er) pathways displayed a task-specific activation: for imagined ballistic as well as real ballistic contractions, the activation was reduced compared to rest whereas enhanced activation was found for imagined tonic and real tonic contractions. This study provides evidence that the excitability of fast and slow(er) motor pathways during motor imagery resembles the activation pattern observed during real contractions. The findings indicate that motor imagery results in task- and pathway-specific subliminal activation of distinct subsets of neurons in the primary motor cortex. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Neuropsychological Investigation of Motor Impairments in Autism

PubMed Central

Duffield, Tyler; Trontel, Haley; Bigler, Erin D.; Froehlich, Alyson; Prigge, Molly B.; Travers, Brittany; Green, Ryan R.; Cariello, Annahir N.; Cooperrider, Jason; Nielsen, Jared; Alexander, Andrew; Anderson, Jeffrey; Fletcher, P. Thomas; Lange, Nicholas; Zielinski, Brandon; Lainhart, Janet

2013-01-01

It is unclear how standardized neuropsychological measures of motor function relate to brain volumes of motor regions in autism spectrum disorder (ASD). An all male sample composed of 59 ASD and 30 controls (ages 5–33 years) completed three measures of motor function: strength of grip (SOG), finger tapping test (FTT), and grooved peg-board test (GPT). Likewise, all participants underwent magnetic resonance imaging with region of interest (ROI) volumes obtained to include the following regions: motor cortex (pre-central gyrus), somatosensory cortex (post-central gyrus), thalamus, basal ganglia, cerebellum and caudal middle frontal gyrus. These traditional neuropsychological measures of motor function are assumed to differ in motor complexity with GPT requiring the most followed by FTT and SOG. Performance by ASD participants on the GPT and FTT differed significantly from controls, with the largest effect size differences observed on the more complex GPT task. Differences on the SOG task between the two groups were non-significant. Since more complex motor tasks tap more complex networks, poorer GPT performance by those with ASD may reflect less efficient motor networks. There was no gross pathology observed in classic motor areas of the brain in ASD, as region of interest (ROI) volumes did not differ, but FTT was negatively related to motor cortex volume in ASD. The results suggest a hierarchical motor disruption in ASD, with difficulties evident only in more complex tasks as well as a potential anomalous size-function relation in motor cortex in ASD. PMID:23985036

The Effect of Haptic Guidance on Learning a Hybrid Rhythmic-Discrete Motor Task.

PubMed

Marchal-Crespo, Laura; Bannwart, Mathias; Riener, Robert; Vallery, Heike

2015-01-01

Bouncing a ball with a racket is a hybrid rhythmic-discrete motor task, combining continuous rhythmic racket movements with discrete impact events. Rhythmicity is exceptionally important in motor learning, because it underlies fundamental movements such as walking. Studies suggested that rhythmic and discrete movements are governed by different control mechanisms at different levels of the Central Nervous System. The aim of this study is to evaluate the effect of fixed/fading haptic guidance on learning to bounce a ball to a desired apex in virtual reality with varying gravity. Changing gravity changes dominance of rhythmic versus discrete control: The higher the value of gravity, the more rhythmic the task; lower values reduce the bouncing frequency and increase dwell times, eventually leading to a repetitive discrete task that requires initiation and termination, resembling target-oriented reaching. Although motor learning in the ball-bouncing task with varying gravity has been studied, the effect of haptic guidance on learning such a hybrid rhythmic-discrete motor task has not been addressed. We performed an experiment with thirty healthy subjects and found that the most effective training condition depended on the degree of rhythmicity: Haptic guidance seems to hamper learning of continuous rhythmic tasks, but it seems to promote learning for repetitive tasks that resemble discrete movements.

Short-term motor learning through non-immersive virtual reality task in individuals with down syndrome.

PubMed

de Mello Monteiro, Carlos Bandeira; da Silva, Talita Dias; de Abreu, Luiz Carlos; Fregni, Felipe; de Araujo, Luciano Vieira; Ferreira, Fernando Henrique Inocêncio Borba; Leone, Claudio

2017-04-14

Down syndrome (DS) has unique physical, motor and cognitive characteristics. Despite cognitive and motor difficulties, there is a possibility of intervention based on the knowledge of motor learning. However, it is important to study the motor learning process in individuals with DS during a virtual reality task to justify the use of virtual reality to organize intervention programs. The aim of this study was to analyze the motor learning process in individuals with DS during a virtual reality task. A total of 40 individuals participated in this study, 20 of whom had DS (24 males and 8 females, mean age of 19 years, ranging between 14 and 30 yrs.) and 20 typically developing individuals (TD) who were matched by age and gender to the individuals with DS. To examine this issue, we used software that uses 3D images and reproduced a coincidence-timing task. The results showed that all individuals improved performance in the virtual task, but the individuals with DS that started the task with worse performance showed higher difference from the beginning. Besides that, they were able to retain and transfer the performance with increase of speed of the task. Individuals with DS are able to learn movements from virtual tasks, even though the movement time was higher compared to the TD individuals. The results showed that individuals with DS who started with low performance improved coincidence- timing task with virtual objects, but were less accurate than typically developing individuals. ClinicalTrials.gov Identifier: NCT02719600 .

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.

PubMed

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

NASA Astrophysics Data System (ADS)

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Multi-Voxel Decoding and the Topography of Maintained Information During Visual Working Memory

PubMed Central

Lee, Sue-Hyun; Baker, Chris I.

2016-01-01

The ability to maintain representations in the absence of external sensory stimulation, such as in working memory, is critical for guiding human behavior. Human functional brain imaging studies suggest that visual working memory can recruit a network of brain regions from visual to parietal to prefrontal cortex. In this review, we focus on the maintenance of representations during visual working memory and discuss factors determining the topography of those representations. In particular, we review recent studies employing multi-voxel pattern analysis (MVPA) that demonstrate decoding of the maintained content in visual cortex, providing support for a “sensory recruitment” model of visual working memory. However, there is some evidence that maintained content can also be decoded in areas outside of visual cortex, including parietal and frontal cortex. We suggest that the ability to maintain representations during working memory is a general property of cortex, not restricted to specific areas, and argue that it is important to consider the nature of the information that must be maintained. Such information-content is critically determined by the task and the recruitment of specific regions during visual working memory will be both task- and stimulus-dependent. Thus, the common finding of maintained information in visual, but not parietal or prefrontal, cortex may be more of a reflection of the need to maintain specific types of visual information and not of a privileged role of visual cortex in maintenance. PMID:26912997

Visual and Haptic Shape Processing in the Human Brain: Unisensory Processing, Multisensory Convergence, and Top-Down Influences.

PubMed

Lee Masson, Haemy; Bulthé, Jessica; Op de Beeck, Hans P; Wallraven, Christian

2016-08-01

Humans are highly adept at multisensory processing of object shape in both vision and touch. Previous studies have mostly focused on where visually perceived object-shape information can be decoded, with haptic shape processing receiving less attention. Here, we investigate visuo-haptic shape processing in the human brain using multivoxel correlation analyses. Importantly, we use tangible, parametrically defined novel objects as stimuli. Two groups of participants first performed either a visual or haptic similarity-judgment task. The resulting perceptual object-shape spaces were highly similar and matched the physical parameter space. In a subsequent fMRI experiment, objects were first compared within the learned modality and then in the other modality in a one-back task. When correlating neural similarity spaces with perceptual spaces, visually perceived shape was decoded well in the occipital lobe along with the ventral pathway, whereas haptically perceived shape information was mainly found in the parietal lobe, including frontal cortex. Interestingly, ventrolateral occipito-temporal cortex decoded shape in both modalities, highlighting this as an area capable of detailed visuo-haptic shape processing. Finally, we found haptic shape representations in early visual cortex (in the absence of visual input), when participants switched from visual to haptic exploration, suggesting top-down involvement of visual imagery on haptic shape processing. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task.

PubMed

Du, Yue; Clark, Jane E

2018-05-03

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

A unifying motor control framework for task-specific dystonia

PubMed Central

Rothwell, John C.; Edwards, Mark J.

2018-01-01

Task-specific dystonia is a movement disorder characterized by the development of a painless loss of dexterity specific to a particular motor skill. This disorder is prevalent among writers, musicians, dancers and athletes. No current treatment is predictably effective and the disorder generally ends the careers of affected individuals. There are a number of limitations with traditional dystonic disease models for task-specific dystonia. We therefore review emerging evidence that the disorder has its origins within normal compensatory mechanisms of a healthy motor system in which the representation and reproduction of motor skill is disrupted. We describe how risk factors for task-specific dystonia can be stratified and translated into mechanisms of dysfunctional motor control. The proposed model aims to define new directions for experimental research and stimulate therapeutic advances for this highly disabling disorder. PMID:29104291

"Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

PubMed

Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

2018-05-01

This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

High variability impairs motor learning regardless of whether it affects task performance.

PubMed

Cardis, Marco; Casadio, Maura; Ranganathan, Rajiv

2018-01-01

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution. NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of variability was more critical than the dimension in which variability was introduced.

Recruitment of faster motor units is associated with greater rates of fascicle strain and rapid changes in muscle force during locomotion

PubMed Central

Lee, Sabrina S. M.; de Boef Miara, Maria; Arnold, Allison S.; Biewener, Andrew A.; Wakeling, James M.

2013-01-01

SUMMARY Animals modulate the power output needed for different locomotor tasks by changing muscle forces and fascicle strain rates. To generate the necessary forces, appropriate motor units must be recruited. Faster motor units have faster activation–deactivation rates than slower motor units, and they contract at higher strain rates; therefore, recruitment of faster motor units may be advantageous for tasks that involve rapid movements or high rates of work. This study identified motor unit recruitment patterns in the gastrocnemii muscles of goats and examined whether faster motor units are recruited when locomotor speed is increased. The study also examined whether locomotor tasks that elicit faster (or slower) motor units are associated with increased (or decreased) in vivo tendon forces, force rise and relaxation rates, fascicle strains and/or strain rates. Electromyography (EMG), sonomicrometry and muscle-tendon force data were collected from the lateral and medial gastrocnemius muscles of goats during level walking, trotting and galloping and during inclined walking and trotting. EMG signals were analyzed using wavelet and principal component analyses to quantify changes in the EMG frequency spectra across the different locomotor conditions. Fascicle strain and strain rate were calculated from the sonomicrometric data, and force rise and relaxation rates were determined from the tendon force data. The results of this study showed that faster motor units were recruited as goats increased their locomotor speeds from level walking to galloping. Slow inclined walking elicited EMG intensities similar to those of fast level galloping but different EMG frequency spectra, indicating that recruitment of the different motor unit types depended, in part, on characteristics of the task. For the locomotor tasks and muscles analyzed here, recruitment patterns were generally associated with in vivo fascicle strain rates, EMG intensity and tendon force. Together, these data provide new evidence that changes in motor unit recruitment have an underlying mechanical basis, at least for certain locomotor tasks. PMID:22972893

Recruitment of faster motor units is associated with greater rates of fascicle strain and rapid changes in muscle force during locomotion.

PubMed

Lee, Sabrina S M; de Boef Miara, Maria; Arnold, Allison S; Biewener, Andrew A; Wakeling, James M

2013-01-15

Animals modulate the power output needed for different locomotor tasks by changing muscle forces and fascicle strain rates. To generate the necessary forces, appropriate motor units must be recruited. Faster motor units have faster activation-deactivation rates than slower motor units, and they contract at higher strain rates; therefore, recruitment of faster motor units may be advantageous for tasks that involve rapid movements or high rates of work. This study identified motor unit recruitment patterns in the gastrocnemii muscles of goats and examined whether faster motor units are recruited when locomotor speed is increased. The study also examined whether locomotor tasks that elicit faster (or slower) motor units are associated with increased (or decreased) in vivo tendon forces, force rise and relaxation rates, fascicle strains and/or strain rates. Electromyography (EMG), sonomicrometry and muscle-tendon force data were collected from the lateral and medial gastrocnemius muscles of goats during level walking, trotting and galloping and during inclined walking and trotting. EMG signals were analyzed using wavelet and principal component analyses to quantify changes in the EMG frequency spectra across the different locomotor conditions. Fascicle strain and strain rate were calculated from the sonomicrometric data, and force rise and relaxation rates were determined from the tendon force data. The results of this study showed that faster motor units were recruited as goats increased their locomotor speeds from level walking to galloping. Slow inclined walking elicited EMG intensities similar to those of fast level galloping but different EMG frequency spectra, indicating that recruitment of the different motor unit types depended, in part, on characteristics of the task. For the locomotor tasks and muscles analyzed here, recruitment patterns were generally associated with in vivo fascicle strain rates, EMG intensity and tendon force. Together, these data provide new evidence that changes in motor unit recruitment have an underlying mechanical basis, at least for certain locomotor tasks.

Oral Motor Abilities Are Task Dependent: A Factor Analytic Approach to Performance Rate.

PubMed

Staiger, Anja; Schölderle, Theresa; Brendel, Bettina; Bötzel, Kai; Ziegler, Wolfram

2017-01-01

Measures of performance rates in speech-like or volitional nonspeech oral motor tasks are frequently used to draw inferences about articulation rate abnormalities in patients with neurologic movement disorders. The study objective was to investigate the structural relationship between rate measures of speech and of oral motor behaviors different from speech. A total of 130 patients with neurologic movement disorders and 130 healthy subjects participated in the study. Rate data was collected for oral reading (speech), rapid syllable repetition (speech-like), and rapid single articulator movements (nonspeech). The authors used factor analysis to determine whether the different rate variables reflect the same or distinct constructs. The behavioral data were most appropriately captured by a measurement model in which the different task types loaded onto separate latent variables. The data on oral motor performance rates show that speech tasks and oral motor tasks such as rapid syllable repetition or repetitive single articulator movements measure separate traits.

Distinct motor impairments of dopamine D1 and D2 receptor knockout mice revealed by three types of motor behavior

PubMed Central

Nakamura, Toru; Sato, Asako; Kitsukawa, Takashi; Momiyama, Toshihiko; Yamamori, Tetsuo; Sasaoka, Toshikuni

2014-01-01

Both D1R and D2R knock out (KO) mice of the major dopamine receptors show significant motor impairments. However, there are some discrepant reports, which may be due to the differences in genetic background and experimental procedures. In addition, only few studies directly compared the motor performance of D1R and D2R KO mice. In this paper, we examined the behavioral difference among N10 congenic D1R and D2R KO, and wild type (WT) mice. First, we examined spontaneous motor activity in the home cage environment for consecutive 5 days. Second, we examined motor performance using the rota-rod task, a standard motor task in rodents. Third, we examined motor ability with the Step-Wheel task in which mice were trained to run in a motor-driven turning wheel adjusting their steps on foothold pegs to drink water. The results showed clear differences among the mice of three genotypes in three different types of behavior. In monitoring spontaneous motor activities, D1R and D2R KO mice showed higher and lower 24 h activities, respectively, than WT mice. In the rota-rod tasks, at a low speed, D1R KO mice showed poor performance but later improved, whereas D2R KO mice showed a good performance at early days without further improvement. When first subjected to a high speed task, the D2R KO mice showed poorer rota-rod performance at a low speed than the D1R KO mice. In the Step-Wheel task, across daily sessions, D2R KO mice increased the duration that mice run sufficiently close to the spout to drink water, and decreased time to touch the floor due to missing the peg steps and number of times the wheel was stopped, which performance was much better than that of D1R KO mice. These incongruent results between the two tasks for D1R and D2R KO mice may be due to the differences in the motivation for the rota-rod and Step-Wheel tasks, aversion- and reward-driven, respectively. The Step-Wheel system may become a useful tool for assessing the motor ability of WT and mutant mice. PMID:25076876

Distinct motor impairments of dopamine D1 and D2 receptor knockout mice revealed by three types of motor behavior.

PubMed

Nakamura, Toru; Sato, Asako; Kitsukawa, Takashi; Momiyama, Toshihiko; Yamamori, Tetsuo; Sasaoka, Toshikuni

2014-01-01

Both D1R and D2R knock out (KO) mice of the major dopamine receptors show significant motor impairments. However, there are some discrepant reports, which may be due to the differences in genetic background and experimental procedures. In addition, only few studies directly compared the motor performance of D1R and D2R KO mice. In this paper, we examined the behavioral difference among N10 congenic D1R and D2R KO, and wild type (WT) mice. First, we examined spontaneous motor activity in the home cage environment for consecutive 5 days. Second, we examined motor performance using the rota-rod task, a standard motor task in rodents. Third, we examined motor ability with the Step-Wheel task in which mice were trained to run in a motor-driven turning wheel adjusting their steps on foothold pegs to drink water. The results showed clear differences among the mice of three genotypes in three different types of behavior. In monitoring spontaneous motor activities, D1R and D2R KO mice showed higher and lower 24 h activities, respectively, than WT mice. In the rota-rod tasks, at a low speed, D1R KO mice showed poor performance but later improved, whereas D2R KO mice showed a good performance at early days without further improvement. When first subjected to a high speed task, the D2R KO mice showed poorer rota-rod performance at a low speed than the D1R KO mice. In the Step-Wheel task, across daily sessions, D2R KO mice increased the duration that mice run sufficiently close to the spout to drink water, and decreased time to touch the floor due to missing the peg steps and number of times the wheel was stopped, which performance was much better than that of D1R KO mice. These incongruent results between the two tasks for D1R and D2R KO mice may be due to the differences in the motivation for the rota-rod and Step-Wheel tasks, aversion- and reward-driven, respectively. The Step-Wheel system may become a useful tool for assessing the motor ability of WT and mutant mice.

Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

PubMed Central

Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C.; Nielsen, Jens Bo

2011-01-01

Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals. PMID:21408054

Motor planning and execution in left- and right-handed individuals during a bimanual grasping and placing task.

PubMed

Hughes, Charmayne M L; Reissig, Paola; Seegelke, Christian

2011-09-01

The issue of handedness has been the topic of great interest for researchers in a number of scientific domains. It is typically observed that the dominant hand yields numerous behavioral advantages over the non-dominant hand during unimanual tasks, which provides evidence of hemispheric specialization. In contrast to advantages for the dominant hand during motor execution, recent research has demonstrated that the right hand has advantages during motor planning (regardless of handedness), indicating that motor planning is a specialized function of the left hemisphere. In the present study we explored hemispheric advantages in motor planning and execution in left- and right-handed individuals during a bimanual grasping and placing task. Replicating previous findings, both motor planning and execution was influenced by object end-orientation congruency. In addition, although motor planning (i.e., end-state comfort) was not influenced by hand or handedness, motor execution differed between left and right hand, with shorter object transport times observed for the left hand, regardless of handedness. These results demonstrate that the hemispheric advantages often observed in unimanual tasks do not extend to discrete bimanual tasks. We propose that the differences in object transport time between the two hands arise from overt shifting visual fixation between the two hands/objects. Copyright © 2011 Elsevier B.V. All rights reserved.

Consolidating the effects of waking and sleep on motor-sequence learning.

PubMed

Brawn, Timothy P; Fenn, Kimberly M; Nusbaum, Howard C; Margoliash, Daniel

2010-10-20

Sleep is widely believed to play a critical role in memory consolidation. Sleep-dependent consolidation has been studied extensively in humans using an explicit motor-sequence learning paradigm. In this task, performance has been reported to remain stable across wakefulness and improve significantly after sleep, making motor-sequence learning the definitive example of sleep-dependent enhancement. Recent work, however, has shown that enhancement disappears when the task is modified to reduce task-related inhibition that develops over a training session, thus questioning whether sleep actively consolidates motor learning. Here we use the same motor-sequence task to demonstrate sleep-dependent consolidation for motor-sequence learning and explain the discrepancies in results across studies. We show that when training begins in the morning, motor-sequence performance deteriorates across wakefulness and recovers after sleep, whereas performance remains stable across both sleep and subsequent waking with evening training. This pattern of results challenges an influential model of memory consolidation defined by a time-dependent stabilization phase and a sleep-dependent enhancement phase. Moreover, the present results support a new account of the behavioral effects of waking and sleep on explicit motor-sequence learning that is consistent across a wide range of tasks. These observations indicate that current theories of memory consolidation that have been formulated to explain sleep-dependent performance enhancements are insufficient to explain the range of behavioral changes associated with sleep.

Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback

PubMed Central

Berman, Brian D.; Horovitz, Silvina G.; Venkataraman, Gaurav; Hallett, Mark

2011-01-01

Advances in fMRI data acquisition and processing have made it possible to analyze brain activity as rapidly as the images are acquired allowing this information to be fed back to subjects in the scanner. The ability of subjects to learn to volitionally control localized brain activity within motor cortex using such real-time fMRI-based neurofeedback (NF) is actively being investigated as it may have clinical implications for motor rehabilitation after central nervous system injury and brain-computer interfaces. We investigated the ability of fifteen healthy volunteers to use NF to modulate brain activity within the primary motor cortex (M1) during a finger tapping and tapping imagery task. The M1 hand area ROI (ROIm) was functionally localized during finger tapping and a visual representation of BOLD signal changes within the ROIm fed back to the subject in the scanner. Surface EMG was used to assess motor output during tapping and ensure no motor activity was present during motor imagery task. Subjects quickly learned to modulate brain activity within their ROIm during the finger-tapping task, which could be dissociated from the magnitude of the tapping, but did not show a significant increase within the ROIm during the hand motor imagery task at the group level despite strongly activating a network consistent with the performance of motor imagery. The inability of subjects to modulate M1 proper with motor imagery may reflect an inherent difficulty in activating synapses in this area, with or without NF, since such activation may lead to M1 neuronal output and obligatory muscle activity. Future real-time fMRI-based NF investigations involving motor cortex may benefit from focusing attention on cortical regions other than M1 for feedback training or alternative feedback strategies such as measures of functional connectivity within the motor system. PMID:21803163

Intermanual transfer and bilateral cortical plasticity is maintained in older adults after skilled motor training with simple and complex tasks

PubMed Central

Dickins, Daina S. E.; Sale, Martin V.; Kamke, Marc R.

2015-01-01

Intermanual transfer refers to the phenomenon whereby unilateral motor training induces performance gains in both the trained limb and in the opposite, untrained limb. Evidence indicates that intermanual transfer is attenuated in older adults following training on a simple ballistic movement task, but not after training on a complex task. This study investigated whether differences in plasticity in bilateral motor cortices underlie these differential intermanual transfer effects in older adults. Twenty young (<35 years-old) and older adults (>65 years) trained on a simple (repeated ballistic thumb abduction) and complex (sequential finger-thumb opposition) task in separate sessions. Behavioral performance was used to quantify intermanual transfer between the dominant (trained) and non-dominant (untrained) hands. The amplitude of motor-evoked potentials induced by single pulse transcranial magnetic stimulation was used to investigate excitability changes in bilateral motor cortices. Contrary to predictions, both age groups exhibited performance improvements in both hands after unilateral skilled motor training with simple and complex tasks. These performance gains were accompanied by bilateral increases in cortical excitability in both groups for the simple but not the complex task. The findings suggest that advancing age does not necessarily influence the capacity for intermanual transfer after training with the dominant hand. PMID:25999856

Characterization of fine motor development: dynamic analysis of children's drawing movements.

PubMed

Lin, Qiushi; Luo, Jianfei; Wu, Zhongcheng; Shen, Fei; Sun, Zengwu

2015-04-01

In this study, we investigated children's fine motor development by analyzing drawing trajectories, kinematics and kinetics. Straight lines drawing task and circles drawing task were performed by using a force sensitive tablet. Forty right-handed and Chinese mother-tongue students aged 6-12, attending classes from grade 1 to 5, were engaged in the experiment. Three spatial parameters, namely cumulative trace length, vector length of straight line and vertical diameter of circle were determined. Drawing duration, mean drawing velocity, and number of peaks in stroke velocity profile (NPV) were derived as kinematic parameters. Besides mean normal force, two kinetic indices were proposed: normalized force angle regulation (NFR) and variation of fine motor control (VFC) for circles drawing task. The maturation and automation of fine motor ability were reflected by increased drawing velocity, reduced drawing duration, NPV and NFR, with decreased VFC in circles drawing task. Grade and task main effects as well as significant correlations between age and parameters suggest that factors such as schooling, age and task should be considered in the assessment of fine motor skills. Compared with kinematic parameters, findings of NFR and VFC revealed that kinetics is another important perspective in the analysis of fine motor movement. Copyright © 2014 Elsevier B.V. All rights reserved.

Measurement of functional task difficulty during motor learning: What level of difficulty corresponds to the optimal challenge point?

PubMed

Akizuki, Kazunori; Ohashi, Yukari

2015-10-01

The relationship between task difficulty and learning benefit was examined, as was the measurability of task difficulty. Participants were required to learn a postural control task on an unstable surface at one of four different task difficulty levels. Results from the retention test showed an inverted-U relationship between task difficulty during acquisition and motor learning. The second-highest level of task difficulty was the most effective for motor learning, while learning was delayed at the most and least difficult levels. Additionally, the results indicate that salivary α-amylase and the performance dimension of the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) are useful indices of task difficulty. Our findings suggested that instructors may be able to adjust task difficulty based on salivary α-amylase and the performance dimension of the NASA-TLX to enhance learning. Copyright © 2015 Elsevier B.V. All rights reserved.

Disruption of self-organized actions in monkeys with progressive MPTP-induced parkinsonism. I. Effects of task complexity.

PubMed

Pessiglione, Mathias; Guehl, Dominique; Hirsch, Etienne C; Féger, Jean; Tremblay, Léon

2004-01-01

Parkinson's disease (PD) is characterized by motor symptoms, usually accompanied by cognitive deficits. The question addressed in this study is whether complexity of routine actions can exacerbate parkinsonian disorders that are often considered to be motor symptoms. To examine this question, we trained four vervet monkeys (Cercopithecus aethiops) to perform three multiple-choice retrieval tasks. In order of ascending complexity, rewards were freely available (task 1), covered with transparent sliding plaques (task 2), and covered with opaque sliding plaques cued by symbols (task 3). Thus, from task 1 to task 2 we added a motor difficulty--the recall of context-adapted movement; and from task 2 to task 3 we added a cognitive difficulty: the recall of symbol-reward associations. The more complex the task, the longer it took to learn, but after extensive training the performance was stable in all tasks, with similar retrieval durations. The monkeys then received systemic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) injections (0.3-0.4 mg/kg) every 4-7 days, until the first motor symptoms appeared. In the course of MPTP intoxication, the behavioural performance declined while the motor symptoms were absent or mild--the retrieval duration increased, and non-initiated choices and hesitations between choices became frequent. Interestingly, this decline was in proportion to task complexity, and was particularly pronounced with the cognitive difficulty. Furthermore, freezing appeared only with the cognitive difficulty. We therefore suggest that everyday cognitive difficulties may exacerbate hypokinesia (lack of initiation, abnormal slowness) and executive disorders (hesitations, freezing) in the early stages of human PD.

The effectiveness of robotic training depends on motor task characteristics.

PubMed

Marchal-Crespo, Laura; Rappo, Nicole; Riener, Robert

2017-12-01

Previous research suggests that the effectiveness of robotic training depends on the motor task to be learned. However, it is still an open question which specific task's characteristics influence the efficacy of error-modulating training strategies. Motor tasks can be classified based on the time characteristics of the task, in particular the task's duration (discrete vs. continuous). Continuous tasks require movements without distinct beginning or end. Discrete tasks require fast movements that include well-defined postures at the beginning and the end. We developed two games, one that requires a continuous movement-a tracking task-and one that requires discrete movements-a fast reaching task. We conducted an experiment with thirty healthy subjects to evaluate the effectiveness of three error-modulating training strategies-no guidance, error amplification (i.e., repulsive forces proportional to errors) and haptic guidance-on self-reported motivation and learning of the continuous and discrete games. Training with error amplification resulted in better motor learning than haptic guidance, besides the fact that error amplification reduced subjects' interest/enjoyment and perceived competence during training. Only subjects trained with error amplification improved their performance after training the discrete game. In fact, subjects trained without guidance improved the performance in the continuous game significantly more than in the discrete game, probably because the continuous task required greater attentional levels. Error-amplifying training strategies have a great potential to provoke better motor learning in continuous and discrete tasks. However, their long-lasting negative effects on motivation might limit their applicability in intense neurorehabilitation programs.

Influence of dual-tasking with different levels of attention diversion on characteristics of the movement-related cortical potential.

PubMed

Aliakbaryhosseinabadi, Susan; Kamavuako, Ernest Nlandu; Jiang, Ning; Farina, Dario; Mrachacz-Kersting, Natalie

2017-11-01

Dual tasking is defined as performing two tasks concurrently and has been shown to have a significant effect on attention directed to the performance of the main task. In this study, an attention diversion task with two different levels was administered while participants had to complete a cue-based motor task consisting of foot dorsiflexion. An auditory oddball task with two levels of complexity was implemented to divert the user's attention. Electroencephalographic (EEG) recordings were made from nine single channels. Event-related potentials (ERPs) confirmed that the oddball task of counting a sequence of two tones decreased the auditory P300 amplitude more than the oddball task of counting one target tone among three different tones. Pre-movement features quantified from the movement-related cortical potential (MRCP) were changed significantly between single and dual-task conditions in motor and fronto-central channels. There was a significant delay in movement detection for the case of single tone counting in two motor channels only (237.1-247.4ms). For the task of sequence counting, motor cortex and frontal channels showed a significant delay in MRCP detection (232.1-250.5ms). This study investigated the effect of attention diversion in dual-task conditions by analysing both ERPs and MRCPs in single channels. The higher attention diversion lead to a significant reduction in specific MRCP features of the motor task. These results suggest that attention division in dual-tasking situations plays an important role in movement execution and detection. This has important implications in designing real-time brain-computer interface systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Learning a stick-balancing task involves task-specific coupling between posture and hand displacements.

PubMed

Cluff, Tyler; Boulet, Jason; Balasubramaniam, Ramesh

2011-08-01

Theories of motor learning argue that the acquisition of novel motor skills requires a task-specific organization of sensory and motor subsystems. We examined task-specific coupling between motor subsystems as subjects learned a novel stick-balancing task. We focused on learning-induced changes in finger movements and body sway and investigated the effect of practice on their coupling. Eight subjects practiced balancing a cylindrical wooden stick for 30 min a day during a 20 day learning period. Finger movements and center of pressure trajectories were recorded in every fifth practice session (4 in total) using a ten camera VICON motion capture system interfaced with two force platforms. Motor learning was quantified using average balancing trial lengths, which increased with practice and confirmed that subjects learned the task. Nonlinear time series and phase space reconstruction methods were subsequently used to investigate changes in the spatiotemporal properties of finger movements, body sway and their progressive coupling. Systematic increases in subsystem coupling were observed despite reduced autocorrelation and differences in the temporal properties of center of pressure and finger trajectories. The average duration of these coupled trajectories increased systematically across the learning period. In short, the abrupt transition between coupled and decoupled subsystem dynamics suggested that stick balancing is regulated by a hierarchical control mechanism that switches from collective to independent control of the finger and center of pressure. In addition to traditional measures of motor performance, dynamical analyses revealed changes in motor subsystem organization that occurred when subjects learned a novel stick-balancing task.

Whole body heat stress increases motor cortical excitability and skill acquisition in humans

PubMed Central

Littmann, Andrew E.; Shields, Richard K.

2015-01-01

Objective Vigorous systemic exercise stimulates a cascade of molecular and cellular processes that enhance central nervous system (CNS) plasticity and performance. The influence of heat stress on CNS performance and learning is novel. We designed two experiments to determine whether passive heat stress 1) facilitated motor cortex excitability and 2) improved motor task acquisition compared to no heat stress. Methods Motor evoked potentials (MEPs) from the first dorsal interosseus (FDI) were collected before and after 30 minutes of heat stress at 73° C. A second cohort of subjects performed a motor learning task using the FDI either following heat or the no heat condition. Results Heat stress increased heart rate to 65% of age-predicted maximum. After heat, mean resting MEP amplitude increased 48% (P < 0.05). MEP stimulus-response amplitudes did not differ according to stimulus intensity. In the second experiment, heat stress caused a significant decrease in absolute and variable error (p < 0.05) during a novel movement task using the FDI. Conclusions Passive environmental heat stress 1) increases motor cortical excitability, and 2) enhances performance in a motor skill acquisition task. Significance Controlled heat stress may prime the CNS to enhance motor skill acquisition during rehabilitation. PMID:26616546

Modeling individual differences in text reading fluency: a different pattern of predictors for typically developing and dyslexic readers

PubMed Central

Zoccolotti, Pierluigi; De Luca, Maria; Marinelli, Chiara V.; Spinelli, Donatella

2014-01-01

This study was aimed at predicting individual differences in text reading fluency. The basic proposal included two factors, i.e., the ability to decode letter strings (measured by discrete pseudo-word reading) and integration of the various sub-components involved in reading (measured by Rapid Automatized Naming, RAN). Subsequently, a third factor was added to the model, i.e., naming of discrete digits. In order to use homogeneous measures, all contributing variables considered the entire processing of the item, including pronunciation time. The model, which was based on commonality analysis, was applied to data from a group of 43 typically developing readers (11- to 13-year-olds) and a group of 25 chronologically matched dyslexic children. In typically developing readers, both orthographic decoding and integration of reading sub-components contributed significantly to the overall prediction of text reading fluency. The model prediction was higher (from ca. 37 to 52% of the explained variance) when we included the naming of discrete digits variable, which had a suppressive effect on pseudo-word reading. In the dyslexic readers, the variance explained by the two-factor model was high (69%) and did not change when the third factor was added. The lack of a suppression effect was likely due to the prominent individual differences in poor orthographic decoding of the dyslexic children. Analyses on data from both groups of children were replicated by using patches of colors as stimuli (both in the RAN task and in the discrete naming task) obtaining similar results. We conclude that it is possible to predict much of the variance in text-reading fluency using basic processes, such as orthographic decoding and integration of reading sub-components, even without taking into consideration higher-order linguistic factors such as lexical, semantic and contextual abilities. The approach validity of using proximal vs. distal causes to predict reading fluency is discussed. PMID:25477856

Classification of different reaching movements from the same limb using EEG

NASA Astrophysics Data System (ADS)

Shiman, Farid; López-Larraz, Eduardo; Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; Spüler, Martin; Birbaumer, Niels; Ramos-Murguialday, Ander

2017-08-01

Objective. Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

Improving Motor Control in Walking: A Randomized Clinical Trial in Older Adults with Subclinical Walking Difficulty

PubMed Central

Brach, Jennifer S.; Lowry, Kristin; Perera, Subashan; Hornyak, Victoria; Wert, David; Studenski, Stephanie A.; VanSwearingen, Jessie M.

2016-01-01

Objective The objective was to test the proposed mechanism of action of a task-specific motor learning intervention by examining its effect on measures of the motor control of gait. Design Single blinded randomized clinical trial. Setting University research laboratory. Participants Forty older adults 65 years of age and older, with gait speed >1.0 m/s and impaired motor skill (Figure of 8 walk time > 8 secs). Interventions The two interventions included a task-oriented motor learning and a standard exercise program. Both interventions lasted 12 weeks, with twice weekly one hour physical therapist supervised sessions. Main Outcome Measures Two measure of the motor control of gait, gait variability and smoothness of walking, were assessed pre and post intervention by assessors masked to treatment arm. Results Of 40 randomized subjects; 38 completed the trial (mean age 77.1±6.0 years). Motor control group improved more than standard group in double support time variability (0.13 vs. 0.05 m/s; adjusted difference, AD=0.006, p=0.03). Smoothness of walking in the anterior/posterior direction improved more in motor control than standard for all conditions (usual: AD=0.53, p=0.05; narrow: AD=0.56, p=0.01; dual task: AD=0.57, p=0.04). Conclusions Among older adults with subclinical walking difficulty, there is initial evidence that task-oriented motor learning exercise results in gains in the motor control of walking, while standard exercise does not. Task-oriented motor learning exercise is a promising intervention for improving timing and coordination deficits related to mobility difficulties in older adults, and needs to be evaluated in a definitive larger trial. PMID:25448244

Developmental study of visual perception of handwriting movement: influence of motor competencies?

PubMed

Bidet-Ildei, Christel; Orliaguet, Jean-Pierre

2008-07-25

This paper investigates the influence of motor competencies for the visual perception of human movements in 6-10 years old children. To this end, we compared the kinematics of actual performed and perceptual preferred handwriting movements. The two children's tasks were (1) to write the letter e on a digitizer (handwriting task) and (2) to adjust the velocity of an e displayed on a screen so that it would correspond to "their preferred velocity" (perceptive task). In both tasks, the size of the letter (from 3.4 to 54.02 cm) was different on each trial. Results showed that irrespective of age and task, total movement time conforms to the isochrony principle, i.e., the tendency to maintain constant the duration of movement across changes of amplitude. However, concerning movement speed, there is no developmental correspondence between results obtained in the motor and the perceptive tasks. In handwriting task, movement time decreased with age but no effect of age was observed in the perceptive task. Therefore, perceptual preference of handwriting movement in children could not be strictly interpreted in terms of motor-perceptual coupling.

Association between Body Composition and Motor Performance in Preschool Children

PubMed Central

Kakebeeke, Tanja H.; Lanzi, Stefano; Zysset, Annina E.; Arhab, Amar; Messerli-Bürgy, Nadine; Stuelb, Kerstin; Leeger-Aschmann, Claudia S.; Schmutz, Einat A.; Meyer, Andrea H.; Kriemler, Susi; Munsch, Simone; Jenni, Oskar G.; Puder, Jardena J.

2017-01-01

Objective Being overweight makes physical movement more difficult. Our aim was to investigate the association between body composition and motor performance in preschool children. Methods A total of 476 predominantly normal-weight preschool children (age 3.9 ± 0.7 years; m/f: 251/225; BMI 16.0 ± 1.4 kg/m2) participated in the Swiss Preschoolers' Health Study (SPLASHY). Body composition assessments included skinfold thickness, waist circumference (WC), and BMI. The Zurich Neuromotor Assessment (ZNA) was used to assess gross and fine motor tasks. Results After adjustment for age, sex, socioeconomic status, sociocultural characteristics, and physical activity (assessed with accelerometers), skinfold thickness and WC were both inversely correlated with jumping sideward (gross motor task β-coefficient −1.92, p = 0.027; and −3.34, p = 0.014, respectively), while BMI was positively correlated with running performance (gross motor task β-coefficient 9.12, p = 0.001). No significant associations were found between body composition measures and fine motor tasks. Conclusion The inverse associations between skinfold thickness or WC and jumping sideward indicates that children with high fat mass may be less proficient in certain gross motor tasks. The positive association between BMI and running suggests that BMI might be an indicator of fat-free (i.e., muscle) mass in predominately normal-weight preschool children. PMID:28934745

How is a motor skill learned? Change and invariance at the levels of task success and trajectory control

PubMed Central

Krakauer, John W.; Mazzoni, Pietro

2012-01-01

The public pays large sums of money to watch skilled motor performance. Notably, however, in recent decades motor skill learning (performance improvement beyond baseline levels) has received less experimental attention than motor adaptation (return to baseline performance in the setting of an external perturbation). Motor skill can be assessed at the levels of task success and movement quality, but the link between these levels remains poorly understood. We devised a motor skill task that required visually guided curved movements of the wrist without a perturbation, and we defined skill learning at the task level as a change in the speed–accuracy trade-off function (SAF). Practice in restricted speed ranges led to a global shift of the SAF. We asked how the SAF shift maps onto changes in trajectory kinematics, to establish a link between task-level performance and fine motor control. Although there were small changes in mean trajectory, improved performance largely consisted of reduction in trial-to-trial variability and increase in movement smoothness. We found evidence for improved feedback control, which could explain the reduction in variability but does not preclude other explanations such as an increased signal-to-noise ratio in cortical representations. Interestingly, submovement structure remained learning invariant. The global generalization of the SAF across a wide range of difficulty suggests that skill for this task is represented in a temporally scalable network. We propose that motor skill acquisition can be characterized as a slow reduction in movement variability, which is distinct from faster model-based learning that reduces systematic error in adaptation paradigms. PMID:22514286

Home-based interventions improve trained, but not novel, dual-task balance performance in older adults: A randomized controlled trial.

PubMed

Wongcharoen, Suleeporn; Sungkarat, Somporn; Munkhetvit, Peeraya; Lugade, Vipul; Silsupadol, Patima

2017-02-01

The purpose of this study was to compare the efficacy of four different home-based interventions on dual-task balance performance and to determine the generalizability of the four trainings to untrained tasks. Sixty older adults, aged 65 and older, were randomly assigned to one of four home-based interventions: single-task motor training, single-task cognitive training, dual-task motor-cognitive training, and dual-task cognitive-cognitive training. Participants received 60-min individualized training sessions, 3 times a week for 4 weeks. Prior to and following the training program, participants were asked to walk under two single-task conditions (i.e. narrow walking and obstacle crossing) and two dual-task conditions (i.e. a trained narrow walking while performing verbal fluency task and an untrained obstacle crossing while counting backward by 3s task). A nine-camera motion capture system was used to collect the trajectories of 32 reflective markers placed on bony landmarks of participants. Three-dimensional kinematics of the whole body center of mass and base of support were computed. Results from the extrapolated center of mass displacement indicated that motor-cognitive training was more effective than the single-task motor training to improve dual-task balance performance (p=0.04, ES=0.11). Interestingly, balance performance under both single-task and dual-task conditions can also be improved through a non-motor, single-task cognitive training program (p=0.01, ES=0.13, and p=0.01, ES=0.11, respectively). However, improved dual-task processing skills during training were not transferred to the novel dual task (p=0.15, ES=0.09). This is the first study demonstrating that home-based dual-task training can be effectively implemented to improve balance performance during gait in older adults. Copyright © 2016 Elsevier B.V. All rights reserved.

[Neuromodulatory effects of bromazepam when individuals were exposed to a motor learning task: quantitative electroencephalography (qEEG)].

PubMed

Salles, José Inácio; Bastos, Victor Hugo; Cunha, Marlo; Machado, Dionis; Cagy, Maurício; Furtado, Vernon; Basile, Luis Fernando; Piedade, Roberto; Ribeiro, Pedro

2006-03-01

The sedative effects of bromazepam on cognitive and performance have been widely investigated. A number of different approaches have assessed the influence of bromazepam when individuals are engaged to a motor task. In this context, the present study aimed to investigate electrophysiological changes when individuals were exposed to a typewriting task after taking 6 mg of bromazepam. qEEG data were simultaneously recorded during the task. In particular, relative power in delta band (0.5-3.5 Hz) was analyzed. Time of execution and errors during the task were registered as behavioral variables. The experimental group, bromazepam 6 mg, showed a better motor performance and higher relative power than control individuals (placebo). These results suggest that the use of bromazepam reduces anxiety levels as expected and thus, produces an increment in motor performance.

Neuromorphic circuits impart a sense of touch

NASA Astrophysics Data System (ADS)

Bartolozzi, Chiara

2018-06-01

The sense of touch is the ability to perceive consistency, texture, and shape of objects that we manipulate, and the forces we exchange with them. Touch is a source of information that we effortlessly decode to smoothly and naturally grasp and manipulate objects, maintain our posture while walking, or avoid stumbling into obstacles, allowing us to plan, adapt, and correct actions in an ever-changing external world. As such, artificial devices, such as robots or prostheses, that aim to accomplish similar tasks must possess artificial tactile-sensing systems. On page 998 of this issue, Kim et al. (1) report on a “neuromorphic” tactile sensory system based on organic, flexible, electronic circuits that can measure the force applied on the sensing regions. The encoding of the signal is similar to that used by human nerves that are sensitive to tactile stimuli (mechanoreceptors), so the device outputs can substitute for them and communicate with other nerves (e.g., residual nerve fibers of amputees or motor neurons). The proposed system exploits organic electronics that allow for three-dimensional printing of flexible structures that conform to large curved surfaces, as required for placing sensors on robots (2) and prostheses.

Motor skills in Czech children with attention-deficit/hyperactivity disorder and their neurotypical counterparts.

PubMed

Scharoun, S M; Bryden, P J; Otipkova, Z; Musalek, M; Lejcarova, A

2013-11-01

Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurobehavioural disorder. Characterized by recurring problems with impulsiveness and inattention in combination with hyperactivity, motor impairments have also been well documented in the literature. The aim of this study was to compare the fine and gross motor skills of male and female children with ADHD and their neurotypical counterparts within seven skill assessments. This included three fine motor tasks: (1) spiral tracing, (2) dot filling, (3) tweezers and beads; and four gross motor tasks: (1) twistbox, (2) foot tapping, (3) small plate finger tapping, and (4) large plate finger tapping. It was hypothesized that children with ADHD would display poorer motor skills in comparison to neurotypical controls in both fine and gross motor assessments. However, statistically significant differences between the groups only emerged in four of the seven tasks (spiral tracing, dot filling, tweezers and beads and foot tapping). In line with previous findings, the complexity underlying upper limb tasks solidified the divide in performance between children with ADHD and their neurotypical counterparts. In light of similar research, impairments in lower limb motor skill were also observed. Future research is required to further delineate trends in motor difficulties in ADHD, while further investigating the underlying mechanisms of impairment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time course of implicit processing and explicit processing of emotional faces and emotional words.

PubMed

Frühholz, Sascha; Jellinghaus, Anne; Herrmann, Manfred

2011-05-01

Facial expressions are important emotional stimuli during social interactions. Symbolic emotional cues, such as affective words, also convey information regarding emotions that is relevant for social communication. Various studies have demonstrated fast decoding of emotions from words, as was shown for faces, whereas others report a rather delayed decoding of information about emotions from words. Here, we introduced an implicit (color naming) and explicit task (emotion judgment) with facial expressions and words, both containing information about emotions, to directly compare the time course of emotion processing using event-related potentials (ERP). The data show that only negative faces affected task performance, resulting in increased error rates compared to neutral faces. Presentation of emotional faces resulted in a modulation of the N170, the EPN and the LPP components and these modulations were found during both the explicit and implicit tasks. Emotional words only affected the EPN during the explicit task, but a task-independent effect on the LPP was revealed. Finally, emotional faces modulated source activity in the extrastriate cortex underlying the generation of the N170, EPN and LPP components. Emotional words led to a modulation of source activity corresponding to the EPN and LPP, but they also affected the N170 source on the right hemisphere. These data show that facial expressions affect earlier stages of emotion processing compared to emotional words, but the emotional value of words may have been detected at early stages of emotional processing in the visual cortex, as was indicated by the extrastriate source activity. Copyright © 2011 Elsevier B.V. All rights reserved.

People with chronic facial pain perform worse than controls at a facial emotion recognition task, but it is not all about the emotion.

PubMed

von Piekartz, H; Wallwork, S B; Mohr, G; Butler, D S; Moseley, G L

2015-04-01

Alexithymia, or a lack of emotional awareness, is prevalent in some chronic pain conditions and has been linked to poor recognition of others' emotions. Recognising others' emotions from their facial expression involves both emotional and motor processing, but the possible contribution of motor disruption has not been considered. It is possible that poor performance on emotional recognition tasks could reflect problems with emotional processing, motor processing or both. We hypothesised that people with chronic facial pain would be less accurate in recognising others' emotions from facial expressions, would be less accurate in a motor imagery task involving the face, and that performance on both tasks would be positively related. A convenience sample of 19 people (15 females) with chronic facial pain and 19 gender-matched controls participated. They undertook two tasks; in the first task, they identified the facial emotion presented in a photograph. In the second, they identified whether the person in the image had a facial feature pointed towards their left or right side, a well-recognised paradigm to induce implicit motor imagery. People with chronic facial pain performed worse than controls at both tasks (Facially Expressed Emotion Labelling (FEEL) task P < 0·001; left/right judgment task P < 0·001). Participants who were more accurate at one task were also more accurate at the other, regardless of group (P < 0·001, r(2)  = 0·523). Participants with chronic facial pain were worse than controls at both the FEEL emotion recognition task and the left/right facial expression task and performance covaried within participants. We propose that disrupted motor processing may underpin or at least contribute to the difficulty that facial pain patients have in emotion recognition and that further research that tests this proposal is warranted. © 2014 John Wiley & Sons Ltd.

Continuously Adaptive vs. Discrete Changes of Task Difficulty in the Training of a Complex Perceptual-Motor Task.

ERIC Educational Resources Information Center

Wood, Milton E.

The purpose of the effort was to determine the benefits to be derived from the adaptive training technique of automatically adjusting task difficulty as a function of a student skill during early learning of a complex perceptual motor task. A digital computer provided the task dynamics, scoring, and adaptive control of a second-order, two-axis,…