A Brain-Machine-Muscle Interface for Restoring Hindlimb Locomotion after Complete Spinal Transection in Rats

PubMed Central

Alam, Monzurul; Chen, Xi; Zhang, Zicong; Li, Yan; He, Jufang

2014-01-01

A brain-machine interface (BMI) is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking) could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics. PMID:25084446

A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.

PubMed

Alam, Monzurul; Chen, Xi; Zhang, Zicong; Li, Yan; He, Jufang

2014-01-01

A brain-machine interface (BMI) is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking) could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.

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.

Decoding position, velocity, or goal: does it matter for brain-machine interfaces?

PubMed

Marathe, A R; Taylor, D M

2011-04-01

Arm end-point position, end-point velocity, and the intended final location or 'goal' of a reach have all been decoded from cortical signals for use in brain-machine interface (BMI) applications. These different aspects of arm movement can be decoded from the brain and used directly to control the position, velocity, or movement goal of a device. However, these decoded parameters can also be remapped to control different aspects of movement, such as using the decoded position of the hand to control the velocity of a device. People easily learn to use the position of a joystick to control the velocity of an object in a videogame. Similarly, in BMI systems, the position, velocity, or goal of a movement could be decoded from the brain and remapped to control some other aspect of device movement. This study evaluates how easily people make transformations between position, velocity, and reach goal in BMI systems. It also evaluates how different amounts of decoding error impact on device control with and without these transformations. Results suggest some remapping options can significantly improve BMI control. This study provides guidance on what remapping options to use when various amounts of decoding error are present.

Decoding position, velocity, or goal: Does it matter for brain-machine interfaces?

NASA Astrophysics Data System (ADS)

Marathe, A. R.; Taylor, D. M.

2011-04-01

Arm end-point position, end-point velocity, and the intended final location or 'goal' of a reach have all been decoded from cortical signals for use in brain-machine interface (BMI) applications. These different aspects of arm movement can be decoded from the brain and used directly to control the position, velocity, or movement goal of a device. However, these decoded parameters can also be remapped to control different aspects of movement, such as using the decoded position of the hand to control the velocity of a device. People easily learn to use the position of a joystick to control the velocity of an object in a videogame. Similarly, in BMI systems, the position, velocity, or goal of a movement could be decoded from the brain and remapped to control some other aspect of device movement. This study evaluates how easily people make transformations between position, velocity, and reach goal in BMI systems. It also evaluates how different amounts of decoding error impact on device control with and without these transformations. Results suggest some remapping options can significantly improve BMI control. This study provides guidance on what remapping options to use when various amounts of decoding error are present.

[Training cortical signals by means of a BMI-EEG system, its evolution and intervention. A case report].

PubMed

Monge-Pereira, E; Casatorres Perez-Higueras, I; Fernandez-Gonzalez, P; Ibanez-Pereda, J; Serrano, J I; Molina-Rueda, F

2017-04-16

In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality. A 40 years-old man with stroke with two months from the injury participated in this study. We used a BMI based on EEG. The subject's motion intention was analyzed calculating the event-related desynchronization. The upper limb motor function was evaluated with the Fugl-Meyer Assessment and the participant's satisfaction was evaluated using the QUEST 2.0. The intervention using a physical therapist as an interface was carried out without difficulty. The BMI systems detect cortical changes in a subacute stroke subject. These changes are coherent with the evolution observed using the Fugl-Meyer Assessment.

Errare machinale est: the use of error-related potentials in brain-machine interfaces

PubMed Central

Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

2014-01-01

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches. PMID:25100937

Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces.

PubMed

Perruchoud, David; Pisotta, Iolanda; Carda, Stefano; Murray, Micah M; Ionta, Silvio

2016-08-01

Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed. The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI's actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms. Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users' needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications. The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.

Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

PubMed

Garcia-Garcia, Martha G; Bergquist, Austin J; Vargas-Perez, Hector; Nagai, Mary K; Zariffa, Jose; Marquez-Chin, Cesar; Popovic, Milos R

2017-11-01

Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.

Brain-machine interfaces in neurorehabilitation of stroke.

PubMed

Soekadar, Surjo R; Birbaumer, Niels; Slutzky, Marc W; Cohen, Leonardo G

2015-11-01

Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke. Copyright © 2014. Published by Elsevier Inc.

Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

PubMed Central

Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.

2016-01-01

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected −367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration. PMID:27065787

Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia.

PubMed

Thorp, Elias B; Abdollahi, Farnaz; Chen, David; Farshchiansadegh, Ali; Lee, Mei-Hua; Pedersen, Jessica P; Pierella, Camilla; Roth, Elliot J; Seanez Gonzalez, Ismael; Mussa-Ivaldi, Ferdinando A

2016-02-01

Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user's residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional control commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control a power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control.

Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.

PubMed

Downey, John E; Weiss, Jeffrey M; Muelling, Katharina; Venkatraman, Arun; Valois, Jean-Sebastien; Hebert, Martial; Bagnell, J Andrew; Schwartz, Andrew B; Collinger, Jennifer L

2016-03-18

Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object. Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps. Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92 % of trials, demonstrating the potential for users to accurately execute their intention while using shared control. Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users. NCT01364480 and NCT01894802 .

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

PubMed

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.

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

Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback

PubMed Central

Zeng, Hong; Wang, Yanxin; Wu, Changcheng; Song, Aiguo; Liu, Jia; Ji, Peng; Xu, Baoguo; Zhu, Lifeng; Li, Huijun; Wen, Pengcheng

2017-01-01

Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes. PMID:29163123

Assessment of brain-machine interfaces from the perspective of people with paralysis.

PubMed

Blabe, Christine H; Gilja, Vikash; Chestek, Cindy A; Shenoy, Krishna V; Anderson, Kim D; Henderson, Jaimie M

2015-08-01

One of the main goals of brain-machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surgically implantable sensors) and output modalities (e.g. control of computer systems, prosthetic arms, and functional electrical stimulation systems). While these technologies may eventually provide some level of benefit, they each carry associated burdens for end-users. We sought to assess the attitudes of people with paralysis toward using various technologies to achieve particular benefits, given the burdens currently associated with the use of each system. We designed and distributed a technology survey to determine the level of benefit necessary for people with tetraplegia due to spinal cord injury to consider using different technologies, given the burdens currently associated with them. The survey queried user preferences for 8 BMI technologies including electroencephalography, electrocorticography, and intracortical microelectrode arrays, as well as a commercially available eye tracking system for comparison. Participants used a 5-point scale to rate their likelihood to adopt these technologies for 13 potential control capabilities. Survey respondents were most likely to adopt BMI technology to restore some of their natural upper extremity function, including restoration of hand grasp and/or some degree of natural arm movement. High speed typing and control of a fast robot arm were also of interest to this population. Surgically implanted wireless technologies were twice as 'likely' to be adopted as their wired equivalents. Assessing end-user preferences is an essential prerequisite to the design and implementation of any assistive technology. The results of this survey suggest that people with tetraplegia would adopt an unobtrusive, autonomous BMI system for both restoration of upper extremity function and control of external devices such as communication interfaces.

A new therapeutic application of brain-machine interface (BMI) training followed by hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy for patients with severe hemiparetic stroke: A proof of concept study.

PubMed

Kawakami, Michiyuki; Fujiwara, Toshiyuki; Ushiba, Junichi; Nishimoto, Atsuko; Abe, Kaoru; Honaga, Kaoru; Nishimura, Atsuko; Mizuno, Katsuhiro; Kodama, Mitsuhiko; Masakado, Yoshihisa; Liu, Meigen

2016-09-21

Hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy improved paretic upper extremity motor function in patients with severe to moderate hemiparesis. We hypothesized that brain machine interface (BMI) training would be able to increase paretic finger muscle activity enough to apply HANDS therapy in patients with severe hemiparesis, whose finger extensor was absent. The aim of this study was to assess the efficacy of BMI training followed by HANDS therapy in patients with severe hemiparesis. Twenty-nine patients with chronic stroke who could not extend their paretic fingers were participated this study. We applied BMI training for 10 days at 40 min per day. The BMI detected the patients' motor imagery of paretic finger extension with event-related desynchronization (ERD) over the affected primary sensorimotor cortex, recorded with electroencephalography. Patients wore a motor-driven orthosis, which extended their paretic fingers and was triggered with ERD. When muscle activity in their paretic fingers was detected with surface electrodes after 10 days of BMI training, we applied HANDS therapy for the following 3 weeks. In HANDS therapy, participants received closed-loop, electromyogram-controlled, neuromuscular electrical stimulation (NMES) combined with a wrist-hand splint for 3 weeks at 8 hours a day. Before BMI training, after BMI training, after HANDS therapy and 3month after HANDS therapy, we assessed Fugl-Meyer Assessment upper extremity motor score (FMA) and the Motor Activity Log14-Amount of Use (MAL-AOU) score. After 10 days of BMI training, finger extensor activity had appeared in 21 patients. Eighteen of 21 patients then participated in 3 weeks of HANDS therapy. We found a statistically significant improvement in the FMA and the MAL-AOU scores after the BMI training, and further improvement was seen after the HANDS therapy. Combining BMI training with HANDS therapy could be an effective therapeutic strategy for severe UE paralysis after stroke.

Invasive brain-machine interfaces: a survey of paralyzed patients' attitudes, knowledge and methods of information retrieval.

PubMed

Lahr, Jacob; Schwartz, Christina; Heimbach, Bernhard; Aertsen, Ad; Rickert, Jörn; Ball, Tonio

2015-08-01

Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Invasive brain-machine interfaces: a survey of paralyzed patients’ attitudes, knowledge and methods of information retrieval

NASA Astrophysics Data System (ADS)

Lahr, Jacob; Schwartz, Christina; Heimbach, Bernhard; Aertsen, Ad; Rickert, Jörn; Ball, Tonio

2015-08-01

Objective. Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Approach. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. Main results. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Significance. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled.

PubMed

Jeyabalan, Vickneswaran; Samraj, Andrews; Loo, Chu Kiong

2010-10-01

Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.

Soft brain-machine interfaces for assistive robotics: A novel control approach.

PubMed

Schiatti, Lucia; Tessadori, Jacopo; Barresi, Giacinto; Mattos, Leonardo S; Ajoudani, Arash

2017-07-01

Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.

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 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.

Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces

NASA Astrophysics Data System (ADS)

Perruchoud, David; Pisotta, Iolanda; Carda, Stefano; Murray, Micah M.; Ionta, Silvio

2016-08-01

Objective. Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed. Approach. The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI’s actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms. Main results. Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users’ needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications. Significance. The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.

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

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.

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 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.

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface

PubMed Central

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them. PMID:24567704

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

PubMed

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

Causal network in a deafferented non-human primate brain.

PubMed

Balasubramanian, Karthikeyan; Takahashi, Kazutaka; Hatsopoulos, Nicholas G

2015-01-01

De-afferented/efferented neural ensembles can undergo causal changes when interfaced to neuroprosthetic devices. These changes occur via recruitment or isolation of neurons, alterations in functional connectivity within the ensemble and/or changes in the role of neurons, i.e., excitatory/inhibitory. In this work, emergence of a causal network and changes in the dynamics are demonstrated for a deafferented brain region exposed to BMI (brain-machine interface) learning. The BMI was controlling a robot for reach-and-grasp behavior. And, the motor cortical regions used for the BMI were deafferented due to chronic amputation, and ensembles of neurons were decoded for velocity control of the multi-DOF robot. A generalized linear model-framework based Granger causality (GLM-GC) technique was used in estimating the ensemble connectivity. Model selection was based on the AIC (Akaike Information Criterion).

Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

DTIC Science & Technology

2011-01-01

open-source BMI software solu- tions are currently available, we feel that the Craniux software package fills a specific need in the realm of BMI...data, such as cortical source imaging using EEG or MEG recordings. It is with these characteristics in mind that we feel the Craniux software package...S. Adee, “Dean Kamen’s ‘luke arm’ prosthesis readies for clinical trials,” IEEE Spectrum, February 2008, http://spectrum .ieee.org/biomedical

Changes in neuromuscular activity during motor training with a body-machine interface after spinal cord injury.

PubMed

Pierella, C; De Luca, A; Tasso, E; Cervetto, F; Gamba, S; Losio, L; Quinland, E; Venegoni, A; Mandraccia, S; Muller, I; Massone, A; Mussa-Ivaldi, F A; Casadio, M

2017-07-01

Body machine interfaces (BMIs) are used by people with severe motor disabilities to control external devices, but they also offer the opportunity to focus on rehabilitative goals. In this study we introduced in a clinical setting a BMI that was integrated by the therapists in the rehabilitative treatments of 2 spinal cord injured (SCI) subjects for 5 weeks. The BMI mapped the user's residual upper body mobility onto the two coordinates of a cursor on a screen. By controlling the cursor, the user engaged in playing computer games. The BMI allowed the mapping between body and cursor spaces to be modified, gradually challenging the user to exercise more impaired movements. With this approach, we were able to change our subjects' behavior, who initially used almost exclusively their proximal upper body-shoulders and arms - for using the BMI. By the end of training, cursor control was shifted toward more distal body regions - forearms instead of upper arms - with an increase of mobility and strength of all the degrees of freedom involved in the control. The clinical tests and the electromyographic signals from the main muscles of the upper body confirmed the positive effect of the training. Encouraging the subjects to explore different and sometimes unusual movement combinations was beneficial for recovering distal arm functions and for increasing their overall mobility.

Upper Body-Based Power Wheelchair Control Interface for Individuals with Tetraplegia

PubMed Central

Thorp, Elias B.; Abdollahi, Farnaz; Chen, David; Farshchiansadegh, Ali; Lee, Mei-Hua; Pedersen, Jessica; Pierella, Camilla; Roth, Elliot J.; Gonzalez, Ismael Seanez; Mussa-Ivaldi, Ferdinando A.

2016-01-01

Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user’s residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional controls commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control the power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control. PMID:26054071

An implantable integrated low-power amplifier-microelectrode array for Brain-Machine Interfaces.

PubMed

Patrick, Erin; Sankar, Viswanath; Rowe, William; Sanchez, Justin C; Nishida, Toshikazu

2010-01-01

One of the important challenges in designing Brain-Machine Interfaces (BMI) is to build implantable systems that have the ability to reliably process the activity of large ensembles of cortical neurons. In this paper, we report the design, fabrication, and testing of a polyimide-based microelectrode array integrated with a low-power amplifier as part of the Florida Wireless Integrated Recording Electrode (FWIRE) project at the University of Florida developing a fully implantable neural recording system for BMI applications. The electrode array was fabricated using planar micromachining MEMS processes and hybrid packaged with the amplifier die using a flip-chip bonding technique. The system was tested both on bench and in-vivo. Acute and chronic neural recordings were obtained from a rodent for a period of 42 days. The electrode-amplifier performance was analyzed over the chronic recording period with the observation of a noise floor of 4.5 microVrms, and an average signal-to-noise ratio of 3.8.

Single-trial dynamics of motor cortex and their applications to brain-machine interfaces

PubMed Central

Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Churchland, Mark M.; Cunningham, John P.; Shenoy, Krishna V.

2015-01-01

Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs. PMID:26220660

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

NASA Astrophysics Data System (ADS)

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

2018-04-01

Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

PubMed Central

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

2014-01-01

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled. PMID:24498055

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

PubMed Central

Su, Yi; Routhu, Sudhamayee; Moon, Kee S.; Lee, Sung Q.; Youm, WooSub; Ozturk, Yusuf

2016-01-01

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time. PMID:27669264

Assessment of brain-machine interfaces from the perspective of people with paralysis

NASA Astrophysics Data System (ADS)

Blabe, Christine H.; Gilja, Vikash; Chestek, Cindy A.; Shenoy, Krishna V.; Anderson, Kim D.; Henderson, Jaimie M.

2015-08-01

Objective. One of the main goals of brain-machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surgically implantable sensors) and output modalities (e.g. control of computer systems, prosthetic arms, and functional electrical stimulation systems). While these technologies may eventually provide some level of benefit, they each carry associated burdens for end-users. We sought to assess the attitudes of people with paralysis toward using various technologies to achieve particular benefits, given the burdens currently associated with the use of each system. Approach. We designed and distributed a technology survey to determine the level of benefit necessary for people with tetraplegia due to spinal cord injury to consider using different technologies, given the burdens currently associated with them. The survey queried user preferences for 8 BMI technologies including electroencephalography, electrocorticography, and intracortical microelectrode arrays, as well as a commercially available eye tracking system for comparison. Participants used a 5-point scale to rate their likelihood to adopt these technologies for 13 potential control capabilities. Main Results. Survey respondents were most likely to adopt BMI technology to restore some of their natural upper extremity function, including restoration of hand grasp and/or some degree of natural arm movement. High speed typing and control of a fast robot arm were also of interest to this population. Surgically implanted wireless technologies were twice as ‘likely’ to be adopted as their wired equivalents. Significance. Assessing end-user preferences is an essential prerequisite to the design and implementation of any assistive technology. The results of this survey suggest that people with tetraplegia would adopt an unobtrusive, autonomous BMI system for both restoration of upper extremity function and control of external devices such as communication interfaces.

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

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients.

PubMed

Donati, Ana R C; Shokur, Solaiman; Morya, Edgard; Campos, Debora S F; Moioli, Renan C; Gitti, Claudia M; Augusto, Patricia B; Tripodi, Sandra; Pires, Cristhiane G; Pereira, Gislaine A; Brasil, Fabricio L; Gallo, Simone; Lin, Anthony A; Takigami, Angelo K; Aratanha, Maria A; Joshi, Sanjay; Bleuler, Hannes; Cheng, Gordon; Rudolph, Alan; Nicolelis, Miguel A L

2016-08-11

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

PubMed Central

Donati, Ana R. C.; Shokur, Solaiman; Morya, Edgard; Campos, Debora S. F.; Moioli, Renan C.; Gitti, Claudia M.; Augusto, Patricia B.; Tripodi, Sandra; Pires, Cristhiane G.; Pereira, Gislaine A.; Brasil, Fabricio L.; Gallo, Simone; Lin, Anthony A.; Takigami, Angelo K.; Aratanha, Maria A.; Joshi, Sanjay; Bleuler, Hannes; Cheng, Gordon; Rudolph, Alan; Nicolelis, Miguel A. L.

2016-01-01

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3–13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage. PMID:27513629

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.

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.

Brain-machine interfaces: electrophysiological challenges and limitations.

PubMed

Lega, Bradley C; Serruya, Mijail D; Zaghloul, Kareem A

2011-01-01

Brain-machine interfaces (BMI) seek to directly communicate with the human nervous system in order to diagnose and treat intrinsic neurological disorders. While the first generation of these devices has realized significant clinical successes, they often rely on gross electrical stimulation using empirically derived parameters through open-loop mechanisms of action that are not yet fully understood. Their limitations reflect the inherent challenge in developing the next generation of these devices. This review identifies lessons learned from the first generation of BMI devices (chiefly deep brain stimulation), identifying key problems for which the solutions will aid the development of the next generation of technologies. Our analysis examines four hypotheses for the mechanism by which brain stimulation alters surrounding neurophysiologic activity. We then focus on motor prosthetics, describing various approaches to overcoming the problems of decoding neural signals. We next turn to visual prosthetics, an area for which the challenges of signal coding to match neural architecture has been partially overcome. Finally, we close with a review of cortical stimulation, examining basic principles that will be incorporated into the design of future devices. Throughout the review, we relate the issues of each specific topic to the common thread of BMI research: translating new knowledge of network neuroscience into improved devices for neuromodulation.

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.

Visual Feedback Dominates the Sense of Agency for Brain-Machine Actions

PubMed Central

Evans, Nathan; Gale, Steven; Schurger, Aaron; Blanke, Olaf

2015-01-01

Recent advances in neuroscience and engineering have led to the development of technologies that permit the control of external devices through real-time decoding of brain activity (brain-machine interfaces; BMI). Though the feeling of controlling bodily movements (sense of agency; SOA) has been well studied and a number of well-defined sensorimotor and cognitive mechanisms have been put forth, very little is known about the SOA for BMI-actions. Using an on-line BMI, and verifying that our subjects achieved a reasonable level of control, we sought to describe the SOA for BMI-mediated actions. Our results demonstrate that discrepancies between decoded neural activity and its resultant real-time sensory feedback are associated with a decrease in the SOA, similar to SOA mechanisms proposed for bodily actions. However, if the feedback discrepancy serves to correct a poorly controlled BMI-action, then the SOA can be high and can increase with increasing discrepancy, demonstrating the dominance of visual feedback on the SOA. Taken together, our results suggest that bodily and BMI-actions rely on common mechanisms of sensorimotor integration for agency judgments, but that visual feedback dominates the SOA in the absence of overt bodily movements or proprioceptive feedback, however erroneous the visual feedback may be. PMID:26066840

Development of an implantable wireless ECoG 128ch recording device for clinical brain machine interface.

PubMed

Matsushita, Kojiro; Hirata, Masayuki; Suzuki, Takafumi; Ando, Hiroshi; Ota, Yuki; Sato, Fumihiro; Morris, Shyne; Yoshida, Takeshi; Matsuki, Hidetoshi; Yoshimine, Toshiki

2013-01-01

Brain Machine Interface (BMI) is a system that assumes user's intention by analyzing user's brain activities and control devices with the assumed intention. It is considered as one of prospective tools to enhance paralyzed patients' quality of life. In our group, we especially focus on ECoG (electro-corti-gram)-BMI, which requires surgery to place electrodes on the cortex. We try to implant all the devices within the patient's head and abdomen and to transmit the data and power wirelessly. Our device consists of 5 parts: (1) High-density multi-electrodes with a 3D shaped sheet fitting to the individual brain surface to effectively record the ECoG signals; (2) A small circuit board with two integrated circuit chips functioning 128 [ch] analogue amplifiers and A/D converters for ECoG signals; (3) A Wifi data communication & control circuit with the target PC; (4) A non-contact power supply transmitting electrical power minimum 400[mW] to the device 20[mm] away. We developed those devices, integrated them, and, investigated the performance.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

PubMed

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

PubMed Central

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal. PMID:26042002

Neurosurgery and the dawning age of Brain-Machine Interfaces

PubMed Central

Rowland, Nathan C.; Breshears, Jonathan; Chang, Edward F.

2013-01-01

Brain–machine interfaces (BMIs) are on the horizon for clinical neurosurgery. Electrocorticography-based platforms are less invasive than implanted microelectrodes, however, the latter are unmatched in their ability to achieve fine motor control of a robotic prosthesis capable of natural human behaviors. These technologies will be crucial to restoring neural function to a large population of patients with severe neurologic impairment – including those with spinal cord injury, stroke, limb amputation, and disabling neuromuscular disorders such as amyotrophic lateral sclerosis. On the opposite end of the spectrum are neural enhancement technologies for specialized applications such as combat. An ongoing ethical dialogue is imminent as we prepare for BMI platforms to enter the neurosurgical realm of clinical management. PMID:23653884

Induced sensorimotor brain plasticity controls pain in phantom limb patients

PubMed Central

Yanagisawa, Takufumi; Fukuma, Ryohei; Seymour, Ben; Hosomi, Koichi; Kishima, Haruhiko; Shimizu, Takeshi; Yokoi, Hiroshi; Hirata, Masayuki; Yoshimine, Toshiki; Kamitani, Yukiyasu; Saitoh, Youichi

2016-01-01

The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback. PMID:27807349

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

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

Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient.

PubMed

Resquin, F; Ibañez, J; Gonzalez-Vargas, J; Brunetti, F; Dimbwadyo, I; Alves, S; Carrasco, L; Torres, L; Pons, Jose Luis

2016-08-01

Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.

Offline decoding of end-point forces using neural ensembles: application to a brain-machine interface.

PubMed

Gupta, Rahul; Ashe, James

2009-06-01

Brain-machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories--those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future.

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.

Control of a 2 DoF robot using a brain-machine interface.

PubMed

Hortal, Enrique; Ubeda, Andrés; Iáñez, Eduardo; Azorín, José M

2014-09-01

In this paper, a non-invasive spontaneous Brain-Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University of Elche (Spain). Two control strategies are compared: hierarchical and directional control. The experimental test (performed by four users) consists of reaching four targets. The errors and time used during the performance of the tests are compared in both control strategies (hierarchical and directional control). The advantages and disadvantages of each method are shown after the analysis of the results. The hierarchical control allows an accurate approaching to the goals but it is slower than using the directional control which, on the contrary, is less precise. The results show both strategies are useful to control this planar robot. In the future, by adding an extra device like a gripper, this BMI could be used in assistive applications such as grasping daily objects in a realistic environment. In order to compare the behavior of the system taking into account the opinion of the users, a NASA Tasks Load Index (TLX) questionnaire is filled out after two sessions are completed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.

PubMed

He, Yongtian; Nathan, Kevin; Venkatakrishnan, Anusha; Rovekamp, Roger; Beck, Christopher; Ozdemir, Recep; Francisco, Gerard E; Contreras-Vidal, Jose L

2014-01-01

Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASA's powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals--the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton--is demonstrated.

A chronic generalized bi-directional brain-machine interface.

PubMed

Rouse, A G; Stanslaski, S R; Cong, P; Jensen, R M; Afshar, P; Ullestad, D; Gupta, R; Molnar, G F; Moran, D W; Denison, T J

2011-06-01

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

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

EXiO-A Brain-Controlled Lower Limb Exoskeleton for Rhesus Macaques.

PubMed

Vouga, Tristan; Zhuang, Katie Z; Olivier, Jeremy; Lebedev, Mikhail A; Nicolelis, Miguel A L; Bouri, Mohamed; Bleuler, Hannes

2017-02-01

Recent advances in the field of brain-machine interfaces (BMIs) have demonstrated enormous potential to shape the future of rehabilitation and prosthetic devices. Here, a lower-limb exoskeleton controlled by the intracortical activity of an awake behaving rhesus macaque is presented as a proof-of-concept for a locomotorBMI. A detailed description of the mechanical device, including its innovative features and first experimental results, is provided. During operation, BMI-decoded position and velocity are directly mapped onto the bipedal exoskeleton's motions, which then move the monkey's legs as the monkey remains physicallypassive. To meet the unique requirements of such an application, the exoskeleton's features include: high output torque with backdrivable actuation, size adjustability, and safe user-robot interface. In addition, a novel rope transmission is introduced and implemented. To test the performance of the exoskeleton, a mechanical assessment was conducted, which yielded quantifiable results for transparency, efficiency, stiffness, and tracking performance. Usage under both brain control and automated actuation demonstrates the device's capability to fulfill the demanding needs of this application. These results lay the groundwork for further advancement in BMI-controlled devices for primates including humans.

Rapid control and feedback rates enhance neuroprosthetic control

PubMed Central

Shanechi, Maryam M.; Orsborn, Amy L.; Moorman, Helene G.; Gowda, Suraj; Dangi, Siddharth; Carmena, Jose M.

2017-01-01

Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject. We developed a new BMI that allows arbitrarily fast control and feedback rates, and used it to dissociate the effects of each rate in two monkeys. Increasing the control rate significantly improved control even when feedback rate was unchanged. Increasing the feedback rate further facilitated control. We also show that our high-rate BMI significantly outperformed state-of-the-art methods due to higher control and feedback rates, combined with a different point process mathematical encoding model. Our BMI paradigm can dissect the contribution of different elements in the sensorimotor pathway, providing a unique tool for studying neuroprosthetic control mechanisms. PMID:28059065

Rapid control and feedback rates enhance neuroprosthetic control

NASA Astrophysics Data System (ADS)

Shanechi, Maryam M.; Orsborn, Amy L.; Moorman, Helene G.; Gowda, Suraj; Dangi, Siddharth; Carmena, Jose M.

2017-01-01

Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject. We developed a new BMI that allows arbitrarily fast control and feedback rates, and used it to dissociate the effects of each rate in two monkeys. Increasing the control rate significantly improved control even when feedback rate was unchanged. Increasing the feedback rate further facilitated control. We also show that our high-rate BMI significantly outperformed state-of-the-art methods due to higher control and feedback rates, combined with a different point process mathematical encoding model. Our BMI paradigm can dissect the contribution of different elements in the sensorimotor pathway, providing a unique tool for studying neuroprosthetic control mechanisms.

Parsing learning in networks using brain-machine interfaces.

PubMed

Orsborn, Amy L; Pesaran, Bijan

2017-10-01

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training

PubMed Central

Curado, Marco Rocha; Cossio, Eliana Garcia; Broetz, Doris; Agostini, Manuel; Cho, Woosang; Brasil, Fabricio Lima; Yilmaz, Oezge; Liberati, Giulia; Lepski, Guilherme

2015-01-01

Background Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies. Methods Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity. Results Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001). Conclusions Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice. PMID:26495971

Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training.

PubMed

Curado, Marco Rocha; Cossio, Eliana Garcia; Broetz, Doris; Agostini, Manuel; Cho, Woosang; Brasil, Fabricio Lima; Yilmaz, Oezge; Liberati, Giulia; Lepski, Guilherme; Birbaumer, Niels; Ramos-Murguialday, Ander

2015-01-01

Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies. Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity. Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001). Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice.

A brain-machine interface for control of medically-induced coma.

PubMed

Shanechi, Maryam M; Chemali, Jessica J; Liberman, Max; Solt, Ken; Brown, Emery N

2013-10-01

Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.

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.

Virtual reality hardware and graphic display options for brain-machine interfaces

PubMed Central

Marathe, Amar R.; Carey, Holle L.; Taylor, Dawn M.

2009-01-01

Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where able-bodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing. PMID:18006069

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

NASA Astrophysics Data System (ADS)

Rajangam, Sankaranarayani; Tseng, Po-He; Yin, Allen; Lehew, Gary; Schwarz, David; Lebedev, Mikhail A.; Nicolelis, Miguel A. L.

2016-03-01

Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

Unscented Kalman Filter for Brain-Machine Interfaces

PubMed Central

Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.

2009-01-01

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074

Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain.

PubMed

Sakurai, Yoshio

2014-01-01

This perspective emphasizes that the brain-machine interface (BMI) research has the potential to clarify major mysteries of the brain and that such clarification of the mysteries by neuroscience is needed to develop BMIs. I enumerate five principal mysteries. The first is "how is information encoded in the brain?" This is the fundamental question for understanding what our minds are and is related to the verification of Hebb's cell assembly theory. The second is "how is information distributed in the brain?" This is also a reconsideration of the functional localization of the brain. The third is "what is the function of the ongoing activity of the brain?" This is the problem of how the brain is active during no-task periods and what meaning such spontaneous activity has. The fourth is "how does the bodily behavior affect the brain function?" This is the problem of brain-body interaction, and obtaining a new "body" by a BMI leads to a possibility of changes in the owner's brain. The last is "to what extent can the brain induce plasticity?" Most BMIs require changes in the brain's neuronal activity to realize higher performance, and the neuronal operant conditioning inherent in the BMIs further enhances changes in the activity.

A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

PubMed Central

O'Doherty, Joseph E.; Lebedev, Mikhail A.; Hanson, Timothy L.; Fitzsimmons, Nathan A.; Nicolelis, Miguel A. L.

2009-01-01

Brain–machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine–brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices. PMID:19750199

Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain-machine interfaces

NASA Astrophysics Data System (ADS)

Abbott, W. W.; Faisal, A. A.

2012-08-01

Eye movements are highly correlated with motor intentions and are often retained by patients with serious motor deficiencies. Despite this, eye tracking is not widely used as control interface for movement in impaired patients due to poor signal interpretation and lack of control flexibility. We propose that tracking the gaze position in 3D rather than 2D provides a considerably richer signal for human machine interfaces by allowing direct interaction with the environment rather than via computer displays. We demonstrate here that by using mass-produced video-game hardware, it is possible to produce an ultra-low-cost binocular eye-tracker with comparable performance to commercial systems, yet 800 times cheaper. Our head-mounted system has 30 USD material costs and operates at over 120 Hz sampling rate with a 0.5-1 degree of visual angle resolution. We perform 2D and 3D gaze estimation, controlling a real-time volumetric cursor essential for driving complex user interfaces. Our approach yields an information throughput of 43 bits s-1, more than ten times that of invasive and semi-invasive brain-machine interfaces (BMIs) that are vastly more expensive. Unlike many BMIs our system yields effective real-time closed loop control of devices (10 ms latency), after just ten minutes of training, which we demonstrate through a novel BMI benchmark—the control of the video arcade game ‘Pong’.

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2008-01-01

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

Internal models for interpreting neural population activity during sensorimotor control

PubMed Central

Golub, Matthew D; Yu, Byron M; Chase, Steven M

2015-01-01

To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects’ internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output. DOI: http://dx.doi.org/10.7554/eLife.10015.001 PMID:26646183

Advances in neuroprosthetic learning and control.

PubMed

Carmena, Jose M

2013-01-01

Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.

Advances in Neuroprosthetic Learning and Control

PubMed Central

Carmena, Jose M.

2013-01-01

Significant progress has occurred in the field of brain–machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action. PMID:23700383

A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces

PubMed Central

Prins, Noeline W.; Sanchez, Justin C.; Prasad, Abhishek

2014-01-01

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration. PMID:24904257

Physiological properties of brain-machine interface input signals.

PubMed

Slutzky, Marc W; Flint, Robert D

2017-08-01

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability. Copyright © 2017 the American Physiological Society.

A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback

PubMed Central

Klaes, Christian; Shi, Ying; Kellis, Spencer; Minxha, Juri; Revechkis, Boris; Andersen, Richard A.

2015-01-01

Present day cortical brain machine interfaces (BMI) have made impressive advances using decoded brain signals to control extracorporeal devices. Although BMIs are used in a closed-loop fashion, sensory feedback typically is visual only. However medical case studies have shown that the loss of somesthesis in a limb greatly reduces the agility of the limb even when visual feedback is available (for review see Robles-De-La-Torre, 2006). To overcome this limitation, this study tested a closed-loop BMI that utilizes intracortical microstimulation (ICMS) to provide ‘tactile’ sensation to a non-human primate (NHP). Using stimulation electrodes in Brodmann area 1 of somatosensory cortex (BA1) and recording electrodes in the anterior intraparietal area (AIP), the parietal reach region (PRR) and dorsal area 5 (area 5d), it was found that this form of feedback can be used in BMI tasks. PMID:25242377

Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

PubMed

Vaidya, Mukta; Balasubramanian, Karthikeyan; Southerland, Joshua; Badreldin, Islam; Eleryan, Ahmed; Shattuck, Kelsey; Gururangan, Suchin; Slutzky, Marc; Osborne, Leslie; Fagg, Andrew; Oweiss, Karim; Hatsopoulos, Nicholas G

2018-04-01

The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW & NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural plasticity in M1 associated with learning to use a brain-machine interface.

Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

PubMed Central

McMullen, David P.; Hotson, Guy; Katyal, Kapil D.; Wester, Brock A.; Fifer, Matthew S.; McGee, Timothy G.; Harris, Andrew; Johannes, Matthew S.; Vogelstein, R. Jacob; Ravitz, Alan D.; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

2014-01-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

PubMed

McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

2014-07-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain

PubMed Central

Sakurai, Yoshio

2014-01-01

This perspective emphasizes that the brain-machine interface (BMI) research has the potential to clarify major mysteries of the brain and that such clarification of the mysteries by neuroscience is needed to develop BMIs. I enumerate five principal mysteries. The first is “how is information encoded in the brain?” This is the fundamental question for understanding what our minds are and is related to the verification of Hebb’s cell assembly theory. The second is “how is information distributed in the brain?” This is also a reconsideration of the functional localization of the brain. The third is “what is the function of the ongoing activity of the brain?” This is the problem of how the brain is active during no-task periods and what meaning such spontaneous activity has. The fourth is “how does the bodily behavior affect the brain function?” This is the problem of brain-body interaction, and obtaining a new “body” by a BMI leads to a possibility of changes in the owner’s brain. The last is “to what extent can the brain induce plasticity?” Most BMIs require changes in the brain’s neuronal activity to realize higher performance, and the neuronal operant conditioning inherent in the BMIs further enhances changes in the activity. PMID:24904323

A novel bioelectronic tongue in vivo for highly sensitive bitterness detection with brain-machine interface.

PubMed

Qin, Zhen; Zhang, Bin; Hu, Liang; Zhuang, Liujing; Hu, Ning; Wang, Ping

2016-04-15

Animals' gustatory system has been widely acknowledged as one of the most sensitive chemosensing systems, especially for its ability to detect bitterness. Since bitterness usually symbolizes inedibility, the potential to use rodent's gustatory system is investigated to detect bitter compounds. In this work, the extracellular potentials of a group of neurons are recorded by chronically coupling microelectrode array to rat's gustatory cortex with brain-machine interface (BMI) technology. Local field potentials (LFPs), which represent the electrophysiological activity of neural networks, are chosen as target signals due to stable response patterns across trials and are further divided into different oscillations. As a result, different taste qualities yield quality-specific LFPs in time domain which suggests the selectivity of this in vivo bioelectronic tongue. Meanwhile, more quantitative study in frequency domain indicates that the post-stimulation power of beta and low gamma oscillations shows dependence with concentrations of denatonium benzoate, a prototypical bitter compound, and the limit of detection is deduced to be 0.076 μM, which is two orders lower than previous in vitro bioelectronic tongues and conventional electronic tongues. According to the results, this in vivo bioelectronic tongue in combination with BMI presents a promising method in highly sensitive bitterness detection and is supposed to provide new platform in measuring bitterness degree. Copyright © 2015 Elsevier B.V. All rights reserved.

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

PubMed

Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

2008-01-01

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

PubMed

Balasubramanian, Karthikeyan; Southerland, Joshua; Vaidya, Mukta; Qian, Kai; Eleryan, Ahmed; Fagg, Andrew H; Sluzky, Marc; Oweiss, Karim; Hatsopoulos, Nicholas

2013-01-01

Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

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.

Reversible large–scale modification of cortical networks during neuroprosthetic control

PubMed Central

Ganguly, Karunesh; Wallis, Jonathan D.

2012-01-01

Brain-Machine Interfaces (BMI) provide a framework to study cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter ‘direct neurons’). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, here we show that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Interestingly, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison to the direct activity. These widespread differential changes in the direct and indirect population activity were remarkably stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control. PMID:21499255

Toward a whole-body neuroprosthetic.

PubMed

Lebedev, Mikhail A; Nicolelis, Miguel A L

2011-01-01

Brain-machine interfaces (BMIs) hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurological diseases, and limb loss. Considerable progress has been achieved in BMIs that enact arm movements, and initial work has been done on BMIs for lower limb and trunk control. These developments put Duke University Center for Neuroengineering in the position to develop the first BMI for whole-body control. This whole-body BMI will incorporate very large-scale brain recordings, advanced decoding algorithms, artificial sensory feedback based on electrical stimulation of somatosensory areas, virtual environment representations, and a whole-body exoskeleton. This system will be first tested in nonhuman primates and then transferred to clinical trials in humans. Copyright © 2011 Elsevier B.V. All rights reserved.

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics.

PubMed

Prins, Noeline W; Sanchez, Justin C; Prasad, Abhishek

2017-06-01

For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor's weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the 'ambiguous' region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture. In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold. The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics

NASA Astrophysics Data System (ADS)

Prins, Noeline W.; Sanchez, Justin C.; Prasad, Abhishek

2017-06-01

Objective. For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. Approach. Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor’s weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the ‘ambiguous’ region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture. Main results. In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold. Significance: The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.

Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models.

PubMed

Lee, Giljae; Matsunaga, Andréa; Dura-Bernal, Salvador; Zhang, Wenjie; Lytton, William W; Francis, Joseph T; Fortes, José Ab

2014-11-01

Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with in vivo data acquisition devices and prosthetic devices. The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.

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.

Neural Coding for Effective Rehabilitation

PubMed Central

2014-01-01

Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices. PMID:25258708

A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback.

PubMed

Klaes, Christian; Shi, Ying; Kellis, Spencer; Minxha, Juri; Revechkis, Boris; Andersen, Richard A

2014-10-01

Present day cortical brain-machine interfaces (BMIs) have made impressive advances using decoded brain signals to control extracorporeal devices. Although BMIs are used in a closed-loop fashion, sensory feedback typically is visual only. However medical case studies have shown that the loss of somesthesis in a limb greatly reduces the agility of the limb even when visual feedback is available. To overcome this limitation, this study tested a closed-loop BMI that utilizes intracortical microstimulation to provide 'tactile' sensation to a non-human primate. Using stimulation electrodes in Brodmann area 1 of somatosensory cortex (BA1) and recording electrodes in the anterior intraparietal area, the parietal reach region and dorsal area 5 (area 5d), it was found that this form of feedback can be used in BMI tasks. Providing somatosensory feedback has the poyential to greatly improve the performance of cognitive neuroprostheses especially for fine control and object manipulation. Adding stimulation to a BMI system could therefore improve the quality of life for severely paralyzed patients.

Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

PubMed

Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

2014-03-15

The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation.

PubMed

López-Larraz, Eduardo; Trincado-Alonso, Fernando; Rajasekaran, Vijaykumar; Pérez-Nombela, Soraya; Del-Ama, Antonio J; Aranda, Joan; Minguez, Javier; Gil-Agudo, Angel; Montesano, Luis

2016-01-01

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).

Control of an Ambulatory Exoskeleton with a Brain–Machine Interface for Spinal Cord Injury Gait Rehabilitation

PubMed Central

López-Larraz, Eduardo; Trincado-Alonso, Fernando; Rajasekaran, Vijaykumar; Pérez-Nombela, Soraya; del-Ama, Antonio J.; Aranda, Joan; Minguez, Javier; Gil-Agudo, Angel; Montesano, Luis

2016-01-01

The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain–machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton—without any weight or balance support—for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation). PMID:27536214

Feasibility study for future implantable neural-silicon interface devices.

PubMed

Al-Armaghany, Allann; Yu, Bo; Mak, Terrence; Tong, Kin-Fai; Sun, Yihe

2011-01-01

The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures.

Observation-based training for neuroprosthetic control of grasping by amputees.

PubMed

Agashe, Harshavardhan A; Contreras-Vidal, Jose L

2014-01-01

Current brain-machine interfaces (BMIs) allow upper limb amputees to position robotic arms with a high degree of accuracy, but lack the ability to control hand pre-shaping for grasping different objects. We have previously shown that low frequency (0.1-1 Hz) time domain cortical activity recorded at the scalp via electroencephalography (EEG) encodes information about grasp pre-shaping. To transfer this technology to clinical populations such as amputees, the challenge lies in constructing BMI models in the absence of overt training hand movements. Here we show that it is possible to train BMI models using observed grasping movements performed by a robotic hand attached to amputees' residual limb. Three transradial amputees controlled the grasping motion of an attached robotic hand via their EEG, following the action-observation training phase. Over multiple sessions, subjects successfully grasped the presented object (a bottle or a credit card) in 53±16 % of trials, demonstrating the validity of the BMI models. Importantly, the validation of the BMI model was through closed-loop performance, which demonstrates generalization of the model to unseen data. These results suggest `mirror neuron system' properties captured by delta band EEG that allows neural representation for action observation to be used for action control in an EEG-based BMI system.

Adaptation to a cortex controlled robot attached at the pelvis and engaged during locomotion in rats

PubMed Central

Song, Weiguo; Giszter, Simon F.

2011-01-01

Brain Machine Interfaces (BMIs) should ideally show robust adaptation of the BMI across different tasks and daily activities. Most BMIs have used over-practiced tasks. Little is known about BMIs in dynamic environments. How are mechanically body-coupled BMIs integrated into ongoing rhythmic dynamics, e.g., in locomotion? To examine this we designed a novel BMI using neural discharge in the hindlimb/trunk motor cortex in rats during locomotion to control a robot attached at the pelvis. We tested neural adaptation when rats experienced (a) control locomotion, (b) ‘simple elastic load’ (a robot load on locomotion without any BMI neural control) and (c) ‘BMI with elastic load’ (in which the robot loaded locomotion and a BMI neural control could counter this load). Rats significantly offset applied loads with the BMI while preserving more normal pelvic height compared to load alone. Adaptation occurred over about 100–200 step cycles in a trial. Firing rates increased in both the loaded conditions compared to baseline. Mean phases of cells’ discharge in the step cycle shifted significantly between BMI and the simple load condition. Over time more BMI cells became positively correlated with the external force and modulated more deeply, and neurons’ network correlations on a 100ms timescale increased. Loading alone showed none of these effects. The BMI neural changes of rate and force correlations persisted or increased over repeated trials. Our results show that rats have the capacity to use motor adaptation and motor learning to fairly rapidly engage hindlimb/trunk coupled BMIs in their locomotion. PMID:21414932

Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.

PubMed

Peikon, Ian D; Fitzsimmons, Nathan A; Lebedev, Mikhail A; Nicolelis, Miguel A L

2009-06-15

Collection and analysis of limb kinematic data are essential components of the study of biological motion, including research into biomechanics, kinesiology, neurophysiology and brain-machine interfaces (BMIs). In particular, BMI research requires advanced, real-time systems capable of sampling limb kinematics with minimal contact to the subject's body. To answer this demand, we have developed an automated video tracking system for real-time tracking of multiple body parts in freely behaving primates. The system employs high-contrast markers painted on the animal's joints to continuously track the three-dimensional positions of their limbs during activity. Two-dimensional coordinates captured by each video camera are combined and converted to three-dimensional coordinates using a quadratic fitting algorithm. Real-time operation of the system is accomplished using direct memory access (DMA). The system tracks the markers at a rate of 52 frames per second (fps) in real-time and up to 100fps if video recordings are captured to be later analyzed off-line. The system has been tested in several BMI primate experiments, in which limb position was sampled simultaneously with chronic recordings of the extracellular activity of hundreds of cortical cells. During these recordings, multiple computational models were employed to extract a series of kinematic parameters from neuronal ensemble activity in real-time. The system operated reliably under these experimental conditions and was able to compensate for marker occlusions that occurred during natural movements. We propose that this system could also be extended to applications that include other classes of biological motion.

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.

Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces

NASA Astrophysics Data System (ADS)

Xu, Kai; Wang, Yiwen; Wang, Yueming; Wang, Fang; Hao, Yaoyao; Zhang, Shaomin; Zhang, Qiaosheng; Chen, Weidong; Zheng, Xiaoxiang

2013-04-01

Objective. The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task. Approach. Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons. Main results. The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset. Significance. The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different decoding models. It shows better robustness of identifying the important neurons with noisy signals presented. The low demand of computational resources which, reflected by the fast convergence, indicates the feasibility of the method applied in portable BMI systems. The ascertainment of the important neurons helps to inspect neural patterns visually associated with the movement task. The elimination of irrelevant neurons greatly reduces the computational burden of mBMI systems and maintains the performance with better robustness.

The Brainarium: An Interactive Immersive Tool for Brain Education, Art, and Neurotherapy

PubMed Central

2016-01-01

Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration. The Brainarium consists of a portable planetarium device that is being used as brain metaphor. This is done by projecting multimedia content on the planetarium dome and displaying EEG data recorded from a subject in real time using Brain Machine Interface (BMI) technologies. The system has been demonstrated through several performances involving an interaction between the subject controlling the BMI, a musician, and the audience during series of exhibitions and workshops in schools. We report here feedback from 134 participants who filled questionnaires to rate their experiences. Our results show improved subjective learning compared to conventional methods, improved entertainment value, improved absorption into the material being presented, and little discomfort. PMID:27698660

The Brainarium: An Interactive Immersive Tool for Brain Education, Art, and Neurotherapy.

PubMed

Grandchamp, Romain; Delorme, Arnaud

2016-01-01

Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration. The Brainarium consists of a portable planetarium device that is being used as brain metaphor. This is done by projecting multimedia content on the planetarium dome and displaying EEG data recorded from a subject in real time using Brain Machine Interface (BMI) technologies. The system has been demonstrated through several performances involving an interaction between the subject controlling the BMI, a musician, and the audience during series of exhibitions and workshops in schools. We report here feedback from 134 participants who filled questionnaires to rate their experiences. Our results show improved subjective learning compared to conventional methods, improved entertainment value, improved absorption into the material being presented, and little discomfort.

Reversible large-scale modification of cortical networks during neuroprosthetic control.

PubMed

Ganguly, Karunesh; Dimitrov, Dragan F; Wallis, Jonathan D; Carmena, Jose M

2011-05-01

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

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

Detection of Error Related Neuronal Responses Recorded by Electrocorticography in Humans during Continuous Movements

PubMed Central

Milekovic, Tomislav; Ball, Tonio; Schulze-Bonhage, Andreas; Aertsen, Ad; Mehring, Carsten

2013-01-01

Background Brain-machine interfaces (BMIs) can translate the neuronal activity underlying a user’s movement intention into movements of an artificial effector. In spite of continuous improvements, errors in movement decoding are still a major problem of current BMI systems. If the difference between the decoded and intended movements becomes noticeable, it may lead to an execution error. Outcome errors, where subjects fail to reach a certain movement goal, are also present during online BMI operation. Detecting such errors can be beneficial for BMI operation: (i) errors can be corrected online after being detected and (ii) adaptive BMI decoding algorithm can be updated to make fewer errors in the future. Methodology/Principal Findings Here, we show that error events can be detected from human electrocorticography (ECoG) during a continuous task with high precision, given a temporal tolerance of 300–400 milliseconds. We quantified the error detection accuracy and showed that, using only a small subset of 2×2 ECoG electrodes, 82% of detection information for outcome error and 74% of detection information for execution error available from all ECoG electrodes could be retained. Conclusions/Significance The error detection method presented here could be used to correct errors made during BMI operation or to adapt a BMI algorithm to make fewer errors in the future. Furthermore, our results indicate that smaller ECoG implant could be used for error detection. Reducing the size of an ECoG electrode implant used for BMI decoding and error detection could significantly reduce the medical risk of implantation. PMID:23383315

Decoding of top-down cognitive processing for SSVEP-controlled BMI

PubMed Central

Min, Byoung-Kyong; Dähne, Sven; Ahn, Min-Hee; Noh, Yung-Kyun; Müller, Klaus-Robert

2016-01-01

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting. PMID:27808125

Decoding of top-down cognitive processing for SSVEP-controlled BMI

NASA Astrophysics Data System (ADS)

Min, Byoung-Kyong; Dähne, Sven; Ahn, Min-Hee; Noh, Yung-Kyun; Müller, Klaus-Robert

2016-11-01

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.

Brain-machine interfaces for controlling lower-limb powered robotic systems.

PubMed

He, Yongtian; Eguren, David; Azorín, José M; Grossman, Robert G; Luu, Trieu Phat; Contreras-Vidal, Jose L

2018-04-01

Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking. To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared. Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others. We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.

Brain-machine interfaces for controlling lower-limb powered robotic systems

NASA Astrophysics Data System (ADS)

He, Yongtian; Eguren, David; Azorín, José M.; Grossman, Robert G.; Phat Luu, Trieu; Contreras-Vidal, Jose L.

2018-04-01

Objective. Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking. Approach. To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared. Main results. Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others. Significance. We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.

Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

PubMed

Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

2017-08-01

This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: a case study.

PubMed

Silvoni, Stefano; Cavinato, Marianna; Volpato, Chiara; Cisotto, Giulia; Genna, Clara; Agostini, Michela; Turolla, Andrea; Ramos-Murguialday, Ander; Piccione, Francesco

2013-01-01

In a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI) paradigm for upper limb motor-training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning. Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for 2 weeks coupling somatosensory brain oscillations with force-field control during a robot-mediated center-out motor-task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic, and motor-behavioral measures were recorded in pre- and post-assessments without force assistance. Patient's healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor-training coupling brain oscillations and force feedback during an actual movement.

Intention Concepts and Brain-Machine Interfacing

PubMed Central

Thinnes-Elker, Franziska; Iljina, Olga; Apostolides, John Kyle; Kraemer, Felicitas; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2012-01-01

Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret (“decode”) the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications. PMID:23162504

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.

Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques

NASA Astrophysics Data System (ADS)

Shimoda, Kentaro; Nagasaka, Yasuo; Chao, Zenas C.; Fujii, Naotaka

2012-06-01

Brain-machine interface (BMI) technology captures brain signals to enable control of prosthetic or communication devices with the goal of assisting patients who have limited or no ability to perform voluntary movements. Decoding of inherent information in brain signals to interpret the user's intention is one of main approaches for developing BMI technology. Subdural electrocorticography (sECoG)-based decoding provides good accuracy, but surgical complications are one of the major concerns for this approach to be applied in BMIs. In contrast, epidural electrocorticography (eECoG) is less invasive, thus it is theoretically more suitable for long-term implementation, although it is unclear whether eECoG signals carry sufficient information for decoding natural movements. We successfully decoded continuous three-dimensional hand trajectories from eECoG signals in Japanese macaques. A steady quantity of information of continuous hand movements could be acquired from the decoding system for at least several months, and a decoding model could be used for ˜10 days without significant degradation in accuracy or recalibration. The correlation coefficients between observed and predicted trajectories were lower than those for sECoG-based decoding experiments we previously reported, owing to a greater degree of chewing artifacts in eECoG-based decoding than is found in sECoG-based decoding. As one of the safest invasive recording methods available, eECoG provides an acceptable level of performance. With the ease of replacement and upgrades, eECoG systems could become the first-choice interface for real-life BMI applications.

Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators

PubMed Central

Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

2013-01-01

Operators of a pair of robotic hands report ownership for those hands when they hold image of a grasp motion and watch the robot perform it. We present a novel body ownership illusion that is induced by merely watching and controlling robot's motions through a brain machine interface. In past studies, body ownership illusions were induced by correlation of such sensory inputs as vision, touch and proprioception. However, in the presented illusion none of the mentioned sensations are integrated except vision. Our results show that during BMI-operation of robotic hands, the interaction between motor commands and visual feedback of the intended motions is adequate to incorporate the non-body limbs into one's own body. Our discussion focuses on the role of proprioceptive information in the mechanism of agency-driven illusions. We believe that our findings will contribute to improvement of tele-presence systems in which operators incorporate BMI-operated robots into their body representations. PMID:23928891

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.

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

Advanced Aircraft Interfaces: The Machine Side of the Man-Machine Interface (Les Interfaces sur les Avions de Pointe: L’Aspect Machine de l’Interface Homme-Machine)

DTIC Science & Technology

1992-10-01

Manager , Advanced Transport Operating Systems Program Office Langley Research Center Mail Stop 265 Hampton, VA 23665-5225 United States Programme Committee...J.H.Lind, and C.G.Burge Advanced Cockpit - Mission and Image Management 4 by J. Struck Aircrew Acceptance of Automation in the Cockpit 5 by M. Hicks and I...DESIGN CONCEPTS AND TOOLS A Systems Approach to the Advanced Aircraft Man-Machine Interface 23 by F. Armogida Management of Avionics Data in the Cockpit

Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

PubMed

Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

2018-01-01

Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness.

PubMed

Li, Gang; Chung, Wan-Young

2015-08-21

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.

A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

PubMed Central

Li, Gang; Chung, Wan-Young

2015-01-01

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness. PMID:26308002

A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions

PubMed Central

Kashihara, Koji

2014-01-01

Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients' emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based non-verbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600–700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In accordance with this classification, a face on a computer morphs into a sad or displeased countenance. The proposed method could be incorporated as a part of non-verbal communication tools to enable emotional expression. PMID:25206321

A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

PubMed

Costa, Álvaro; Hortal, Enrique; Iáñez, Eduardo; Azorín, José M

2014-01-01

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.

A Supplementary System for a Brain-Machine Interface Based on Jaw Artifacts for the Bidimensional Control of a Robotic Arm

PubMed Central

Costa, Álvaro; Hortal, Enrique; Iáñez, Eduardo; Azorín, José M.

2014-01-01

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment. PMID:25390372

A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.

PubMed

Kashihara, Koji

2014-01-01

Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients' emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based non-verbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600-700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In accordance with this classification, a face on a computer morphs into a sad or displeased countenance. The proposed method could be incorporated as a part of non-verbal communication tools to enable emotional expression.

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

PubMed Central

Mahmoudi, Babak; Sanchez, Justin C.

2011-01-01

Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain. PMID:21423797

Examining the Association Between School Vending Machines and Children's Body Mass Index by Socioeconomic Status.

PubMed

O'Hara, Jeffrey K; Haynes-Maslow, Lindsey

2015-01-01

To examine the association between vending machine availability in schools and body mass index (BMI) among subgroups of children based on gender, race/ethnicity, and socioeconomic status classifications. First-difference multivariate regressions were estimated using longitudinal fifth- and eighth-grade data from the Early Childhood Longitudinal Study. The specifications were disaggregated by gender, race/ethnicity, and family socioeconomic status classifications. Vending machine availability had a positive association (P < .10) with BMI among Hispanic male children and low-income Hispanic children. Living in an urban location (P < .05) and hours watching television (P < .05) were also positively associated with BMI for these subgroups. Supplemental Nutrition Assistance Program enrollment was negatively associated with BMI for low-income Hispanic students (P < .05). These findings were not statistically significant when using Bonferroni adjusted critical values. The results suggest that the school food environment could reinforce health disparities that exist for Hispanic male children and low-income Hispanic children. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Redundant information encoding in primary motor cortex during natural and prosthetic motor control.

PubMed

So, Kelvin; Ganguly, Karunesh; Jimenez, Jessica; Gastpar, Michael C; Carmena, Jose M

2012-06-01

Redundant encoding of information facilitates reliable distributed information processing. To explore this hypothesis in the motor system, we applied concepts from information theory to quantify the redundancy of movement-related information encoded in the macaque primary motor cortex (M1) during natural and neuroprosthetic control. Two macaque monkeys were trained to perform a delay center-out reaching task controlling a computer cursor under natural arm movement (manual control, 'MC'), and using a brain-machine interface (BMI) via volitional control of neural ensemble activity (brain control, 'BC'). During MC, we found neurons in contralateral M1 to contain higher and more redundant information about target direction than ipsilateral M1 neurons, consistent with the laterality of movement control. During BC, we found that the M1 neurons directly incorporated into the BMI ('direct' neurons) contained the highest and most redundant target information compared to neurons that were not incorporated into the BMI ('indirect' neurons). This effect was even more significant when comparing to M1 neurons of the opposite hemisphere. Interestingly, when we retrained the BMI to use ipsilateral M1 activity, we found that these neurons were more redundant and contained higher information than contralateral M1 neurons, even though ensembles from this hemisphere were previously less redundant during natural arm movement. These results indicate that ensembles most associated to movement contain highest redundancy and information encoding, which suggests a role for redundancy in proficient natural and prosthetic motor control.

Development and validation of methods for man-made machine interface evaluation. [for shuttles and shuttle payloads

NASA Technical Reports Server (NTRS)

Malone, T. B.; Micocci, A.

1975-01-01

The alternate methods of conducting a man-machine interface evaluation are classified as static and dynamic, and are evaluated. A dynamic evaluation tool is presented to provide for a determination of the effectiveness of the man-machine interface in terms of the sequence of operations (task and task sequences) and in terms of the physical characteristics of the interface. This dynamic checklist approach is recommended for shuttle and shuttle payload man-machine interface evaluations based on reduced preparation time, reduced data, and increased sensitivity of critical problems.

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.

UIVerify: A Web-Based Tool for Verification and Automatic Generation of User Interfaces

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Degani, Asaf; Heymann, Michael

2004-01-01

In this poster, we describe a web-based tool for verification and automatic generation of user interfaces. The verification component of the tool accepts as input a model of a machine and a model of its interface, and checks that the interface is adequate (correct). The generation component of the tool accepts a model of a given machine and the user's task, and then generates a correct and succinct interface. This write-up will demonstrate the usefulness of the tool by verifying the correctness of a user interface to a flight-control system. The poster will include two more examples of using the tool: verification of the interface to an espresso machine, and automatic generation of a succinct interface to a large hypothetical machine.

Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values.

PubMed

Franco-Villoria, Maria; Wright, Charlotte M; McColl, John H; Sherriff, Andrea; Pearce, Mark S

2016-01-07

To explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI). Cross-sectional observational study within population representative cohort study. Urban community, North East England Sample of 506 mothers of children aged 7-8 years, mean age 36.3 years. Participants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA). Height, weight, bioelectrical impedance (BIA). Lean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data. Estimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height(2). Of 112 women with BMI >30 kg/m(2), 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m(2)) only 5 (31%) also had low fat. Lean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Assessment of adult body composition using bioelectrical impedance: comparison of researcher calculated to machine outputted values

PubMed Central

Franco-Villoria, Maria; Wright, Charlotte M; McColl, John H; Sherriff, Andrea; Pearce, Mark S

2016-01-01

Objectives To explore the usefulness of Bioelectrical Impedance Analysis (BIA) for general use by identifying best-evidenced formulae to calculate lean and fat mass, comparing these to historical gold standard data and comparing these results with machine-generated output. In addition, we explored how to best to adjust lean and fat estimates for height and how these overlapped with body mass index (BMI). Design Cross-sectional observational study within population representative cohort study. Setting Urban community, North East England Participants Sample of 506 mothers of children aged 7–8 years, mean age 36.3 years. Methods Participants were measured at a home visit using a portable height measure and leg-to-leg BIA machine (Tanita TBF-300MA). Measures Height, weight, bioelectrical impedance (BIA). Outcome measures Lean and fat mass calculated using best-evidenced published formulae as well as machine-calculated lean and fat mass data. Results Estimates of lean mass were similar to historical results using gold standard methods. When compared with the machine-generated values, there were wide limits of agreement for fat mass and a large relative bias for lean that varied with size. Lean and fat residuals adjusted for height differed little from indices of lean (or fat)/height2. Of 112 women with BMI >30 kg/m2, 100 (91%) also had high fat, but of the 16 with low BMI (<19 kg/m2) only 5 (31%) also had low fat. Conclusions Lean and fat mass calculated from BIA using published formulae produces plausible values and demonstrate good concordance between high BMI and high fat, but these differ substantially from the machine-generated values. Bioelectrical impedance can supply a robust and useful field measure of body composition, so long as the machine-generated output is not used. PMID:26743700

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.

Man-systems integration and the man-machine interface

NASA Technical Reports Server (NTRS)

Hale, Joseph P.

1990-01-01

Viewgraphs on man-systems integration and the man-machine interface are presented. Man-systems integration applies the systems' approach to the integration of the user and the machine to form an effective, symbiotic Man-Machine System (MMS). A MMS is a combination of one or more human beings and one or more physical components that are integrated through the common purpose of achieving some objective. The human operator interacts with the system through the Man-Machine Interface (MMI).

The Body-Machine Interface: A new perspective on an old theme

PubMed Central

Casadio, Maura; Ranganathan, Rajiv; Mussa-Ivaldi, Ferdinando A.

2012-01-01

Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer-interfaces to bionic limbs, have had important consequences for people with movement disorders. In this article, we provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. We outline the steps involved in building such an interface and we highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation. PMID:23237465

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

PubMed

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

PubMed Central

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method. PMID:26884745

Future developments in brain-machine interface research.

PubMed

Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L

2011-01-01

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals.

PubMed

Gudiño-Mendoza, Berenice; Sanchez-Ante, Gildardo; Antelis, Javier M

2016-01-01

Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.

Future developments in brain-machine interface research

PubMed Central

Lebedev, Mikhail A; Tate, Andrew J; Hanson, Timothy L; Li, Zheng; O'Doherty, Joseph E; Winans, Jesse A; Ifft, Peter J; Zhuang, Katie Z; Fitzsimmons, Nathan A; Schwarz, David A; Fuller, Andrew M; An, Je Hi; Nicolelis, Miguel A L

2011-01-01

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition. PMID:21779720

Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

NASA Astrophysics Data System (ADS)

Kim, Tae Gyo

Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord researches with freely moving animals as well as to BMI researches. In addition this is extensible to various applications.

Transmission of wireless neural signals through a 0.18 µm CMOS low-power amplifier.

PubMed

Gazziro, M; Braga, C F R; Moreira, D A; Carvalho, A C P L F; Rodrigues, J F; Navarro, J S; Ardila, J C M; Mioni, D P; Pessatti, M; Fabbro, P; Freewin, C; Saddow, S E

2015-01-01

In the field of Brain Machine Interfaces (BMI) researchers still are not able to produce clinically viable solutions that meet the requirements of long-term operation without the use of wires or batteries. Another problem is neural compatibility with the electrode probes. One of the possible ways of approaching these problems is the use of semiconductor biocompatible materials (silicon carbide) combined with an integrated circuit designed to operate with low power consumption. This paper describes a low-power neural signal amplifier chip, named Cortex, fabricated using 0.18 μm CMOS process technology with all electronics integrated in an area of 0.40 mm(2). The chip has 4 channels, total power consumption of only 144 μW, and is impedance matched to silicon carbide biocompatible electrodes.

Minimizing data transfer with sustained performance in wireless brain-machine interfaces

NASA Astrophysics Data System (ADS)

Thor Thorbergsson, Palmi; Garwicz, Martin; Schouenborg, Jens; Johansson, Anders J.

2012-06-01

Brain-machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1-5%. We have then estimated the performance of alternative algorithms for spike detection and spike sorting in order to examine the generalizability of our results to other algorithms. Our findings indicate that the minimization of recording noise is the primary factor to consider in the design process. In most cases, there are breakpoints for sampling rates and resolution that provide guidelines for BMI designers in terms of minimum amount raw input data that guarantees sustained performance. Such guidelines are essential during system dimensioning. Based on these findings we conclude by presenting a quantitative task-allocation scheme that can be followed to achieve optimal utilization of available resources.

Minimizing data transfer with sustained performance in wireless brain-machine interfaces.

PubMed

Thorbergsson, Palmi Thor; Garwicz, Martin; Schouenborg, Jens; Johansson, Anders J

2012-06-01

Brain-machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1-5%. We have then estimated the performance of alternative algorithms for spike detection and spike sorting in order to examine the generalizability of our results to other algorithms. Our findings indicate that the minimization of recording noise is the primary factor to consider in the design process. In most cases, there are breakpoints for sampling rates and resolution that provide guidelines for BMI designers in terms of minimum amount raw input data that guarantees sustained performance. Such guidelines are essential during system dimensioning. Based on these findings we conclude by presenting a quantitative task-allocation scheme that can be followed to achieve optimal utilization of available resources.

A lower limb exoskeleton control system based on steady state visual evoked potentials.

PubMed

Kwak, No-Sang; Müller, Klaus-Robert; Lee, Seong-Whan

2015-10-01

We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.

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

A lower limb exoskeleton control system based on steady state visual evoked potentials

NASA Astrophysics Data System (ADS)

Kwak, No-Sang; Müller, Klaus-Robert; Lee, Seong-Whan

2015-10-01

Objective. We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). Approach. By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. Main results. Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. Significance. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.

Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants.

PubMed

Sanchez, Justin C; Mahmoudi, Babak; DiGiovanna, Jack; Principe, Jose C

2009-04-01

The success of brain-machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic 'tool' into its own cognitive space and use it as an extension of the user's body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user's internal goal representation of the world and the external physical environment enabling a much deeper human-tool symbiosis. A key factor in the transformation of 'simple tools' into 'intelligent tools' is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user's goals. Recent advancements in the neuroscience and engineering of neuroprosthetics are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user's ability to accomplish tasks that were not possible using conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants.

Future Cyborgs: Human-Machine Interface for Virtual Reality Applications

DTIC Science & Technology

2007-04-01

FUTURE CYBORGS : HUMAN-MACHINE INTERFACE FOR VIRTUAL REALITY APPLICATIONS Robert R. Powell, Major, USAF April 2007 Blue Horizons...SUBTITLE Future Cyborgs : Human-Machine Interface for Virtual Reality Applications 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...Nicholas Negroponte, Being Digital (New York: Alfred A Knopf, Inc, 1995), 123. 23 Ibid. 24 Andy Clark, Natural-Born Cyborgs (New York: Oxford

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

PubMed Central

Grimm, Florian; Walter, Armin; Spüler, Martin; Naros, Georgios; Rosenstiel, Wolfgang; Gharabaghi, Alireza

2016-01-01

Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients. PMID:27555805

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton.

PubMed

Grimm, Florian; Walter, Armin; Spüler, Martin; Naros, Georgios; Rosenstiel, Wolfgang; Gharabaghi, Alireza

2016-01-01

Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.

Intelligent man/machine interfaces on the space station

NASA Technical Reports Server (NTRS)

Daughtrey, Rodney S.

1987-01-01

Some important topics in the development of good, intelligent, usable man/machine interfaces for the Space Station are discussed. These computer interfaces should adhere strictly to three concepts or doctrines: generality, simplicity, and elegance. The motivation for natural language interfaces and their use and value on the Space Station, both now and in the future, are discussed.

Task-Oriented, Naturally Elicited Speech (TONE) Database for the Force Requirements Expert System, Hawaii (FRESH)

DTIC Science & Technology

1988-09-01

Group Subgroup Command and control; Computational linguistics; expert system voice recognition; man- machine interface; U.S. Government 19 Abstract...simulates the characteristics of FRESH on a smaller scale. This study assisted NOSC in developing a voice-recognition, man- machine interface that could...scale. This study assisted NOSC in developing a voice-recogni- tion, man- machine interface that could be used with TONE and upgraded at a later date

The habitual meal and snacking patterns of university students in two countries and their use of vending machines.

PubMed

Spanos, D; Hankey, C R

2010-02-01

Dietary patterns and food choices in western and northern European countries can differ from those in countries that surround the Mediterranean basin. However, irregular meal patterns and the consumption of high-energy snacks tend to become common in most countries and their association with the prevalence of obesity has been examined in many studies. The first aim of the present study was to describe the habitual meal and snack intakes, including the use of vending machines, for two groups of first-year university students in two countries of different cultural backgrounds. The second aim was to explore the relationships between body mass index (BMI) and snacking for these two groups. One hundred and sixty first-year undergraduate university students from two defined universities in Greece (n = 80) and Scotland (n = 80) volunteered to complete a food frequency questionnaire (FFQ). The FFQ comprised 16 questions assessing their meal and snacking habits. Self-assessed height and weight data were collected. The majority of the 160 students reported a BMI in the healthy range (<25 kg m(-2)). Overall, 26% of the students reported never consuming breakfast. More Scottish students (74%) used vending machines (P < 0.05). More Scottish students consumed chocolate bars and crisps than Greek students (41% and 34% versus 37.5% and 20%, respectively). Only the choice of chocolate bars from vending machines and the consumption of crisps and low fat yogurts were related to BMI (P < 0.05) for both groups. University students living in different countries report similar dietary patterns but differ in their snacking habits. No relationships were found between BMI and snacking. There is a need to carry out research to further our understanding of this relationship.

Learning Machine, Vietnamese Based Human-Computer Interface.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

The sixth session of IT@EDU98 consisted of seven papers on the topic of the learning machine--Vietnamese based human-computer interface, and was chaired by Phan Viet Hoang (Informatics College, Singapore). "Knowledge Based Approach for English Vietnamese Machine Translation" (Hoang Kiem, Dinh Dien) presents the knowledge base approach,…

Hypothesis to Explain the Size Effect Observed in APO-BMI Compression Tests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schembri, Philip Edward; Siranosian, Antranik Antonio; Kingston, Lance Allen

2015-01-07

In 2013 compression tests were performed on cylindrical specimens of carbon-microballoon-APOBMI syntactic foam machined to different lengths (0.25, 0.5, and 2.8 inches1) (Kingston, 2013). In 2014 similar tests were performed on glass-microballoon-APO-BMI of different lengths (~0.15”, ~0.32”, and ~0.57”). In all these tests it was observed that, when strains were calculated from the platen displacement (corrected for machine compliance), the apparent Young’s modulus of the material decreased with specimen size, as shown in Table 1. The reason for this size effect was speculated to be a layer of damage on or near the top and bottom machined surfaces of themore » specimens (Kingston, Schembri, & Siranosian, 2014). This report examines that hypothesis in further detail.« less

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.

Nanoscale wear and machining behavior of nanolayer interfaces.

PubMed

Nie, Xueyuan; Zhang, Peng; Weiner, Anita M; Cheng, Yang-Tse

2005-10-01

An atomic force microscope was used to subnanometer incise a nanomultilayer to consequently expose individual nanolayers and interfaces on which sliding and scanning nanowear/machining have been performed. The letter reports the first observation on the nanoscale where (i) atomic debris forms in a collective manner, most-likely by deformation and rupture of atomic bonds, and (ii) the nanolayer interfaces possess a much higher wear resistance (desired for nanomachines) or lower machinability (not desired for nanomachining) than the layers.

Howitzer Ammunition System Procurement (HASP).

DTIC Science & Technology

1991-07-01

machine tools , etc.) * Most critical part of base to reassemble. IPP * Industry to plan round-specific...beyond allowed tolerances. - Conducting tolerance studies and funding machining studies at sul’on "’actors. " Facility development was controlled by the...Manufacturing Balimoy Mfg. of Venice, Inc. Action Manufacturing Co. Lanson Industries Inc. Hercules Aerospace Company CIMA Machine & Tool Co., Inc. Talley Defense Systems Tracor Aerospace Inc. BMY E49030APPBMAC

Best face forward.

PubMed

Rayport, Jeffrey F; Jaworski, Bernard J

2004-12-01

Most companies serve customers through a broad array of interfaces, from retail sales clerks to Web sites to voice-response telephone systems. But while the typical company has an impressive interface collection, it doesn't have an interface system. That is, the whole set does not add up to the sum of its parts in its ability to provide service and build customer relationships. Too many people and too many machines operating with insufficient coordination (and often at cross-purposes) mean rising complexity, costs, and customer dissatisfaction. In a world where companies compete not on what they sell but on how they sell it, turning that liability into an asset is what separates winners from losers. In this adaptation of their forthcoming book by the same title, Jeffrey Rayport and Bernard Jaworski explain how companies must reengineer their customer interface systems for optimal efficiency and effectiveness. Part of that transformation, they observe, will involve a steady encroachment by machine interfaces into areas that have long been the sacred province of humans. Managers now have opportunities unprecedented in the history of business to use machines, not just people, to credibly manage their interactions with customers. Because people and machines each have their strengths and weaknesses, company executives must identify what people do best, what machines do best, and how to deploy them separately and together. Front-office reengineering subjects every current and potential service interface to an analysis of opportunities for substitution (using machines instead of people), complementarity (using a mix of machines and people), and displacement (using networks to shift physical locations of people and machines), with the twin objectives of compressing costs and driving top-line growth through increased customer value.

Multiple man-machine interfaces

NASA Technical Reports Server (NTRS)

Stanton, L.; Cook, C. W.

1981-01-01

The multiple man machine interfaces inherent in military pilot training, their social implications, and the issue of possible negative feedback were explored. Modern technology has produced machines which can see, hear, and touch with greater accuracy and precision than human beings. Consequently, the military pilot is more a systems manager, often doing battle against a target he never sees. It is concluded that unquantifiable human activity requires motivation that is not intrinsic in a machine.

Self-assembling fluidic machines

NASA Astrophysics Data System (ADS)

Grzybowski, Bartosz A.; Radkowski, Michal; Campbell, Christopher J.; Lee, Jessamine Ng; Whitesides, George M.

2004-03-01

This letter describes dynamic self-assembly of two-component rotors floating at the interface between liquid and air into simple, reconfigurable mechanical systems ("machines"). The rotors are powered by an external, rotating magnetic field, and their positions within the interface are controlled by: (i) repulsive hydrodynamic interactions between them and (ii) by localized magnetic fields produced by an array of small electromagnets located below the plane of the interface. The mechanical functions of the machines depend on the spatiotemporal sequence of activation of the electromagnets.

Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

NASA Astrophysics Data System (ADS)

Ardi, S.; Ardyansyah, D.

2018-02-01

In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

Gloved Human-Machine Interface

NASA Technical Reports Server (NTRS)

Adams, Richard (Inventor); Hannaford, Blake (Inventor); Olowin, Aaron (Inventor)

2015-01-01

Certain exemplary embodiments can provide a system, machine, device, manufacture, circuit, composition of matter, and/or user interface adapted for and/or resulting from, and/or a method and/or machine-readable medium comprising machine-implementable instructions for, activities that can comprise and/or relate to: tracking movement of a gloved hand of a human; interpreting a gloved finger movement of the human; and/or in response to interpreting the gloved finger movement, providing feedback to the human.

Diverse applications of advanced man-telerobot interfaces

NASA Technical Reports Server (NTRS)

Mcaffee, Douglas A.

1991-01-01

Advancements in man-machine interfaces and control technologies used in space telerobotics and teleoperators have potential application wherever human operators need to manipulate multi-dimensional spatial relationships. Bilateral six degree-of-freedom position and force cues exchanged between the user and a complex system can broaden and improve the effectiveness of several diverse man-machine interfaces.

Interface Metaphors for Interactive Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jasper, Robert J.; Blaha, Leslie M.

To promote more interactive and dynamic machine learn- ing, we revisit the notion of user-interface metaphors. User-interface metaphors provide intuitive constructs for supporting user needs through interface design elements. A user-interface metaphor provides a visual or action pattern that leverages a user’s knowledge of another domain. Metaphors suggest both the visual representations that should be used in a display as well as the interactions that should be afforded to the user. We argue that user-interface metaphors can also offer a method of extracting interaction-based user feedback for use in machine learning. Metaphors offer indirect, context-based information that can be usedmore » in addition to explicit user inputs, such as user-provided labels. Implicit information from user interactions with metaphors can augment explicit user input for active learning paradigms. Or it might be leveraged in systems where explicit user inputs are more challenging to obtain. Each interaction with the metaphor provides an opportunity to gather data and learn. We argue this approach is especially important in streaming applications, where we desire machine learning systems that can adapt to dynamic, changing data.« less

Investigation of Implantable Multi-Channel Electrode Array in Rat Cerebral Cortex Used for Recording

NASA Astrophysics Data System (ADS)

Taniguchi, Noriyuki; Fukayama, Osamu; Suzuki, Takafumi; Mabuchi, Kunihiko

There have recently been many studies concerning the control of robot movements using neural signals recorded from the brain (usually called the Brain-Machine interface (BMI)). We fabricated implantable multi-electrode arrays to obtain neural signals from the rat cerebral cortex. As any multi-electrode array should have electrode alignment that minimizes invasion, it is necessary to customize the recording site. We designed three types of 22-channel multi-electrode arrays, i.e., 1) wide, 2) three-layered, and 3) separate. The first extensively covers the cerebral cortex. The second has a length of 2 mm, which can cover the area of the primary motor cortex. The third array has a separate structure, which corresponds to the position of the forelimb and hindlimb areas of the primary motor cortex. These arrays were implanted into the cerebral cortex of a rat. We estimated the walking speed from neural signals using our fabricated three-layered array to investigate its feasibility for BMI research. The neural signal of the rat and its walking speed were simultaneously recorded. The results revealed that evaluation using either the anterior electrode group or posterior group provided accurate estimates. However, two electrode groups around the center yielded poor estimates although it was possible to record neural signals.

Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke.

PubMed

Nishimoto, Atsuko; Kawakami, Michiyuki; Fujiwara, Toshiyuki; Hiramoto, Miho; Honaga, Kaoru; Abe, Kaoru; Mizuno, Katsuhiro; Ushiba, Junichi; Liu, Meigen

2018-01-10

Brain-machine interface training was developed for upper-extremity rehabilitation for patients with severe hemiparesis. Its clinical application, however, has been limited because of its lack of feasibility in real-world rehabilitation settings. We developed a new compact task-specific brain-machine interface system that enables task-specific training, including reach-and-grasp tasks, and studied its clinical feasibility and effectiveness for upper-extremity motor paralysis in patients with stroke. Prospective beforeâ€"after study. Twenty-six patients with severe chronic hemiparetic stroke. Participants were trained with the brain-machine interface system to pick up and release pegs during 40-min sessions and 40 min of standard occupational therapy per day for 10 days. Fugl-Meyer upper-extremity motor (FMA) and Motor Activity Log-14 amount of use (MAL-AOU) scores were assessed before and after the intervention. To test its feasibility, 4 occupational therapists who operated the system for the first time assessed it with the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) 2.0. FMA and MAL-AOU scores improved significantly after brain-machine interface training, with the effect sizes being medium and large, respectively (p<0.01, d=0.55; p<0.01, d=0.88). QUEST effectiveness and safety scores showed feasibility and satisfaction in the clinical setting. Our newly developed compact brain-machine interface system is feasible for use in real-world clinical settings.

Neuroplasticity of the Sensorimotor Cortex during Learning

PubMed Central

Francis, Joseph Thachil; Song, Weiguo

2011-01-01

We will discuss some of the current issues in understanding plasticity in the sensorimotor (SM) cortices on the behavioral, neurophysiological, and synaptic levels. We will focus our paper on reaching and grasping movements in the rat. In addition, we will discuss our preliminary work utilizing inhibition of protein kinase Mζ (PKMζ), which has recently been shown necessary and sufficient for the maintenance of long-term potentiation (LTP) (Ling et al., 2002). With this new knowledge and inhibitors to this system, as well as the ability to overexpress this system, we can start to directly modulate LTP and determine its influence on behavior as well as network level processing dependent at least in part due to this form of LTP. We will also briefly introduce the use of brain machine interface (BMI) paradigms to ask questions about sensorimotor plasticity and discuss current analysis techniques that may help in our understanding of neuroplasticity. PMID:21949908

Estimation of locomotion speed and directions changes to control a vehicle using neural signals from the motor cortex of rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2006-01-01

We have developed a brain-machine interface (BMI) in the form of a small vehicle, which we call the RatCar. In this system, we implanted wire electrodes in the motor cortices of rat's brain to continuously record neural signals. We applied a linear model to estimate the locomotion state (e.g., speed and directions) of a rat using a weighted summation model for the neural firing rates. With this information, we then determined the approximate movement of a rat. Although the estimation is still imprecise, results suggest that our model is able to control the system to some degree. In this paper, we give an overview of our system and describe the methods used, which include continuous neural recording, spike detection and a discrimination algorithm, and a locomotion estimation model minimizes the square error of the locomotion speed and changes in direction.

The evolution of endovascular electroencephalography: historical perspective and future applications.

PubMed

Sefcik, Roberta K; Opie, Nicholas L; John, Sam E; Kellner, Christopher P; Mocco, J; Oxley, Thomas J

2016-05-01

Current standard practice requires an invasive approach to the recording of electroencephalography (EEG) for epilepsy surgery, deep brain stimulation (DBS), and brain-machine interfaces (BMIs). The development of endovascular techniques offers a minimally invasive route to recording EEG from deep brain structures. This historical perspective aims to describe the technical progress in endovascular EEG by reviewing the first endovascular recordings made using a wire electrode, which was followed by the development of nanowire and catheter recordings and, finally, the most recent progress in stent-electrode recordings. The technical progress in device technology over time and the development of the ability to record chronic intravenous EEG from electrode arrays is described. Future applications for the use of endovascular EEG in the preoperative and operative management of epilepsy surgery are then discussed, followed by the possibility of the technique's future application in minimally invasive operative approaches to DBS and BMI.

Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate

NASA Astrophysics Data System (ADS)

Degenhart, Alan D.; Eles, James; Dum, Richard; Mischel, Jessica L.; Smalianchuk, Ivan; Endler, Bridget; Ashmore, Robin C.; Tyler-Kabara, Elizabeth C.; Hatsopoulos, Nicholas G.; Wang, Wei; Batista, Aaron P.; Cui, X. Tracy

2016-08-01

Objective. Electrocorticography (ECoG), used as a neural recording modality for brain-machine interfaces (BMIs), potentially allows for field potentials to be recorded from the surface of the cerebral cortex for long durations without suffering the host-tissue reaction to the extent that it is common with intracortical microelectrodes. Though the stability of signals obtained from chronically implanted ECoG electrodes has begun receiving attention, to date little work has characterized the effects of long-term implantation of ECoG electrodes on underlying cortical tissue. Approach. We implanted and recorded from a high-density ECoG electrode grid subdurally over cortical motor areas of a Rhesus macaque for 666 d. Main results. Histological analysis revealed minimal damage to the cortex underneath the implant, though the grid itself was encapsulated in collagenous tissue. We observed macrophages and foreign body giant cells at the tissue-array interface, indicative of a stereotypical foreign body response. Despite this encapsulation, cortical modulation during reaching movements was observed more than 18 months post-implantation. Significance. These results suggest that ECoG may provide a means by which stable chronic cortical recordings can be obtained with comparatively little tissue damage, facilitating the development of clinically viable BMI systems.

Brain-controlled muscle stimulation for the restoration of motor function

PubMed Central

Ethier, Christian; Miller, Lee E

2014-01-01

Loss of the ability to move, as a consequence of spinal cord injury or neuromuscular disorder, has devastating consequences for the paralyzed individual, and great economic consequences for society. Functional Electrical Stimulation (FES) offers one means to restore some mobility to these individuals, improving not only their autonomy, but potentially their general health and well-being as well. FES uses electrical stimulation to cause the paralyzed muscles to contract. Existing clinical systems require the stimulation to be preprogrammed, with the patient typically using residual voluntary movement of another body part to trigger and control the patterned stimulation. The rapid development of neural interfacing in the past decade offers the promise of dramatically improved control for these patients, potentially allowing continuous control of FES through signals recorded from motor cortex, as the patient attempts to control the paralyzed body part. While application of these ‘Brain Machine Interfaces’ (BMIs) has undergone dramatic development for control of computer cursors and even robotic limbs, their use as an interface for FES has been much more limited. In this review, we consider both FES and BMI technologies and discuss the prospect for combining the two to provide important new options for paralyzed individuals. PMID:25447224

Flexible software architecture for user-interface and machine control in laboratory automation.

PubMed

Arutunian, E B; Meldrum, D R; Friedman, N A; Moody, S E

1998-10-01

We describe a modular, layered software architecture for automated laboratory instruments. The design consists of a sophisticated user interface, a machine controller and multiple individual hardware subsystems, each interacting through a client-server architecture built entirely on top of open Internet standards. In our implementation, the user-interface components are built as Java applets that are downloaded from a server integrated into the machine controller. The user-interface client can thereby provide laboratory personnel with a familiar environment for experiment design through a standard World Wide Web browser. Data management and security are seamlessly integrated at the machine-controller layer using QNX, a real-time operating system. This layer also controls hardware subsystems through a second client-server interface. This architecture has proven flexible and relatively easy to implement and allows users to operate laboratory automation instruments remotely through an Internet connection. The software architecture was implemented and demonstrated on the Acapella, an automated fluid-sample-processing system that is under development at the University of Washington.

Multi-electrode stimulation in somatosensory cortex increases probability of detection

NASA Astrophysics Data System (ADS)

Zaaimi, Boubker; Ruiz-Torres, Ricardo; Solla, Sara A.; Miller, Lee E.

2013-10-01

Objective. Brain machine interfaces (BMIs) that decode control signals from motor cortex have developed tremendously in the past decade, but virtually all rely exclusively on vision to provide feedback. There is now increasing interest in developing an afferent interface to replace natural somatosensation, much as the cochlear implant has done for the sense of hearing. Preliminary experiments toward a somatosensory neuroprosthesis have mostly addressed the sense of touch, but proprioception, the sense of limb position and movement, is also critical for the control of movement. However, proprioceptive areas of cortex lack the precise somatotopy of tactile areas. We showed previously that there is only a weak tendency for neighboring neurons in area 2 to signal similar directions of hand movement. Consequently, stimulation with the relatively large currents used in many studies is likely to activate a rather heterogeneous set of neurons. Approach. Here, we have compared the effect of single-electrode stimulation at subthreshold levels to the effect of stimulating as many as seven electrodes in combination. Main results. We found a mean enhancement in the sensitivity to the stimulus (d‧) of 0.17 for pairs compared to individual electrodes (an increase of roughly 30%), and an increase of 2.5 for groups of seven electrodes (260%). Significance. We propose that a proprioceptive interface made up of several hundred electrodes may yield safer, more effective sensation than a BMI using fewer electrodes and larger currents.

Low Latency Messages on Distributed Memory Multiprocessors

DOE PAGES

Rosing, Matt; Saltz, Joel

1995-01-01

This article describes many of the issues in developing an efficient interface for communication on distributed memory machines. Although the hardware component of message latency is less than 1 ws on many distributed memory machines, the software latency associated with sending and receiving typed messages is on the order of 50 μs. The reason for this imbalance is that the software interface does not match the hardware. By changing the interface to match the hardware more closely, applications with fine grained communication can be put on these machines. This article describes several tests performed and many of the issues involvedmore » in supporting low latency messages on distributed memory machines.« less

Snack food as a modulator of human resting-state functional connectivity.

PubMed

Mendez-Torrijos, Andrea; Kreitz, Silke; Ivan, Claudiu; Konerth, Laura; Rösch, Julie; Pischetsrieder, Monika; Moll, Gunther; Kratz, Oliver; Dörfler, Arnd; Horndasch, Stefanie; Hess, Andreas

2018-04-04

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation. Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques. Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus. The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.

A Function-Behavior-State Approach to Designing Human Machine Interface for Nuclear Power Plant Operators

NASA Astrophysics Data System (ADS)

Lin, Y.; Zhang, W. J.

2005-02-01

This paper presents an approach to human-machine interface design for control room operators of nuclear power plants. The first step in designing an interface for a particular application is to determine information content that needs to be displayed. The design methodology for this step is called the interface design framework (called framework ). Several frameworks have been proposed for applications at varying levels, including process plants. However, none is based on the design and manufacture of a plant system for which the interface is designed. This paper presents an interface design framework which originates from design theory and methodology for general technical systems. Specifically, the framework is based on a set of core concepts of a function-behavior-state model originally proposed by the artificial intelligence research community and widely applied in the design research community. Benefits of this new framework include the provision of a model-based fault diagnosis facility, and the seamless integration of the design (manufacture, maintenance) of plants and the design of human-machine interfaces. The missing linkage between design and operation of a plant was one of the causes of the Three Mile Island nuclear reactor incident. A simulated plant system is presented to explain how to apply this framework in designing an interface. The resulting human-machine interface is discussed; specifically, several fault diagnosis examples are elaborated to demonstrate how this interface could support operators' fault diagnosis in an unanticipated situation.

Low latency messages on distributed memory multiprocessors

NASA Technical Reports Server (NTRS)

Rosing, Matthew; Saltz, Joel

1993-01-01

Many of the issues in developing an efficient interface for communication on distributed memory machines are described and a portable interface is proposed. Although the hardware component of message latency is less than one microsecond on many distributed memory machines, the software latency associated with sending and receiving typed messages is on the order of 50 microseconds. The reason for this imbalance is that the software interface does not match the hardware. By changing the interface to match the hardware more closely, applications with fine grained communication can be put on these machines. Based on several tests that were run on the iPSC/860, an interface that will better match current distributed memory machines is proposed. The model used in the proposed interface consists of a computation processor and a communication processor on each node. Communication between these processors and other nodes in the system is done through a buffered network. Information that is transmitted is either data or procedures to be executed on the remote processor. The dual processor system is better suited for efficiently handling asynchronous communications compared to a single processor system. The ability to send data or procedure is very flexible for minimizing message latency, based on the type of communication being performed. The test performed and the proposed interface are described.

Automated visual imaging interface for the plant floor

NASA Astrophysics Data System (ADS)

Wutke, John R.

1991-03-01

The paper will provide an overview of the challenges facing a user of automated visual imaging (" AVI" ) machines and the philosophies that should be employed in designing them. As manufacturing tools and equipment become more sophisticated it is increasingly difficult to maintain an efficient interaction between the operator and machine. The typical user of an AVI machine in a production environment is technically unsophisticated. Also operator and machine ergonomics are often a neglected or poorly addressed part of an efficient manufacturing process. This paper presents a number of man-machine interface design techniques and philosophies that effectively solve these problems.

Associations between state-level soda taxes and adolescent body mass index.

PubMed

Powell, Lisa M; Chriqui, Jamie; Chaloupka, Frank J

2009-09-01

Soft drink consumption has been linked with higher energy intake, obesity, and poorer health. Fiscal pricing policies such as soda taxes may lower soda consumption and, in turn, reduce weight among U.S. adolescents. This study used multivariate linear regression analyses to examine the associations between state-level grocery store and vending machine soda taxes and adolescent body mass index (BMI). We used repeated cross-sections of individual-level data on adolescents drawn from the Monitoring the Future surveys combined with state-level tax data and local area contextual measures for the years 1997 through 2006. The results showed no statistically significant associations between state-level soda taxes and adolescent BMI. Only a weak economic and statistically significant effect was found between vending machine soda tax rates and BMI among teens at risk for overweight. Current state-level tax rates are not found to be significantly associated with adolescent weight outcomes. It is likely that taxes would need to be raised substantially to detect significant associations between taxes and adolescent weight.

MARTI: man-machine animation real-time interface

NASA Astrophysics Data System (ADS)

Jones, Christian M.; Dlay, Satnam S.

1997-05-01

The research introduces MARTI (man-machine animation real-time interface) for the realization of natural human-machine interfacing. The system uses simple vocal sound-tracks of human speakers to provide lip synchronization of computer graphical facial models. We present novel research in a number of engineering disciplines, which include speech recognition, facial modeling, and computer animation. This interdisciplinary research utilizes the latest, hybrid connectionist/hidden Markov model, speech recognition system to provide very accurate phone recognition and timing for speaker independent continuous speech, and expands on knowledge from the animation industry in the development of accurate facial models and automated animation. The research has many real-world applications which include the provision of a highly accurate and 'natural' man-machine interface to assist user interactions with computer systems and communication with one other using human idiosyncrasies; a complete special effects and animation toolbox providing automatic lip synchronization without the normal constraints of head-sets, joysticks, and skilled animators; compression of video data to well below standard telecommunication channel bandwidth for video communications and multi-media systems; assisting speech training and aids for the handicapped; and facilitating player interaction for 'video gaming' and 'virtual worlds.' MARTI has introduced a new level of realism to man-machine interfacing and special effect animation which has been previously unseen.

Computation of the Distribution of the Fiber-Matrix Interface Cracks in the Edge Trimming of CFRP

NASA Astrophysics Data System (ADS)

Wang, Fu-ji; Zhang, Bo-yu; Ma, Jian-wei; Bi, Guang-jian; Hu, Hai-bo

2018-04-01

Edge trimming is commonly used to bring the CFRP components to right dimension and shape in aerospace industries. However, various forms of undesirable machining damage occur frequently which will significantly decrease the material performance of CFRP. The damage is difficult to predict and control due to the complicated changing laws, causing unsatisfactory machining quality of CFRP components. Since the most of damage has the same essence: the fiber-matrix interface cracks, this study aims to calculate the distribution of them in edge trimming of CFRP, thereby to obtain the effects of the machining parameters, which could be helpful to guide the optimal selection of the machining parameters in engineering. Through the orthogonal cutting experiments, the quantitative relation between the fiber-matrix interface crack depth and the fiber cutting angle, cutting depth as well as cutting speed is established. According to the analysis on material removal process on any location of the workpiece in edge trimming, the instantaneous cutting parameters are calculated, and the formation process of the fiber-matrix interface crack is revealed. Finally, the computational method for the fiber-matrix interface cracks in edge trimming of CFRP is proposed. Upon the computational results, it is found that the fiber orientations of CFRP workpieces is the most significant factor on the fiber-matrix interface cracks, which can not only change the depth of them from micrometers to millimeters, but control the distribution image of them. Other machining parameters, only influence the fiber-matrix interface cracks depth but have little effect on the distribution image.

The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices.

PubMed

Marathe, A R; Taylor, D M

2015-08-01

Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a diverse range of decoding options offline.

The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices

NASA Astrophysics Data System (ADS)

Marathe, A. R.; Taylor, D. M.

2015-08-01

Objective. Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain. Approach. To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains. Main results. Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present. Significance. This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a diverse range of decoding options offline.

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

Research interface on a programmable ultrasound scanner.

PubMed

Shamdasani, Vijay; Bae, Unmin; Sikdar, Siddhartha; Yoo, Yang Mo; Karadayi, Kerem; Managuli, Ravi; Kim, Yongmin

2008-07-01

Commercial ultrasound machines in the past did not provide the ultrasound researchers access to raw ultrasound data. Lack of this ability has impeded evaluation and clinical testing of novel ultrasound algorithms and applications. Recently, we developed a flexible ultrasound back-end where all the processing for the conventional ultrasound modes, such as B, M, color flow and spectral Doppler, was performed in software. The back-end has been incorporated into a commercial ultrasound machine, the Hitachi HiVision 5500. The goal of this work is to develop an ultrasound research interface on the back-end for acquiring raw ultrasound data from the machine. The research interface has been designed as a software module on the ultrasound back-end. To increase the amount of raw ultrasound data that can be spooled in the limited memory available on the back-end, we have developed a method that can losslessly compress the ultrasound data in real time. The raw ultrasound data could be obtained in any conventional ultrasound mode, including duplex and triplex modes. Furthermore, use of the research interface does not decrease the frame rate or otherwise affect the clinical usability of the machine. The lossless compression of the ultrasound data in real time can increase the amount of data spooled by approximately 2.3 times, thus allowing more than 6s of raw ultrasound data to be acquired in all the modes. The interface has been used not only for early testing of new ideas with in vitro data from phantoms, but also for acquiring in vivo data for fine-tuning ultrasound applications and conducting clinical studies. We present several examples of how newer ultrasound applications, such as elastography, vibration imaging and 3D imaging, have benefited from this research interface. Since the research interface is entirely implemented in software, it can be deployed on existing HiVision 5500 ultrasound machines and may be easily upgraded in the future. The developed research interface can aid researchers in the rapid testing and clinical evaluation of new ultrasound algorithms and applications. Additionally, we believe that our approach would be applicable to designing research interfaces on other ultrasound machines.

Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations

NASA Astrophysics Data System (ADS)

Wodlinger, B.; Downey, J. E.; Tyler-Kabara, E. C.; Schwartz, A. B.; Boninger, M. L.; Collinger, J. L.

2015-02-01

Objective. In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject’s left motor cortex. Approach. Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously. Main results. Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping. Significance. Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.

Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading

DOE PAGES

Cousineau, Justine Emily; Bennion, Kevin S.; Chieduko, Victor; ...

2018-05-08

Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. Furthermore, the results were compared to currently available modelsmore » for contact resistance, and one model was adapted for prediction of TCR in future motor designs.« less

Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cousineau, Justine Emily; Bennion, Kevin S.; Chieduko, Victor

Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. Furthermore, the results were compared to currently available modelsmore » for contact resistance, and one model was adapted for prediction of TCR in future motor designs.« less

The desktop interface in intelligent tutoring systems

NASA Technical Reports Server (NTRS)

Baudendistel, Stephen; Hua, Grace

1987-01-01

The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine.

Graphical user interfaces for symbol-oriented database visualization and interaction

NASA Astrophysics Data System (ADS)

Brinkschulte, Uwe; Siormanolakis, Marios; Vogelsang, Holger

1997-04-01

In this approach, two basic services designed for the engineering of computer based systems are combined: a symbol-oriented man-machine-service and a high speed database-service. The man-machine service is used to build graphical user interfaces (GUIs) for the database service; these interfaces are stored using the database service. The idea is to create a GUI-builder and a GUI-manager for the database service based upon the man-machine service using the concept of symbols. With user-definable and predefined symbols, database contents can be visualized and manipulated in a very flexible and intuitive way. Using the GUI-builder and GUI-manager, a user can build and operate its own graphical user interface for a given database according to its needs without writing a single line of code.

Human machine interface display design document.

DOT National Transportation Integrated Search

2008-01-01

The purpose of this document is to describe the design for the human machine interface : (HMI) display for the Next Generation 9-1-1 (NG9-1-1) System (or system of systems) : based on the initial Tier 1 requirements identified for the NG9-1-1 S...

Work hours, weight status, and weight-related behaviors: a study of metro transit workers.

PubMed

Escoto, Kamisha H; French, Simone A; Harnack, Lisa J; Toomey, Traci L; Hannan, Peter J; Mitchell, Nathan R

2010-12-20

Associations between hours worked per week and Body Mass Index (BMI), food intake, physical activity, and perceptions of eating healthy at work were examined in a sample of transit workers. Survey data were collected from 1086 transit workers. Participants reported hours worked per week, food choices, leisure-time physical activity and perceptions of the work environment with regard to healthy eating. Height and weight were measured for each participant. Multivariate linear and logistic regressions were conducted to examine associations between work hours and behavioral variables. Associations were examined in the full sample and stratified by gender. Transit workers working in the highest work hour categories had higher BMI and poorer dietary habits, with results differing by gender. Working 50 or more hours per week was associated with higher BMI among men but not women. Additionally, working 50 or more hours per week was significantly associated with higher frequency of accessing cold beverage, cold food, and snack vending machines among men. Working 40 or more hours per week was associated with higher frequency of accessing cold food vending machines among women. Reported frequency of fruit and vegetable intake was highest among women working 50 or more hours per week. Intake of sweets, sugar sweetened beverages, and fast food did not vary with work hours in men or women. Physical activity and perception of ease of eating healthy at work were not associated with work hours in men or women. Long work hours were associated with more frequent use of garage vending machines and higher BMI in transit workers, with associations found primarily among men. Long work hours may increase dependence upon food availability at the worksite, which highlights the importance of availability of healthy food choices.

Work hours, weight status, and weight-related behaviors: a study of metro transit workers

PubMed Central

2010-01-01

Background Associations between hours worked per week and Body Mass Index (BMI), food intake, physical activity, and perceptions of eating healthy at work were examined in a sample of transit workers. Methods Survey data were collected from 1086 transit workers. Participants reported hours worked per week, food choices, leisure-time physical activity and perceptions of the work environment with regard to healthy eating. Height and weight were measured for each participant. Multivariate linear and logistic regressions were conducted to examine associations between work hours and behavioral variables. Associations were examined in the full sample and stratified by gender. Results Transit workers working in the highest work hour categories had higher BMI and poorer dietary habits, with results differing by gender. Working 50 or more hours per week was associated with higher BMI among men but not women. Additionally, working 50 or more hours per week was significantly associated with higher frequency of accessing cold beverage, cold food, and snack vending machines among men. Working 40 or more hours per week was associated with higher frequency of accessing cold food vending machines among women. Reported frequency of fruit and vegetable intake was highest among women working 50 or more hours per week. Intake of sweets, sugar sweetened beverages, and fast food did not vary with work hours in men or women. Physical activity and perception of ease of eating healthy at work were not associated with work hours in men or women. Conclusions Long work hours were associated with more frequent use of garage vending machines and higher BMI in transit workers, with associations found primarily among men. Long work hours may increase dependence upon food availability at the worksite, which highlights the importance of availability of healthy food choices. PMID:21172014

Insect-machine interface based neurocybernetics.

PubMed

Bozkurt, Alper; Gilmour, Robert F; Sinha, Ayesa; Stern, David; Lal, Amit

2009-06-01

We present details of a novel bioelectric interface formed by placing microfabricated probes into insect during metamorphic growth cycles. The inserted microprobes emerge with the insect where the development of tissue around the electronics during the pupal development allows mechanically stable and electrically reliable structures coupled to the insect. Remarkably, the insects do not react adversely or otherwise to the inserted electronics in the pupae stage, as is true when the electrodes are inserted in adult stages. We report on the electrical and mechanical characteristics of this novel bioelectronic interface, which we believe would be adopted by many investigators trying to investigate biological behavior in insects with negligible or minimal traumatic effect encountered when probes are inserted in adult stages. This novel insect-machine interface also allows for hybrid insect-machine platforms for further studies. As an application, we demonstrate our first results toward navigation of flight in moths. When instrumented with equipment to gather information for environmental sensing, such insects potentially can assist man to monitor the ecosystems that we share with them for sustainability. The simplicity of the optimized surgical procedure we invented allows for batch insertions to the insect for automatic and mass production of such hybrid insect-machine platforms. Therefore, our bioelectronic interface and hybrid insect-machine platform enables multidisciplinary scientific and engineering studies not only to investigate the details of insect behavioral physiology but also to control it.

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

PubMed

Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

2015-08-01

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.

PubMed

Irwin, Zachary T; Thompson, David E; Schroeder, Karen E; Tat, Derek M; Hassani, Ali; Bullard, Autumn J; Woo, Shoshana L; Urbanchek, Melanie G; Sachs, Adam J; Cederna, Paul S; Stacey, William C; Patil, Parag G; Chestek, Cynthia A

2016-05-01

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Hybrid EEG-EOG brain-computer interface system for practical machine control.

PubMed

Punsawad, Yunyong; Wongsawat, Yodchanan; Parnichkun, Manukid

2010-01-01

Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

Design Guidelines and Criteria for User/Operator Transactions with Battlefield Automated Systems. Volume 5. Background Literature

DTIC Science & Technology

1981-02-01

the machine . ARI’s efforts in this area focus on human perfor- mance problems related to interactions with command and control centers, and on issues...improvement of the user- machine interface. Lacking consistent design principles, current practice results in a fragmented and unsystematic approach to system...complexity in the user- machine interface of BAS, ARI supported this effort for develop- me:nt of an online language for Army tactical intelligence

Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals.

PubMed

Fukuma, Ryohei; Yanagisawa, Takufumi; Yorifuji, Shiro; Kato, Ryu; Yokoi, Hiroshi; Hirata, Masayuki; Saitoh, Youichi; Kishima, Haruhiko; Kamitani, Yukiyasu; Yoshimine, Toshiki

2015-01-01

A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis. Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.

Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals

PubMed Central

Fukuma, Ryohei; Yanagisawa, Takufumi; Yorifuji, Shiro; Kato, Ryu; Yokoi, Hiroshi; Hirata, Masayuki; Saitoh, Youichi; Kishima, Haruhiko; Kamitani, Yukiyasu; Yoshimine, Toshiki

2015-01-01

Objective A neuroprosthesis using a brain–machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects’ ability to control a neuroprosthesis. Method Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. Results The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student’s t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. Conclusions Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI. PMID:26134845

Development of the FITS tools package for multiple software environments

NASA Technical Reports Server (NTRS)

Pence, W. D.; Blackburn, J. K.

1992-01-01

The HEASARC is developing a package of general purpose software for analyzing data files in FITS format. This paper describes the design philosophy which makes the software both machine-independent (it runs on VAXs, Suns, and DEC-stations) and software environment-independent. Currently the software can be compiled and linked to produce IRAF tasks, or alternatively, the same source code can be used to generate stand-alone tasks using one of two implementations of a user-parameter interface library. The machine independence of the software is achieved by writing the source code in ANSI standard Fortran or C, using the machine-independent FITSIO subroutine interface for all data file I/O, and using a standard user-parameter subroutine interface for all user I/O. The latter interface is based on the Fortran IRAF Parameter File interface developed at STScI. The IRAF tasks are built by linking to the IRAF implementation of this parameter interface library. Two other implementations of this parameter interface library, which have no IRAF dependencies, are now available which can be used to generate stand-alone executable tasks. These stand-alone tasks can simply be executed from the machine operating system prompt either by supplying all the task parameters on the command line or by entering the task name after which the user will be prompted for any required parameters. A first release of this FTOOLS package is now publicly available. The currently available tasks are described, along with instructions on how to obtain a copy of the software.

All printed touchless human-machine interface based on only five functional materials

NASA Astrophysics Data System (ADS)

Scheipl, G.; Zirkl, M.; Sawatdee, A.; Helbig, U.; Krause, M.; Kraker, E.; Andersson Ersman, P.; Nilsson, D.; Platt, D.; Bodö, P.; Bauer, S.; Domann, G.; Mogessie, A.; Hartmann, Paul; Stadlober, B.

2012-02-01

We demonstrate the printing of a complex smart integrated system using only five functional inks: the fluoropolymer P(VDF:TrFE) (Poly(vinylidene fluoride trifluoroethylene) sensor ink, the conductive polymer PEDOT:PSS (poly(3,4 ethylenedioxythiophene):poly(styrene sulfonic acid) ink, a conductive carbon paste, a polymeric electrolyte and SU8 for separation. The result is a touchless human-machine interface, including piezo- and pyroelectric sensor pixels (sensitive to pressure changes and impinging infrared light), transistors for impedance matching and signal conditioning, and an electrochromic display. Applications may not only emerge in human-machine interfaces, but also in transient temperature or pressure sensing used in safety technology, in artificial skins and in disposable sensor labels.

Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles.

PubMed

Eom, Hwisoo; Lee, Sang Hun

2015-06-12

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for ACC systems that can reduce mode confusion. To meet this requirement, this paper presents a new human-automation interaction design methodology in which the compatibility of the machine and interface models is determined using the proposed criteria, and if the models are incompatible, one or both of the models is/are modified to make them compatible. To investigate the effectiveness of our methodology, we designed two new interfaces by separately modifying the machine model and the interface model and then performed driver-in-the-loop experiments. The results showed that modifying the machine model provides a more compact, acceptable, effective, and safe interface than modifying the interface model.

Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles

PubMed Central

Eom, Hwisoo; Lee, Sang Hun

2015-01-01

A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane keeping assistance systems. ACC systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for ACC systems that can reduce mode confusion. To meet this requirement, this paper presents a new human-automation interaction design methodology in which the compatibility of the machine and interface models is determined using the proposed criteria, and if the models are incompatible, one or both of the models is/are modified to make them compatible. To investigate the effectiveness of our methodology, we designed two new interfaces by separately modifying the machine model and the interface model and then performed driver-in-the-loop experiments. The results showed that modifying the machine model provides a more compact, acceptable, effective, and safe interface than modifying the interface model. PMID:26076406

Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces.

PubMed

Lopes, Ana C; Nunes, Urbano; Vaz, Luis; Vaz, Luís

2010-01-01

This paper presents a shared-control approach for Assistive Mobile Robots (AMR), which depends on the user's ability to navigate a semi-autonomous powered wheelchair, using a sparse and discrete human-machine interface (HMI). This system is primarily intended to help users with severe motor disabilities that prevent them to use standard human-machine interfaces. Scanning interfaces and Brain Computer Interfaces (BCI), characterized to provide a small set of commands issued sparsely, are possible HMIs. This shared-control approach is intended to be applied in an Assisted Navigation Training Framework (ANTF) that is used to train users' ability in steering a powered wheelchair in an appropriate manner, given the restrictions imposed by their limited motor capabilities. A shared-controller based on user characterization, is proposed. This controller is able to share the information provided by the local motion planning level with the commands issued sparsely by the user. Simulation results of the proposed shared-control method, are presented.

A Smart Modeling Framework for Integrating BMI-enabled Models as Web Services

NASA Astrophysics Data System (ADS)

Jiang, P.; Elag, M.; Kumar, P.; Peckham, S. D.; Liu, R.; Marini, L.; Hsu, L.

2015-12-01

Serviced-oriented computing provides an opportunity to couple web service models using semantic web technology. Through this approach, models that are exposed as web services can be conserved in their own local environment, thus making it easy for modelers to maintain and update the models. In integrated modeling, the serviced-oriented loose-coupling approach requires (1) a set of models as web services, (2) the model metadata describing the external features of a model (e.g., variable name, unit, computational grid, etc.) and (3) a model integration framework. We present the architecture of coupling web service models that are self-describing by utilizing a smart modeling framework. We expose models that are encapsulated with CSDMS (Community Surface Dynamics Modeling System) Basic Model Interfaces (BMI) as web services. The BMI-enabled models are self-describing by uncovering models' metadata through BMI functions. After a BMI-enabled model is serviced, a client can initialize, execute and retrieve the meta-information of the model by calling its BMI functions over the web. Furthermore, a revised version of EMELI (Peckham, 2015), an Experimental Modeling Environment for Linking and Interoperability, is chosen as the framework for coupling BMI-enabled web service models. EMELI allows users to combine a set of component models into a complex model by standardizing model interface using BMI as well as providing a set of utilities smoothing the integration process (e.g., temporal interpolation). We modify the original EMELI so that the revised modeling framework is able to initialize, execute and find the dependencies of the BMI-enabled web service models. By using the revised EMELI, an example will be presented on integrating a set of topoflow model components that are BMI-enabled and exposed as web services. Reference: Peckham, S.D. (2014) EMELI 1.0: An experimental smart modeling framework for automatic coupling of self-describing models, Proceedings of HIC 2014, 11th International Conf. on Hydroinformatics, New York, NY.

Redesigning the Human-Machine Interface for Computer-Mediated Visual Technologies.

ERIC Educational Resources Information Center

Acker, Stephen R.

1986-01-01

This study examined an application of a human machine interface which relies on the use of optical bar codes incorporated in a computer-based module to teach radio production. The sequencing procedure used establishes the user rather than the computer as the locus of control for the mediated instruction. (Author/MBR)

Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke.

PubMed

McConnell, Alistair C; Moioli, Renan C; Brasil, Fabricio L; Vallejo, Marta; Corne, David W; Vargas, Patricia A; Stokes, Adam A

2017-06-28

To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation. The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke. A total of 110 commercial and non-commercial hand and wrist devices, spanning the 2 major core designs: end-effector and exoskeleton are reviewed. The growing body of evidence on the efficacy and relevance of incorporating brain-machine interfaces in stroke rehabilitation is summarized. The challenges involved in integrating robotic rehabilitation into the healthcare system are discussed. This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.

Adapting human-machine interfaces to user performance.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2008-01-01

The goal of this study was to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user of a human-machine interface and the controlled device. In this experiment, subjects' high-dimensional finger motions remotely controlled the joint angles of a simulated planar 2-link arm, which was used to hit targets on a computer screen. Subjects were required to move the cursor at the endpoint of the simulated arm.

Proceedings of the 1986 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1986-01-01

This book presents the papers given at a conference on man-machine systems. Topics considered at the conference included neural model-based cognitive theory and engineering, user interfaces, adaptive and learning systems, human interaction with robotics, decision making, the testing and evaluation of expert systems, software development, international conflict resolution, intelligent interfaces, automation in man-machine system design aiding, knowledge acquisition in expert systems, advanced architectures for artificial intelligence, pattern recognition, knowledge bases, and machine vision.

Man-machine interface for the control of a lunar transport machine

NASA Technical Reports Server (NTRS)

Ashley, Richard; Bacon, Loring; Carlton, Scott Tim; May, Mark; Moore, Jimmy; Peek, Dennis

1987-01-01

A proposed first generation human interface control panel is described which will be used to control SKITTER, a three-legged lunar walking machine. Under development at Georgia Tech, SKITTER will be a multi-purpose, un-manned vehicle capable of preparing a site for the proposed lunar base in advance of the arrival of men. This walking machine will be able to accept modular special purpose tools, such as a crane, a core sampling drill, and a digging device, among others. The project was concerned with the design of a human interface which could be used, from earth, to control the movements of SKITTER on the lunar surface. Preliminary inquiries were also made into necessary modifications required to adapt the panel to both a shirt-sleeve lunar environment and to a mobile unit which could be used by a man in a space suit at a lunar work site.

Chronic, Wireless Recordings of Large Scale Brain Activity in Freely Moving Rhesus Monkeys

PubMed Central

Schwarz, David A.; Lebedev, Mikhail A.; Hanson, Timothy L.; Dimitrov, Dragan F.; Lehew, Gary; Meloy, Jim; Rajangam, Sankaranarayani; Subramanian, Vivek; Ifft, Peter J.; Li, Zheng; Ramakrishnan, Arjun; Tate, Andrew; Zhuang, Katie; Nicolelis, Miguel A.L.

2014-01-01

Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 units per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years), and recording of a broad range of behaviors, e.g. social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research, while providing a framework for the development and testing of clinically relevant neuroprostheses. PMID:24776634

Data storage technology: Hardware and software, Appendix B

NASA Technical Reports Server (NTRS)

Sable, J. D.

1972-01-01

This project involves the development of more economical ways of integrating and interfacing new storage devices and data processing programs into a computer system. It involves developing interface standards and a software/hardware architecture which will make it possible to develop machine independent devices and programs. These will interface with the machine dependent operating systems of particular computers. The development project will not be to develop the software which would ordinarily be the responsibility of the manufacturer to supply, but to develop the standards with which that software is expected to confirm in providing an interface with the user or storage system.

21 CFR 870.4220 - Cardiopulmonary bypass heart-lung machine console.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Cardiopulmonary bypass heart-lung machine console... Cardiopulmonary bypass heart-lung machine console. (a) Identification. A cardiopulmonary bypass heart-lung machine... heart-lung machine. The console is designed to interface with the basic units used in a gas exchange...

21 CFR 870.4220 - Cardiopulmonary bypass heart-lung machine console.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Cardiopulmonary bypass heart-lung machine console... Cardiopulmonary bypass heart-lung machine console. (a) Identification. A cardiopulmonary bypass heart-lung machine... heart-lung machine. The console is designed to interface with the basic units used in a gas exchange...

21 CFR 870.4220 - Cardiopulmonary bypass heart-lung machine console.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Cardiopulmonary bypass heart-lung machine console... Cardiopulmonary bypass heart-lung machine console. (a) Identification. A cardiopulmonary bypass heart-lung machine... heart-lung machine. The console is designed to interface with the basic units used in a gas exchange...

21 CFR 870.4220 - Cardiopulmonary bypass heart-lung machine console.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Cardiopulmonary bypass heart-lung machine console... Cardiopulmonary bypass heart-lung machine console. (a) Identification. A cardiopulmonary bypass heart-lung machine... heart-lung machine. The console is designed to interface with the basic units used in a gas exchange...

21 CFR 870.4220 - Cardiopulmonary bypass heart-lung machine console.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Cardiopulmonary bypass heart-lung machine console... Cardiopulmonary bypass heart-lung machine console. (a) Identification. A cardiopulmonary bypass heart-lung machine... heart-lung machine. The console is designed to interface with the basic units used in a gas exchange...

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

PubMed Central

Castellini, Claudio; Artemiadis, Panagiotis; Wininger, Michael; Ajoudani, Arash; Alimusaj, Merkur; Bicchi, Antonio; Caputo, Barbara; Craelius, William; Dosen, Strahinja; Englehart, Kevin; Farina, Dario; Gijsberts, Arjan; Godfrey, Sasha B.; Hargrove, Levi; Ison, Mark; Kuiken, Todd; Marković, Marko; Pilarski, Patrick M.; Rupp, Rüdiger; Scheme, Erik

2014-01-01

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it. PMID:25177292

Techniques and applications for binaural sound manipulation in human-machine interfaces

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1990-01-01

The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

Techniques and applications for binaural sound manipulation in human-machine interfaces

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1992-01-01

The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements

NASA Astrophysics Data System (ADS)

Kilicarslan, Atilla; Grossman, Robert G.; Contreras-Vidal, Jose Luis

2016-04-01

Objective. Non-invasive measurement of human neural activity based on the scalp electroencephalogram (EEG) allows for the development of biomedical devices that interface with the nervous system for scientific, diagnostic, therapeutic, or restorative purposes. However, EEG recordings are often considered as prone to physiological and non-physiological artifacts of different types and frequency characteristics. Among them, ocular artifacts and signal drifts represent major sources of EEG contamination, particularly in real-time closed-loop brain-machine interface (BMI) applications, which require effective handling of these artifacts across sessions and in natural settings. Approach. We extend the usage of a robust adaptive noise cancelling (ANC) scheme ({H}∞ filtering) for removal of eye blinks, eye motions, amplitude drifts and recording biases simultaneously. We also characterize the volume conduction, by estimating the signal propagation levels across all EEG scalp recording areas due to ocular artifact generators. We find that the amplitude and spatial distribution of ocular artifacts vary greatly depending on the electrode location. Therefore, fixed filtering parameters for all recording areas would naturally hinder the true overall performance of an ANC scheme for artifact removal. We treat each electrode as a separate sub-system to be filtered, and without the loss of generality, they are assumed to be uncorrelated and uncoupled. Main results. Our results show over 95-99.9% correlation between the raw and processed signals at non-ocular artifact regions, and depending on the contamination profile, 40-70% correlation when ocular artifacts are dominant. We also compare our results with the offline independent component analysis and artifact subspace reconstruction methods, and show that some local quantities are handled better by our sample-adaptive real-time framework. Decoding performance is also compared with multi-day experimental data from 2 subjects, totaling 19 sessions, with and without {H}∞ filtering of the raw data. Significance. The proposed method allows real-time adaptive artifact removal for EEG-based closed-loop BMI applications and mobile EEG studies in general, thereby increasing the range of tasks that can be studied in action and context while reducing the need for discarding data due to artifacts. Significant increase in decoding performances also justify the effectiveness of the method to be used in real-time closed-loop BMI applications.

Literate Specification: Using Design Rationale To Support Formal Methods in the Development of Human-Machine Interfaces.

ERIC Educational Resources Information Center

Johnson, Christopher W.

1996-01-01

The development of safety-critical systems (aircraft cockpits and reactor control rooms) is qualitatively different from that of other interactive systems. These differences impose burdens on design teams that must ensure the development of human-machine interfaces. Analyzes strengths and weaknesses of formal methods for the design of user…

The Portals 4.0 network programming interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian W.; Brightwell, Ronald Brian; Pedretti, Kevin

2012-11-01

This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generationmore » of machines employing advanced network interface architectures that support enhanced offload capabilities.« less

Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

PubMed Central

Ghafoor, Usman; Kim, Sohee; Hong, Keum-Shik

2017-01-01

For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve (invasive), and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section Current Prosthetic Technology summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In the Discussion section, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques. PMID:29163122

Knowledge-based load leveling and task allocation in human-machine systems

NASA Technical Reports Server (NTRS)

Chignell, M. H.; Hancock, P. A.

1986-01-01

Conventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.

Transfer of control system interface solutions from other domains to the thermal power industry.

PubMed

Bligård, L-O; Andersson, J; Osvalder, A-L

2012-01-01

In a thermal power plant the operators' roles are to control and monitor the process to achieve efficient and safe production. To achieve this, the human-machine interfaces have a central part. The interfaces need to be updated and upgraded together with the technical functionality to maintain optimal operation. One way of achieving relevant updates is to study other domains and see how they have solved similar issues in their design solutions. The purpose of this paper is to present how interface design solution ideas can be transferred from domains with operator control to thermal power plants. In the study 15 domains were compared using a model for categorisation of human-machine systems. The result from the domain comparison showed that nuclear power, refinery and ship engine control were most similar to thermal power control. From the findings a basic interface structure and three specific display solutions were proposed for thermal power control: process parameter overview, plant overview, and feed water view. The systematic comparison of the properties of a human-machine system allowed interface designers to find suitable objects, structures and navigation logics in a range of domains that could be transferred to the thermal power domain.

European public deliberation on brain machine interface technology: five convergence seminars.

PubMed

Jebari, Karim; Hansson, Sven-Ove

2013-09-01

We present a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of five seminars are presented.

Human Machine Interfaces for Teleoperators and Virtual Environments

NASA Technical Reports Server (NTRS)

Durlach, Nathaniel I. (Compiler); Sheridan, Thomas B. (Compiler); Ellis, Stephen R. (Compiler)

1991-01-01

In Mar. 1990, a meeting organized around the general theme of teleoperation research into virtual environment display technology was conducted. This is a collection of conference-related fragments that will give a glimpse of the potential of the following fields and how they interplay: sensorimotor performance; human-machine interfaces; teleoperation; virtual environments; performance measurement and evaluation methods; and design principles and predictive models.

Human factors in the presentation of computer-generated information - Aspects of design and application in automated flight traffic

NASA Technical Reports Server (NTRS)

Roske-Hofstrand, Renate J.

1990-01-01

The man-machine interface and its influence on the characteristics of computer displays in automated air traffic is discussed. The graphical presentation of spatial relationships and the problems it poses for air traffic control, and the solution of such problems are addressed. Psychological factors involved in the man-machine interface are stressed.

Cursor control by Kalman filter with a non-invasive body–machine interface

PubMed Central

Seáñez-González, Ismael; Mussa-Ivaldi, Ferdinando A

2015-01-01

Objective We describe a novel human–machine interface for the control of a two-dimensional (2D) computer cursor using four inertial measurement units (IMUs) placed on the user’s upper-body. Approach A calibration paradigm where human subjects follow a cursor with their body as if they were controlling it with their shoulders generates a map between shoulder motions and cursor kinematics. This map is used in a Kalman filter to estimate the desired cursor coordinates from upper-body motions. We compared cursor control performance in a centre-out reaching task performed by subjects using different amounts of information from the IMUs to control the 2D cursor. Main results Our results indicate that taking advantage of the redundancy of the signals from the IMUs improved overall performance. Our work also demonstrates the potential of non-invasive IMU-based body–machine interface systems as an alternative or complement to brain–machine interfaces for accomplishing cursor control in 2D space. Significance The present study may serve as a platform for people with high-tetraplegia to control assistive devices such as powered wheelchairs using a joystick. PMID:25242561

Human-machine interface for a VR-based medical imaging environment

NASA Astrophysics Data System (ADS)

Krapichler, Christian; Haubner, Michael; Loesch, Andreas; Lang, Manfred K.; Englmeier, Karl-Hans

1997-05-01

Modern 3D scanning techniques like magnetic resonance imaging (MRI) or computed tomography (CT) produce high- quality images of the human anatomy. Virtual environments open new ways to display and to analyze those tomograms. Compared with today's inspection of 2D image sequences, physicians are empowered to recognize spatial coherencies and examine pathological regions more facile, diagnosis and therapy planning can be accelerated. For that purpose a powerful human-machine interface is required, which offers a variety of tools and features to enable both exploration and manipulation of the 3D data. Man-machine communication has to be intuitive and efficacious to avoid long accustoming times and to enhance familiarity with and acceptance of the interface. Hence, interaction capabilities in virtual worlds should be comparable to those in the real work to allow utilization of our natural experiences. In this paper the integration of hand gestures and visual focus, two important aspects in modern human-computer interaction, into a medical imaging environment is shown. With the presented human- machine interface, including virtual reality displaying and interaction techniques, radiologists can be supported in their work. Further, virtual environments can even alleviate communication between specialists from different fields or in educational and training applications.

Human facial neural activities and gesture recognition for machine-interfacing applications.

PubMed

Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

2011-01-01

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

A novel device for head gesture measurement system in combination with eye-controlled human machine interface

NASA Astrophysics Data System (ADS)

Lin, Chern-Sheng; Ho, Chien-Wa; Chang, Kai-Chieh; Hung, San-Shan; Shei, Hung-Jung; Yeh, Mau-Shiun

2006-06-01

This study describes the design and combination of an eye-controlled and a head-controlled human-machine interface system. This system is a highly effective human-machine interface, detecting head movement by changing positions and numbers of light sources on the head. When the users utilize the head-mounted display to browse a computer screen, the system will catch the images of the user's eyes with CCD cameras, which can also measure the angle and position of the light sources. In the eye-tracking system, the program in the computer will locate each center point of the pupils in the images, and record the information on moving traces and pupil diameters. In the head gesture measurement system, the user wears a double-source eyeglass frame, so the system catches images of the user's head by using a CCD camera in front of the user. The computer program will locate the center point of the head, transferring it to the screen coordinates, and then the user can control the cursor by head motions. We combine the eye-controlled and head-controlled human-machine interface system for the virtual reality applications.

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.

The portals 4.0.1 network programming interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian W.; Brightwell, Ronald Brian; Pedretti, Kevin

2013-04-01

This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generationmore » of machines employing advanced network interface architectures that support enhanced offload capabilities. 3« less

Optimal design method to minimize users' thinking mapping load in human-machine interactions.

PubMed

Huang, Yanqun; Li, Xu; Zhang, Jie

2015-01-01

The discrepancy between human cognition and machine requirements/behaviors usually results in serious mental thinking mapping loads or even disasters in product operating. It is important to help people avoid human-machine interaction confusions and difficulties in today's mental work mastered society. Improving the usability of a product and minimizing user's thinking mapping and interpreting load in human-machine interactions. An optimal human-machine interface design method is introduced, which is based on the purpose of minimizing the mental load in thinking mapping process between users' intentions and affordance of product interface states. By analyzing the users' thinking mapping problem, an operating action model is constructed. According to human natural instincts and acquired knowledge, an expected ideal design with minimized thinking loads is uniquely determined at first. Then, creative alternatives, in terms of the way human obtains operational information, are provided as digital interface states datasets. In the last, using the cluster analysis method, an optimum solution is picked out from alternatives, by calculating the distances between two datasets. Considering multiple factors to minimize users' thinking mapping loads, a solution nearest to the ideal value is found in the human-car interaction design case. The clustering results show its effectiveness in finding an optimum solution to the mental load minimizing problems in human-machine interaction design.

Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees.

PubMed

Agashe, H A; Paek, A Y; Contreras-Vidal, J L

2016-01-01

Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration. In this study we tested if transradial amputees (A1 and A2) could control grasp preshaping in a prosthetic device using a noninvasive electroencephalography (EEG)-based closed-loop BMI system. Participants attempted to grasp presented objects by controlling two grasping synergies, in 12 sessions performed over 5 weeks. Prior to closed-loop control, the first six sessions included a decoder calibration phase using action observation by the participants; thereafter, the decoder was fixed to examine neuroprosthetic performance in the absence of decoder recalibration. Ability of participants to control the prosthetic was measured by the success rate of grasping; ie, the percentage of trials within a session in which presented objects were successfully grasped. Participant A1 maintained a steady success rate (63±3%) across sessions (significantly above chance [41±5%] for 11 sessions). Participant A2, who was under the influence of pharmacological treatment for depression, hormone imbalance, pain management (for phantom pain as well as shoulder joint inflammation), and drug dependence, achieved a success rate of 32±2% across sessions (significantly above chance [27±5%] in only two sessions). EEG signal quality was stable across sessions, but the decoders created during the first six sessions showed variation, indicating EEG features relevant to decoding at a smaller timescale (100ms) may not be stable. Overall, our results show that (a) an EEG-based BMI for grasping is a feasible strategy for further investigation of prosthetic control by amputees, and (b) factors that may affect brain activity such as medication need further examination to improve accuracy and stability of BMI performance. © 2016 Elsevier B.V. All rights reserved.

Support Vector Machines to improve physiologic hot flash measures: application to the ambulatory setting.

PubMed

Thurston, Rebecca C; Hernandez, Javier; Del Rio, Jose M; De La Torre, Fernando

2011-07-01

Most midlife women have hot flashes. The conventional criterion (≥2 μmho rise/30 s) for classifying hot flashes physiologically has shown poor performance. We improved this performance in the laboratory with Support Vector Machines (SVMs), a pattern classification method. We aimed to compare conventional to SVM methods to classify hot flashes in the ambulatory setting. Thirty-one women with hot flashes underwent 24 h of ambulatory sternal skin conductance monitoring. Hot flashes were quantified with conventional (≥2 μmho/30 s) and SVM methods. Conventional methods had low sensitivity (sensitivity=.57, specificity=.98, positive predictive value (PPV)=.91, negative predictive value (NPV)=.90, F1=.60), with performance lower with higher body mass index (BMI). SVMs improved this performance (sensitivity=.87, specificity=.97, PPV=.90, NPV=.96, F1=.88) and reduced BMI variation. SVMs can improve ambulatory physiologic hot flash measures. Copyright © 2010 Society for Psychophysiological Research.

Soft, Conformal Bioelectronics for a Wireless Human-Wheelchair Interface

PubMed Central

Mishra, Saswat; Norton, James J. S.; Lee, Yongkuk; Lee, Dong Sup; Agee, Nicolas; Chen, Yanfei; Chun, Youngjae; Yeo, Woon-Hong

2017-01-01

There are more than 3 million people in the world whose mobility relies on wheelchairs. Recent advancement on engineering technology enables more intuitive, easy-to-use rehabilitation systems. A human-machine interface that uses non-invasive, electrophysiological signals can allow a systematic interaction between human and devices; for example, eye movement-based wheelchair control. However, the existing machine-interface platforms are obtrusive, uncomfortable, and often cause skin irritations as they require a metal electrode affixed to the skin with a gel and acrylic pad. Here, we introduce a bioelectronic system that makes dry, conformal contact to the skin. The mechanically comfortable sensor records high-fidelity electrooculograms, comparable to the conventional gel electrode. Quantitative signal analysis and infrared thermographs show the advantages of the soft biosensor for an ergonomic human-machine interface. A classification algorithm with an optimized set of features shows the accuracy of 94% with five eye movements. A Bluetooth-enabled system incorporating the soft bioelectronics demonstrates a precise, hands-free control of a robotic wheelchair via electrooculograms. PMID:28152485

Flexible Parsing.

DTIC Science & Technology

1986-06-30

Machine Studies .. 14. Minton, S. N., Hayes, P. J., and Fain, J. E. Controlling Search in Flexible Parsing. Proc. Ninth Int. Jt. Conf. on Artificial...interaction through the COUSIN command interface", International Journal of Man- Machine Studies , Vol. 19, No. 3, September 1983, pp. 285-305. 8...in a gracefully interacting user interface," "Dynamic strategy selection in flexible parsing," and "Parsing spoken language: a semantic case frame

Problems in modeling man machine control behavior in biodynamic environments

NASA Technical Reports Server (NTRS)

Jex, H. R.

1972-01-01

Reviewed are some current problems in modeling man-machine control behavior in a biodynamic environment. It is given in two parts: (1) a review of the models which are appropriate for manual control behavior and the added elements necessary to deal with biodynamic interfaces; and (2) a review of some biodynamic interface pilot/vehicle problems which have occurred, been solved, or need to be solved.

Household wireless electroencephalogram hat

NASA Astrophysics Data System (ADS)

Szu, Harold; Hsu, Charles; Moon, Gyu; Yamakawa, Takeshi; Tran, Binh

2012-06-01

We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

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

Combat Automation for Airborne Weapon Systems: Man/Machine Interface Trends and Technologies (L’Automatisation du Combat Aerien: Tendances et Technologies pour l’Interface Homme/Machine)

DTIC Science & Technology

1993-04-01

Homme /Machine) Aocesion For ; 1 [ NTIS ’ D:i: Ü J-H CRA& l TAB 3...I’utilisateur. - Enfm, utilise avec le bouton droit de la souris, le poten- tiom&tre de temps 6coul6 permet de charger une alterna- tive dans le syst&me...a a a a rn£Q £ OB E o 15 l | I? ^©J&Mß) NATO ^ OTAN 7 RUE ANCELLE • 92200 NEUILLY-SÜR-SEINE DIFFUSION DES PUBLICATIONS FRANCE AGARD

Chip breaking system for automated machine tool

DOEpatents

Arehart, Theodore A.; Carey, Donald O.

1987-01-01

The invention is a rotary selectively directional valve assembly for use in an automated turret lathe for directing a stream of high pressure liquid machining coolant to the interface of a machine tool and workpiece for breaking up ribbon-shaped chips during the formation thereof so as to inhibit scratching or other marring of the machined surfaces by these ribbon-shaped chips. The valve assembly is provided by a manifold arrangement having a plurality of circumferentially spaced apart ports each coupled to a machine tool. The manifold is rotatable with the turret when the turret is positioned for alignment of a machine tool in a machining relationship with the workpiece. The manifold is connected to a non-rotational header having a single passageway therethrough which conveys the high pressure coolant to only the port in the manifold which is in registry with the tool disposed in a working relationship with the workpiece. To position the machine tools the turret is rotated and one of the tools is placed in a material-removing relationship of the workpiece. The passageway in the header and one of the ports in the manifold arrangement are then automatically aligned to supply the machining coolant to the machine tool workpiece interface for breaking up of the chips as well as cooling the tool and workpiece during the machining operation.

Operation of micro and molecular machines: a new concept with its origins in interface science.

PubMed

Ariga, Katsuhiko; Ishihara, Shinsuke; Izawa, Hironori; Xia, Hong; Hill, Jonathan P

2011-03-21

A landmark accomplishment of nanotechnology would be successful fabrication of ultrasmall machines that can work like tweezers, motors, or even computing devices. Now we must consider how operation of micro- and molecular machines might be implemented for a wide range of applications. If these machines function only under limited conditions and/or require specialized apparatus then they are useless for practical applications. Therefore, it is important to carefully consider the access of functionality of the molecular or nanoscale systems by conventional stimuli at the macroscopic level. In this perspective, we will outline the position of micro- and molecular machines in current science and technology. Most of these machines are operated by light irradiation, application of electrical or magnetic fields, chemical reactions, and thermal fluctuations, which cannot always be applied in remote machine operation. We also propose strategies for molecular machine operation using the most conventional of stimuli, that of macroscopic mechanical force, achieved through mechanical operation of molecular machines located at an air-water interface. The crucial roles of the characteristics of an interfacial environment, i.e. connection between macroscopic dimension and nanoscopic function, and contact of media with different dielectric natures, are also described.

Design of Human-Machine Interface and altering of pelvic obliquity with RGR Trainer.

PubMed

Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

2011-01-01

The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system's ability to alter the pattern of movement of the pelvis during gait in a healthy subject. Results are shown for experiments during which we induced hip-hiking - in healthy subjects. Our findings indicate that the RGR Trainer has the ability of affecting pelvic obliquity during gait. Furthermore, we provide preliminary evidence of short-term retention of the modified pelvic obliquity pattern induced by the RGR Trainer. © 2011 IEEE

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.

Design of Human – Machine Interface and Altering of Pelvic Obliquity with RGR Trainer

PubMed Central

Pietrusinski, Maciej; Unluhisarcikli, Ozer; Mavroidis, Constantinos; Cajigas, Iahn; Bonato, Paolo

2012-01-01

The Robotic Gait Rehabilitation (RGR) Trainer targets secondary gait deviations in stroke survivors undergoing rehabilitation. Using an impedance control strategy and a linear electromagnetic actuator, the device generates a force field to control pelvic obliquity through a Human-Machine Interface (i.e. a lower body exoskeleton). Herein we describe the design of the RGR Trainer Human-Machine Interface (HMI) and we demonstrate the system’s ability to alter the pattern of movement of the pelvis during gait in a healthy subject. Results are shown for experiments during which we induced hip-hiking – in healthy subjects. Our findings indicate that the RGR Trainer has the ability of affecting pelvic obliquity during gait. Furthermore, we provide preliminary evidence of short-term retention of the modified pelvic obliquity pattern induced by the RGR Trainer. PMID:22275693

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.

40 CFR 63.464 - Alternative standards.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (a)(2) of this section. (1) If the cleaning machine has a solvent/air interface, as defined in § 63... cleaning machines 153 New in-line solvent cleaning machines 99 (2) If the cleaning machine is a batch vapor... requirements specified in paragraphs (a)(2)(i) and (a)(2)(ii) of this section. (i) Maintain a log of solvent...

40 CFR 63.464 - Alternative standards.

Code of Federal Regulations, 2010 CFR

2010-07-01

... (a)(2) of this section. (1) If the cleaning machine has a solvent/air interface, as defined in § 63... cleaning machines 153 New in-line solvent cleaning machines 99 (2) If the cleaning machine is a batch vapor... requirements specified in paragraphs (a)(2)(i) and (a)(2)(ii) of this section. (i) Maintain a log of solvent...

Man-machine interface requirements - advanced technology

NASA Technical Reports Server (NTRS)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton.

PubMed

Yin, Yue H; Fan, Yuan J; Xu, Li D

2012-07-01

Although a lower extremity exoskeleton shows great prospect in the rehabilitation of the lower limb, it has not yet been widely applied to the clinical rehabilitation of the paralyzed. This is partly caused by insufficient information interactions between the paralyzed and existing exoskeleton that cannot meet the requirements of harmonious control. In this research, a bidirectional human-machine interface including a neurofuzzy controller and an extended physiological proprioception (EPP) feedback system is developed by imitating the biological closed-loop control system of human body. The neurofuzzy controller is built to decode human motion in advance by the fusion of the fuzzy electromyographic signals reflecting human motion intention and the precise proprioception providing joint angular feedback information. It transmits control information from human to exoskeleton, while the EPP feedback system based on haptic stimuli transmits motion information of the exoskeleton back to the human. Joint angle and torque information are transmitted in the form of air pressure to the human body. The real-time bidirectional human-machine interface can help a patient with lower limb paralysis to control the exoskeleton with his/her healthy side and simultaneously perceive motion on the paralyzed side by EPP. The interface rebuilds a closed-loop motion control system for paralyzed patients and realizes harmonious control of the human-machine system.

Active tactile exploration using a brain-machine-brain interface.

PubMed

O'Doherty, Joseph E; Lebedev, Mikhail A; Ifft, Peter J; Zhuang, Katie Z; Shokur, Solaiman; Bleuler, Hannes; Nicolelis, Miguel A L

2011-10-05

Brain-machine interfaces use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain-machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.

Sitting in the Pilot's Seat; Optimizing Human-Systems Interfaces for Unmanned Aerial Vehicles

NASA Technical Reports Server (NTRS)

Queen, Steven M.; Sanner, Kurt Gregory

2011-01-01

One of the pilot-machine interfaces (the forward viewing camera display) for an Unmanned Aerial Vehicle called the DROID (Dryden Remotely Operated Integrated Drone) will be analyzed for optimization. The goal is to create a visual display for the pilot that as closely resembles an out-the-window view as possible. There are currently no standard guidelines for designing pilot-machine interfaces for UAVs. Typically, UAV camera views have a narrow field, which limits the situational awareness (SA) of the pilot. Also, at this time, pilot-UAV interfaces often use displays that have a diagonal length of around 20". Using a small display may result in a distorted and disproportional view for UAV pilots. Making use of a larger display and a camera lens with a wider field of view may minimize the occurrences of pilot error associated with the inability to see "out the window" as in a manned airplane. It is predicted that the pilot will have a less distorted view of the DROID s surroundings, quicker response times and more stable vehicle control. If the experimental results validate this concept, other UAV pilot-machine interfaces will be improved with this design methodology.

Mold Heating and Cooling Pump Package Operator Interface Controls Upgrade

DOE Office of Scientific and Technical Information (OSTI.GOV)

Josh A. Salmond

2009-08-07

The modernization of the Mold Heating and Cooling Pump Package Operator Interface (MHC PP OI) consisted of upgrading the antiquated single board computer with a proprietary operating system to off-the-shelf hardware and off-the-shelf software with customizable software options. The pump package is the machine interface between a central heating and cooling system that pumps heat transfer fluid through an injection or compression mold base on a local plastic molding machine. The operator interface provides the intelligent means of controlling this pumping process. Strict temperature control of a mold allows the production of high quality parts with tight tolerances and lowmore » residual stresses. The products fabricated are used on multiple programs.« less

Materials and optimized designs for human-machine interfaces via epidermal electronics.

PubMed

Jeong, Jae-Woong; Yeo, Woon-Hong; Akhtar, Aadeel; Norton, James J S; Kwack, Young-Jin; Li, Shuo; Jung, Sung-Young; Su, Yewang; Lee, Woosik; Xia, Jing; Cheng, Huanyu; Huang, Yonggang; Choi, Woon-Seop; Bretl, Timothy; Rogers, John A

2013-12-17

Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Tool for Assessing the Text Legibility of Digital Human Machine Interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roger Lew; Ronald L. Boring; Thomas A. Ulrich

2015-08-01

A tool intended to aid qualified professionals in the assessment of the legibility of text presented on a digital display is described. The assessment of legibility is primarily for the purposes of designing and analyzing human machine interfaces in accordance with NUREG-0700 and MIL-STD 1472G. The tool addresses shortcomings of existing guidelines by providing more accurate metrics of text legibility with greater sensitivity to design alternatives.

Mobile Tactical HF/VHF/EW System for Ground Forces

DTIC Science & Technology

1989-09-01

presen- tation of what I have learned . I would like to thank my advisor, Professor Robert Partelow, and co-advisor, Commander James R. Powell, for the...analyze newly developed systems to determine how the man- machine interfaces of such systems can best be designed for optimal use by the operators. B...terminals and other controls. If factors like luminance ratio, reflectance, glare illuminance are allowed for good man- machine interface then an effective

Reverse-micelle-induced porous pressure-sensitive rubber for wearable human-machine interfaces.

PubMed

Jung, Sungmook; Kim, Ji Hoon; Kim, Jaemin; Choi, Suji; Lee, Jongsu; Park, Inhyuk; Hyeon, Taeghwan; Kim, Dae-Hyeong

2014-07-23

A novel method to produce porous pressure-sensitive rubber is developed. For the controlled size distribution of embedded micropores, solution-based procedures using reverse micelles are adopted. The piezosensitivity of the pressure sensitive rubber is significantly increased by introducing micropores. Using this method, wearable human-machine interfaces are fabricated, which can be applied to the remote control of a robot. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting Increased Blood Pressure Using Machine Learning

PubMed Central

Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Soares, Telma de Jesus; dos Reis, Luciana Araujo

2014-01-01

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R 2 (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R 2 (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power. PMID:24669313

Predicting increased blood pressure using machine learning.

PubMed

Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Gomes, Cristiano Mauro Assis; Soares, Telma de Jesus; Dos Reis, Luciana Araujo; Santos, Joselito

2014-01-01

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

Finite element analysis when orthogonal cutting of hybrid composite CFRP/Ti

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2015-07-01

Hybrid composite, especially CFRP/Ti stack, is usually considered as an innovative structural configuration for manufacturing the key load-bearing components in modern aerospace industry. This paper originally proposed an FE model to simulate the total chip formation process dominated the hybrid cutting operation. The hybrid composite model was established based on three physical constituents, i.e., Ti constituent, interface and CFRP constituent. Different constitutive models and damage criteria were introduced to replicate the interrelated cutting behaviour of the stack material. The CFRP/Ti interface was modelled as a third phase through the concept of cohesive zone (CZ). Particular attention was made on the comparative studies of the influence of different cutting-sequence strategies on the machining responses induced in hybrid stack cutting. The numerical results emphasized the pivotal role of cutting-sequence strategy on the various machining induced responses including cutting-force generation, machined surface quality and induced interface damage.

Estimation of Locomotion States of a Rat by Neural Signals from the Motor Cortices Based on a Linear Correlation Model

NASA Astrophysics Data System (ADS)

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

We are developing a brain-machine interface (BMI) called “RatCar," a small vehicle controlled by the neural signals of a rat's brain. An unconfined adult rat with a set of bundled neural electrodes in the brain rides on the vehicle. Each bundle consists of four tungsten wires isolated with parylene polymer. These bundles were implanted in the primary motor and premotor cortices in both hemispheres of the brain. In this paper, methods and results for estimating locomotion speed and directional changes are described. Neural signals were recorded as the rat moved in a straight line and as it changed direction in a curve. Spike-like waveforms were then detected and classified into several clusters to calculate a firing rate for each neuron. The actual locomotion velocity and directional changes of the rat were recorded concurrently. Finally, the locomotion states were correlated with the neural firing rates using a simple linear model. As a result, the abstract estimation of the locomotion velocity and directional changes were achieved.

Neural Prediction of Multidimensional Decisions in Monkey Superior Colliculus

NASA Astrophysics Data System (ADS)

Hasegawa, Ryohei P.; Hasegawa, Yukako T.; Segraves, Mark A.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for “neural mind reading,” we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions.

PubMed

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

2017-07-05

Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions. Copyright © 2017 Elsevier Inc. All rights reserved.

Compatibility Problems of Network Interfacing.

ERIC Educational Resources Information Center

Stevens, Mary Elizabeth

From the standpoint of information network technology there is a necessary emphasis upon compatibility requirements which, in turn, will be met at least in part by various techniques of achieving convertibility --- between machine and machine, between man and machine, and between man and man. It may be hoped that improved compatibilities between…

Model validation of untethered, ultrasonic neural dust motes for cortical recording.

PubMed

Seo, Dongjin; Carmena, Jose M; Rabaey, Jan M; Maharbiz, Michel M; Alon, Elad

2015-04-15

A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10-100 μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127 μm)(3) neural dust motes immersed in water 3 cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246 ppm backscatter, resulting in a maximum received power of ∼0.5 μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions. Copyright © 2014 Elsevier B.V. All rights reserved.

Gesture-Controlled Interfaces for Self-Service Machines

NASA Technical Reports Server (NTRS)

Cohen, Charles J.; Beach, Glenn

2006-01-01

Gesture-controlled interfaces are software- driven systems that facilitate device control by translating visual hand and body signals into commands. Such interfaces could be especially attractive for controlling self-service machines (SSMs) for example, public information kiosks, ticket dispensers, gasoline pumps, and automated teller machines (see figure). A gesture-controlled interface would include a vision subsystem comprising one or more charge-coupled-device video cameras (at least two would be needed to acquire three-dimensional images of gestures). The output of the vision system would be processed by a pure software gesture-recognition subsystem. Then a translator subsystem would convert a sequence of recognized gestures into commands for the SSM to be controlled; these could include, for example, a command to display requested information, change control settings, or actuate a ticket- or cash-dispensing mechanism. Depending on the design and operational requirements of the SSM to be controlled, the gesture-controlled interface could be designed to respond to specific static gestures, dynamic gestures, or both. Static and dynamic gestures can include stationary or moving hand signals, arm poses or motions, and/or whole-body postures or motions. Static gestures would be recognized on the basis of their shapes; dynamic gestures would be recognized on the basis of both their shapes and their motions. Because dynamic gestures include temporal as well as spatial content, this gesture- controlled interface can extract more information from dynamic than it can from static gestures.

Human-machine interface hardware: The next decade

NASA Technical Reports Server (NTRS)

Marcus, Elizabeth A.

1991-01-01

In order to understand where human-machine interface hardware is headed, it is important to understand where we are today, how we got there, and what our goals for the future are. As computers become more capable, faster, and programs become more sophisticated, it becomes apparent that the interface hardware is the key to an exciting future in computing. How can a user interact and control a seemingly limitless array of parameters effectively? Today, the answer is most often a limitless array of controls. The link between these controls and human sensory motor capabilities does not utilize existing human capabilities to their full extent. Interface hardware for teleoperation and virtual environments is now facing a crossroad in design. Therefore, we as developers need to explore how the combination of interface hardware, human capabilities, and user experience can be blended to get the best performance today and in the future.

Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking.

PubMed

Zacksenhouse, Miriam; Lebedev, Mikhail A; Nicolelis, Miguel A L

2014-01-01

What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA) is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i) reaching experiments with Brain-Machine Interface (BMI), and (ii) locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.

Body-Machine Interfaces after Spinal Cord Injury: Rehabilitation and Brain Plasticity.

PubMed

Seáñez-González, Ismael; Pierella, Camilla; Farshchiansadegh, Ali; Thorp, Elias B; Wang, Xue; Parrish, Todd; Mussa-Ivaldi, Ferdinando A

2016-12-19

The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface. Subjects' upper-body ability was evaluated before the start, in the middle and a day after the completion of training. MR imaging data were acquired before the start and within two days of the completion of training. Subjects learned to use upper-body movements that survived the injury to control the body-machine interface and improved their performance with practice. Motor training increased Manual Muscle Test scores and the isometric force of subjects' shoulders and upper arms. Moreover, motor training increased fractional anisotropy (FA) values in the cingulum of the left hemisphere by 6.02% on average, indicating localized white matter microstructure changes induced by activity-dependent modulation of axon diameter, myelin thickness or axon number. This body-machine interface may serve as a platform to develop a new generation of assistive-rehabilitative devices that promote the use of, and that re-strengthen, the motor and sensory functions that survived the injury.

Proceedings of the Annual Seminar (First), ’The Art of Communications Interfaces’, Held at Fort Monmouth, New Jersey on 22 April 1976,

DTIC Science & Technology

Both the oldest and the newest problem areas in communications electronics interfaces are discussed in conjunction with the currently critical...digital communication system evolution. The oldest interface problem, still the most essential is the man machine communications interfaces. The newest is

A Machine Learning and Optimization Toolkit for the Swarm

DTIC Science & Technology

2014-11-17

Machine   Learning  and  Op0miza0on   Toolkit  for  the  Swarm   Ilge  Akkaya,  Shuhei  Emoto...3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE A Machine Learning and Optimization Toolkit for the Swarm 5a. CONTRACT NUMBER... machine   learning   methodologies  by  providing  the  right  interfaces  between   machine   learning  tools  and

In vivo performance of a microelectrode neural probe with integrated drug delivery

PubMed Central

Rohatgi, Pratik; Langhals, Nicholas B.; Kipke, Daryl R.; Patil, Parag G.

2014-01-01

Object The availability of sophisticated neural probes is a key prerequisite in the development of future brain machine interfaces (BMI). In this study, we developed and validated a neural probe design capable of simultaneous drug delivery and electrophysiology recordings in vivo. Focal drug delivery has promise to dramatically extend the recording lives of neural probes, a limiting factor to clinical adoption of BMI technology. Methods To form the multifunctional neural probe, we affixed a 16-channel microfabricated silicon electrode array to a fused silica catheter. Three experiments were conducted to characterize the performance of the device. Experiment 1 examines cellular damage from probe insertion and the drug distribution in tissue. Experiment 2 measures the effects of saline infusions delivered through the probe on concurrent electrophysiology. Experiment 3 demonstrates that a physiologically relevant amount of drug can be delivered in a controlled fashion. For these experiments, Hoechst and propidium iodide were used to assess insertion trauma and the tissue distribution of the infusate. Artificial cerebral spinal fluid and tetrodotoxin were injected to determine the efficacy of drug delivery. Results The newly developed multifunctional neural probes were successfully inserted into rat cortex and were able to deliver fluids and drugs that resulted in the expected electrophysiological and histological responses. The damage from insertion of the device into brain tissue was substantially less than the volume of drug dispersion in tissue. Electrophysiological activity, including both individual spikes as well as local field potentials, was successfully recorded with this device during real-time drug delivery. No significant changes were seen in response to delivery of artificial cerebral spinal fluid as a control experiment, whereas delivery of tetrodotoxin produced the expected result of suppressing all spiking activity in the vicinity of the catheter outlet. Conclusions Multifunctional neural probes such as the ones developed and validated within this study have great potential to help further understand the design space and criteria for the next generation of neural probe technology. By incorporating integrated drug delivery functionality into the probes, new treatment options for neurological disorders and regenerative neural interfaces utilizing localized and feedback controlled delivery of drugs can be realized in the near future. PMID:19569896

Object Management Group object transaction service based on an X/Open and International Organization for Standardization open systems interconnection transaction processing kernel

NASA Astrophysics Data System (ADS)

Liang, J.; Sédillot, S.; Traverson, B.

1997-09-01

This paper addresses federation of a transactional object standard - Object Management Group (OMG) object transaction service (OTS) - with the X/Open distributed transaction processing (DTP) model and International Organization for Standardization (ISO) open systems interconnection (OSI) transaction processing (TP) communication protocol. The two-phase commit propagation rules within a distributed transaction tree are similar in the X/Open, ISO and OMG models. Building an OTS on an OSI TP protocol machine is possible because the two specifications are somewhat complementary. OTS defines a set of external interfaces without specific internal protocol machine, while OSI TP specifies an internal protocol machine without any application programming interface. Given these observations, and having already implemented an X/Open two-phase commit transaction toolkit based on an OSI TP protocol machine, we analyse the feasibility of using this implementation as a transaction service provider for OMG interfaces. Based on the favourable result of this feasibility study, we are implementing an OTS compliant system, which, by initiating the extensibility and openness strengths of OSI TP, is able to provide interoperability between X/Open DTP and OMG OTS models.

AIMSsim Version 2.3.4 - User Manual

DTIC Science & Technology

2008-01-01

sera en mesure d’utiliser le système efficacement et moyennant une formation minimale, un prototype d’interface humain -machine (IHM) a été développé...d’utiliser l’ensemble de capteurs efficacement et moyennant une formation minimale, un prototype d’interface humain -machine (IHM) a été développé pour...recherche AIMSsim offrent à l’expérimentateur un niveau de simulation assez détaillé pour mener des analyses du rendement humain , qui fournissent à

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

NASA Technical Reports Server (NTRS)

Barrett, Eamon B. (Editor); Pearson, James J. (Editor)

1989-01-01

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

A Natural Language Interface to Databases

NASA Technical Reports Server (NTRS)

Ford, D. R.

1990-01-01

The development of a Natural Language Interface (NLI) is presented which is semantic-based and uses Conceptual Dependency representation. The system was developed using Lisp and currently runs on a Symbolics Lisp machine.

Chips: A Tool for Developing Software Interfaces Interactively.

ERIC Educational Resources Information Center

Cunningham, Robert E.; And Others

This report provides a detailed description of Chips, an interactive tool for developing software employing graphical/computer interfaces on Xerox Lisp machines. It is noted that Chips, which is implemented as a collection of customizable classes, provides the programmer with a rich graphical interface for the creation of rich graphical…

Emotion detection from text

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Jaiswal, Abhijeet; Bhatia, Nikhar

2018-04-01

This paper presents a novel method based on concept of Machine Learning for Emotion Detection using various algorithms of Support Vector Machine and major emotions described are linked to the Word-Net for enhanced accuracy. The approach proposed plays a promising role to augment the Artificial Intelligence in the near future and could be vital in optimization of Human-Machine Interface.

Formal verification of human-automation interaction

NASA Technical Reports Server (NTRS)

Degani, Asaf; Heymann, Michael

2002-01-01

This paper discusses a formal and rigorous approach to the analysis of operator interaction with machines. It addresses the acute problem of detecting design errors in human-machine interaction and focuses on verifying the correctness of the interaction in complex and automated control systems. The paper describes a systematic methodology for evaluating whether the interface provides the necessary information about the machine to enable the operator to perform a specified task successfully and unambiguously. It also addresses the adequacy of information provided to the user via training material (e.g., user manual) about the machine's behavior. The essentials of the methodology, which can be automated and applied to the verification of large systems, are illustrated by several examples and through a case study of pilot interaction with an autopilot aboard a modern commercial aircraft. The expected application of this methodology is an augmentation and enhancement, by formal verification, of human-automation interfaces.

A video, text, and speech-driven realistic 3-d virtual head for human-machine interface.

PubMed

Yu, Jun; Wang, Zeng-Fu

2015-05-01

A multiple inputs-driven realistic facial animation system based on 3-D virtual head for human-machine interface is proposed. The system can be driven independently by video, text, and speech, thus can interact with humans through diverse interfaces. The combination of parameterized model and muscular model is used to obtain a tradeoff between computational efficiency and high realism of 3-D facial animation. The online appearance model is used to track 3-D facial motion from video in the framework of particle filtering, and multiple measurements, i.e., pixel color value of input image and Gabor wavelet coefficient of illumination ratio image, are infused to reduce the influence of lighting and person dependence for the construction of online appearance model. The tri-phone model is used to reduce the computational consumption of visual co-articulation in speech synchronized viseme synthesis without sacrificing any performance. The objective and subjective experiments show that the system is suitable for human-machine interaction.

Advanced warfighter machine interface (Invited Paper)

NASA Astrophysics Data System (ADS)

Franks, Erin

2005-05-01

Future military crewmen may have more individual and shared tasks to complete throughout a mission as a result of smaller crew sizes and an increased number of technology interactions. To maintain reasonable workload levels, the Warfighter Machine Interface (WMI) must provide information in a consistent, logical manner, tailored to the environment in which the soldier will be completing their mission. This paper addresses design criteria for creating an advanced, multi-modal warfighter machine interface for on-the-move mounted operations. The Vetronics Technology Integration (VTI) WMI currently provides capabilities such as mission planning and rehearsal, voice and data communications, and manned/unmanned vehicle payload and mobility control. A history of the crewstation and more importantly, the WMI software will be provided with an overview of requirements and criteria used for completing the design. Multiple phases of field and laboratory testing provide the opportunity to evaluate the design and hardware in stationary and motion environments. Lessons learned related to system usability and user performance are presented with mitigation strategies to be tested in the future.

Open multi-agent control architecture to support virtual-reality-based man-machine interfaces

NASA Astrophysics Data System (ADS)

Freund, Eckhard; Rossmann, Juergen; Brasch, Marcel

2001-10-01

Projective Virtual Reality is a new and promising approach to intuitively operable man machine interfaces for the commanding and supervision of complex automation systems. The user interface part of Projective Virtual Reality heavily builds on latest Virtual Reality techniques, a task deduction component and automatic action planning capabilities. In order to realize man machine interfaces for complex applications, not only the Virtual Reality part has to be considered but also the capabilities of the underlying robot and automation controller are of great importance. This paper presents a control architecture that has proved to be an ideal basis for the realization of complex robotic and automation systems that are controlled by Virtual Reality based man machine interfaces. The architecture does not just provide a well suited framework for the real-time control of a multi robot system but also supports Virtual Reality metaphors and augmentations which facilitate the user's job to command and supervise a complex system. The developed control architecture has already been used for a number of applications. Its capability to integrate sensor information from sensors of different levels of abstraction in real-time helps to make the realized automation system very responsive to real world changes. In this paper, the architecture will be described comprehensively, its main building blocks will be discussed and one realization that is built based on an open source real-time operating system will be presented. The software design and the features of the architecture which make it generally applicable to the distributed control of automation agents in real world applications will be explained. Furthermore its application to the commanding and control of experiments in the Columbus space laboratory, the European contribution to the International Space Station (ISS), is only one example which will be described.

Hand-in-hand advances in biomedical engineering and sensorimotor restoration.

PubMed

Pisotta, Iolanda; Perruchoud, David; Ionta, Silvio

2015-05-15

Living in a multisensory world entails the continuous sensory processing of environmental information in order to enact appropriate motor routines. The interaction between our body and our brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical conditions dramatically affect the constant body-brain exchange, but the latest developments in biomedical engineering provide promising solutions for overcoming this communication breakdown. The ultimate technological developments succeeded in transforming neuronal electrical activity into computational input for robotic devices, giving birth to the era of the so-called brain-machine interfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain-machine interfaces into clinical protocols provided the technological solution for bypassing the neural disconnection and restore sensorimotor function. Based on these advances, the recovery of sensorimotor functionality is progressively becoming a concrete reality. However, despite the success of several recent techniques, some open issues still need to be addressed. Typical interventions for sensorimotor deficits include pharmaceutical treatments and manual/robotic assistance in passive movements. These procedures achieve symptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal cord injury or amputation). Here we review how state-of-the-art solutions in biomedical engineering are continuously increasing expectances in sensorimotor rehabilitation, as well as the current challenges especially with regards to the translation of the signals from brain-machine interfaces into sensory feedback and the incorporation of brain-machine interfaces into daily activities. Copyright © 2015 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian; Brightwell, Ronald B.; Grant, Ryan

This report presents a specification for the Portals 4 networ k programming interface. Portals 4 is intended to allow scalable, high-performance network communication betwee n nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded syste ms. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platfor ms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is tarmore » geted to the next generation of machines employing advanced network interface architectures that support enh anced offload capabilities.« less

State of the art in nuclear telerobotics: focus on the man/machine connection

NASA Astrophysics Data System (ADS)

Greaves, Amna E.

1995-12-01

The interface between the human controller and remotely operated device is a crux of telerobotic investigation today. This human-to-machine connection is the means by which we communicate our commands to the device, as well as the medium for decision-critical feedback to the operator. The amount of information transferred through the user interface is growing. This can be seen as a direct result of our need to support added complexities, as well as a rapidly expanding domain of applications. A user interface, or UI, is therefore subject to increasing demands to present information in a meaningful manner to the user. Virtual reality, and multi degree-of-freedom input devices lend us the ability to augment the man/machine interface, and handle burgeoning amounts of data in a more intuitive and anthropomorphically correct manner. Along with the aid of 3-D input and output devices, there are several visual tools that can be employed as part of a graphical UI that enhance and accelerate our comprehension of the data being presented. Thus an advanced UI that features these improvements would reduce the amount of fatigue on the teleoperator, increase his level of safety, facilitate learning, augment his control, and potentially reduce task time. This paper investigates the cutting edge concepts and enhancements that lead to the next generation of telerobotic interface systems.

DESIGN AND EVALUATION OF INDIVIDUAL ELEMENTS OF THE INTERFACE FOR AN AGRICULTURAL MACHINE.

PubMed

Rakhra, Aadesh K; Mann, Danny D

2018-01-29

If a user-centered approach is not used to design information displays, the quantity and quality of information presented to the user may not match the needs of the user, or it may exceed the capability of the human operator for processing and using that information. The result may be an excessive mental workload and reduced situation awareness of the operator, which can negatively affect the machine performance and operational outcomes. The increasing use of technology in agricultural machines may expose the human operator to excessive and undesirable information if the operator's information needs and information processing capabilities are ignored. In this study, a user-centered approach was used to design specific interface elements for an agricultural air seeder. Designs of the interface elements were evaluated in a laboratory environment by developing high-fidelity prototypes. Evaluations of the user interface elements yielded significant improvement in situation awareness (up to 11%; overall mean difference = 5.0 (4.8%), 95% CI (6.4728, 3.5939), p 0.0001). Mental workload was reduced by up to 19.7% (overall mean difference = -5.2 (-7.9%), n = 30, a = 0.05). Study participants rated the overall performance of the newly designed user-centered interface elements higher in comparison to the previous designs (overall mean difference = 27.3 (189.8%), 99% CI (35.150, 19.384), p 0.0001. Copyright© by the American Society of Agricultural Engineers.

Automated placement of interfaces in conformational kinetics calculations using machine learning

NASA Astrophysics Data System (ADS)

Grazioli, Gianmarc; Butts, Carter T.; Andricioaei, Ioan

2017-10-01

Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full feature space describing the dynamics of a macromolecule is divided into Voronoi (or other) cells, and the global kinetics of the molecular motions can be calculated from the set of fluxes through the interfaces between the cells. Although some methods of this type are exact for an arbitrary set of cells, in practice, the calculations will converge fastest when the interfaces are placed in regions where they can best capture transitions between configurations corresponding to local minima. The aim of this paper is to introduce a fully automated machine-learning algorithm for defining a set of cells for use in kinetic sampling methodologies based on subdividing the dynamical feature space; the algorithm requires no intuition about the system or input from the user and scales to high-dimensional systems.

Automated placement of interfaces in conformational kinetics calculations using machine learning.

PubMed

Grazioli, Gianmarc; Butts, Carter T; Andricioaei, Ioan

2017-10-21

Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full feature space describing the dynamics of a macromolecule is divided into Voronoi (or other) cells, and the global kinetics of the molecular motions can be calculated from the set of fluxes through the interfaces between the cells. Although some methods of this type are exact for an arbitrary set of cells, in practice, the calculations will converge fastest when the interfaces are placed in regions where they can best capture transitions between configurations corresponding to local minima. The aim of this paper is to introduce a fully automated machine-learning algorithm for defining a set of cells for use in kinetic sampling methodologies based on subdividing the dynamical feature space; the algorithm requires no intuition about the system or input from the user and scales to high-dimensional systems.

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Brian B; Lin, Yashen; Gevorgian, Vahan

From the inception of power systems, synchronous machines have acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, power electronics interfaces are playing a growing role as they are the primary interface for several types of renewable energy sources and storage technologies. As the role of power electronics in systems continues to grow, it is crucial to investigate the properties of bulk power systems in low inertia settings. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator,more » three-phase inverter, and a load. Furthermore, the inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings and, hence, differing levels of inertia. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the interaction between the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.« less

Semantics of User Interface for Image Retrieval: Possibility Theory and Learning Techniques.

ERIC Educational Resources Information Center

Crehange, M.; And Others

1989-01-01

Discusses the need for a rich semantics for the user interface in interactive image retrieval and presents two methods for building such interfaces: possibility theory applied to fuzzy data retrieval, and a machine learning technique applied to learning the user's deep need. Prototypes developed using videodisks and knowledge-based software are…

Highly efficient lithium composite anode with hydrophobic molten salt in seawater

NASA Astrophysics Data System (ADS)

Zhang, Yancheng; Urquidi-Macdonald, Mirna

A lithium composite anode (lithium/1-butyl-3-methyl-imidazoleum hexafluorophosphate (BMI +PF 6-)/4-VLZ) for primary lithium/seawater semi-fuel-cells is proposed to reduce lithium-water parasitic reaction and, hence, increase the lithium anodic efficiency up to 100%. The lithium composite anode was activated when in contact with artificial seawater (3% NaCl solution) and the output was a stable anodic current density at 0.2 mA/cm 2, which lasted about 10 h under potentiostatic polarization at +0.5 V versus open circuit potential (OCP); the anodic efficiency was indirectly measured to be 100%. With time, a small traces of water diffused through the hydrophobic molten salt, BMI +PF 6-, reached the lithium interface and formed a double layer film (LiH/LiOH). Accordingly, the current density decreased and the anodic efficiency was estimated to be 90%. The hypothesis of small traces of water penetrating the molten salt and reaching the lithium anode—after several hours of operation—is supported by the collected experimental current density and hydrogen evolution, electrochemical impedance spectrum analysis, and non-mechanistic interface film modeling of lithium/BMI +PF 6-.

A Formal Characterization of Relevant Information in Multi-Agent Systems

DTIC Science & Technology

2009-10-01

Conference iTrust. (2004) [17] Sadek, D.: Le dialogue homme-machine : de l’ ergonomie des interfaces à l’ agent intelligent dia- loguant. In: Nouvelles interfaces hommemachine, Lavoisier Editeur, Arago 18 (1996) 277–321

Charting the energy landscape of metal/organic interfaces via machine learning

NASA Astrophysics Data System (ADS)

Scherbela, Michael; Hörmann, Lukas; Jeindl, Andreas; Obersteiner, Veronika; Hofmann, Oliver T.

2018-04-01

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

Charting the energy landscape of metal/organic interfaces via machine learning

DOE PAGES

Scherbela, Michael; Hormann, Lukas; Jeindl, Andreas; ...

2018-04-17

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. Here in this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. Finally, we demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

Charting the energy landscape of metal/organic interfaces via machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scherbela, Michael; Hormann, Lukas; Jeindl, Andreas

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. Here in this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. Finally, we demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

Adaptive displays and controllers using alternative feedback.

PubMed

Repperger, D W

2004-12-01

Investigations on the design of haptic (force reflecting joystick or force display) controllers were conducted by viewing the display of force information within the context of several different paradigms. First, using analogies from electrical and mechanical systems, certain schemes of the haptic interface were hypothesized which may improve the human-machine interaction with respect to various criteria. A discussion is given on how this interaction benefits the electrical and mechanical system. To generalize this concept to the design of human-machine interfaces, three studies with haptic mechanisms were then synthesized and analyzed.

Developing Human-Machine Interfaces to Support Appropriate Trust and Reliance on Automated Combat Identification Systems (Developpement d’Interfaces Homme-Machine Pour Appuyer la Confiance dans les Systemes Automatises d’Identification au Combat)

DTIC Science & Technology

2008-03-31

on automation; the ‘response bias’ approach. This new approach is based on Signal Detection Theory (SDT) (Macmillan & Creelman , 1991; Wickens...SDT), response bias will vary with the expectation of the target probability, whereas their sensitivity will stay constant (Macmillan & Creelman ...measures, C has the simplest statistical properties (Macmillan & Creelman , 1991, p273), and it was also the measure used in Dzindolet et al.’s study

Man-machine interfaces in health care

NASA Technical Reports Server (NTRS)

Charles, Steve; Williams, Roy E.

1991-01-01

The surgeon, like the pilot, is confronted with an ever increasing volume of voice, data, and image input. Simultaneously, the surgeon must control a rapidly growing number of devices to deliver care to the patient. The broad disciplines of man-machine interface design, systems integration, and teleoperation will play a role in the operating room of the future. The purpose of this communication is to report the incorporation of these design concepts into new surgical and laser delivery systems. A review of each general problem area and the systems under development to solve the problems are presented.

Cutting Modeling of Hybrid CFRP/Ti Composite with Induced Damage Analysis

PubMed Central

Xu, Jinyang; El Mansori, Mohamed

2016-01-01

In hybrid carbon fiber reinforced polymer (CFRP)/Ti machining, the bi-material interface is the weakest region vulnerable to severe damage formation when the tool cutting from one phase to another phase and vice versa. The interface delamination as well as the composite-phase damage is the most serious failure dominating the bi-material machining. In this paper, an original finite element (FE) model was developed to inspect the key mechanisms governing the induced damage formation when cutting this multi-phase material. The hybrid composite model was constructed by establishing three disparate physical constituents, i.e., the Ti phase, the interface, and the CFRP phase. Different constitutive laws and damage criteria were implemented to build up the entire cutting behavior of the bi-material system. The developed orthogonal cutting (OC) model aims to characterize the dynamic mechanisms of interface delamination formation and the affected interface zone (AIZ). Special focus was made on the quantitative analyses of the parametric effects on the interface delamination and composite-phase damage. The numerical results highlighted the pivotal role of AIZ in affecting the formation of interface delamination, and the significant impacts of feed rate and cutting speed on delamination extent and fiber/matrix failure. PMID:28787824

VOTable JAVA Streaming Writer and Applications.

NASA Astrophysics Data System (ADS)

Kulkarni, P.; Kembhavi, A.; Kale, S.

2004-07-01

Virtual Observatory related tools use a new standard for data transfer called the VOTable format. This is a variant of the xml format that enables easy transfer of data over the web. We describe a streaming interface that can bridge the VOTable format, through a user friendly graphical interface, with the FITS and ASCII formats, which are commonly used by astronomers. A streaming interface is important for efficient use of memory because of the large size of catalogues. The tools are developed in JAVA to provide a platform independent interface. We have also developed a stand-alone version that can be used to convert data stored in ASCII or FITS format on a local machine. The Streaming writer is successfully being used in VOPlot (See Kale et al 2004 for a description of VOPlot).We present the test results of converting huge FITS and ASCII data into the VOTable format on machines that have only limited memory.

What Do We Really Need? Visions of an Ideal Human-Machine Interface for NOTES Mechatronic Support Systems From the View of Surgeons, Gastroenterologists, and Medical Engineers.

PubMed

Kranzfelder, Michael; Schneider, Armin; Fiolka, Adam; Koller, Sebastian; Wilhelm, Dirk; Reiser, Silvano; Meining, Alexander; Feussner, Hubertus

2015-08-01

To investigate why natural orifice translumenal endoscopic surgery (NOTES) has not yet become widely accepted and to prove whether the main reason is still the lack of appropriate platforms due to the deficiency of applicable interfaces. To assess expectations of a suitable interface design, we performed a survey on human-machine interfaces for NOTES mechatronic support systems among surgeons, gastroenterologists, and medical engineers. Of 120 distributed questionnaires, each consisting of 14 distinct questions, 100 (83%) were eligible for analysis. A mechatronic platform for NOTES was considered "important" by 71% of surgeons, 83% of gastroenterologist,s and 56% of medical engineers. "Intuitivity" and "simple to use" were the most favored aspects (33% to 51%). Haptic feedback was considered "important" by 70% of participants. In all, 53% of surgeons, 50% of gastroenterologists, and 33% of medical engineers already had experience with NOTES platforms or other surgical robots; however, current interfaces only met expectations in just more than 50%. Whereas surgeons did not favor a certain working posture, gastroenterologists and medical engineers preferred a sitting position. Three-dimensional visualization was generally considered "nice to have" (67% to 72%); however, for 26% of surgeons, 17% of gastroenterologists, and 7% of medical engineers it did not matter (P = 0.018). Requests and expectations of human-machine interfaces for NOTES seem to be generally similar for surgeons, gastroenterologist, and medical engineers. Consensus exists on the importance of developing interfaces that should be both intuitive and simple to use, are similar to preexisting familiar instruments, and exceed current available systems. © The Author(s) 2014.

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

Thermocouple and infrared sensor-based measurement of temperature distribution in metal cutting.

PubMed

Kus, Abdil; Isik, Yahya; Cakir, M Cemal; Coşkun, Salih; Özdemir, Kadir

2015-01-12

In metal cutting, the magnitude of the temperature at the tool-chip interface is a function of the cutting parameters. This temperature directly affects production; therefore, increased research on the role of cutting temperatures can lead to improved machining operations. In this study, tool temperature was estimated by simultaneous temperature measurement employing both a K-type thermocouple and an infrared radiation (IR) pyrometer to measure the tool-chip interface temperature. Due to the complexity of the machining processes, the integration of different measuring techniques was necessary in order to obtain consistent temperature data. The thermal analysis results were compared via the ANSYS finite element method. Experiments were carried out in dry machining using workpiece material of AISI 4140 alloy steel that was heat treated by an induction process to a hardness of 50 HRC. A PVD TiAlN-TiN-coated WNVG 080404-IC907 carbide insert was used during the turning process. The results showed that with increasing cutting speed, feed rate and depth of cut, the tool temperature increased; the cutting speed was found to be the most effective parameter in assessing the temperature rise. The heat distribution of the cutting tool, tool-chip interface and workpiece provided effective and useful data for the optimization of selected cutting parameters during orthogonal machining.

Thermocouple and Infrared Sensor-Based Measurement of Temperature Distribution in Metal Cutting

PubMed Central

Kus, Abdil; Isik, Yahya; Cakir, M. Cemal; Coşkun, Salih; Özdemir, Kadir

2015-01-01

In metal cutting, the magnitude of the temperature at the tool-chip interface is a function of the cutting parameters. This temperature directly affects production; therefore, increased research on the role of cutting temperatures can lead to improved machining operations. In this study, tool temperature was estimated by simultaneous temperature measurement employing both a K-type thermocouple and an infrared radiation (IR) pyrometer to measure the tool-chip interface temperature. Due to the complexity of the machining processes, the integration of different measuring techniques was necessary in order to obtain consistent temperature data. The thermal analysis results were compared via the ANSYS finite element method. Experiments were carried out in dry machining using workpiece material of AISI 4140 alloy steel that was heat treated by an induction process to a hardness of 50 HRC. A PVD TiAlN-TiN-coated WNVG 080404-IC907 carbide insert was used during the turning process. The results showed that with increasing cutting speed, feed rate and depth of cut, the tool temperature increased; the cutting speed was found to be the most effective parameter in assessing the temperature rise. The heat distribution of the cutting tool, tool-chip interface and workpiece provided effective and useful data for the optimization of selected cutting parameters during orthogonal machining. PMID:25587976

PyMT: A Python package for model-coupling in the Earth sciences

NASA Astrophysics Data System (ADS)

Hutton, E.

2016-12-01

The current landscape of Earth-system models is not only broad in scientific scope, but also broad in type. On the one hand, the large variety of models is exciting, as it provides fertile ground for extending or linking models together in novel ways to answer new scientific questions. However, the heterogeneity in model type acts to inhibit model coupling, model development, or even model use. Existing models are written in a variety of programming languages, operate on different grids, use their own file formats (both for input and output), have different user interfaces, have their own time steps, etc. Each of these factors become obstructions to scientists wanting to couple, extend - or simply run - existing models. For scientists whose main focus may not be computer science these barriers become even larger and become significant logistical hurdles. And this is all before the scientific difficulties of coupling or running models are addressed. The CSDMS Python Modeling Toolkit (PyMT) was developed to help non-computer scientists deal with these sorts of modeling logistics. PyMT is the fundamental package the Community Surface Dynamics Modeling System uses for the coupling of models that expose the Basic Modeling Interface (BMI). It contains: Tools necessary for coupling models of disparate time and space scales (including grid mappers) Time-steppers that coordinate the sequencing of coupled models Exchange of data between BMI-enabled models Wrappers that automatically load BMI-enabled models into the PyMT framework Utilities that support open-source interfaces (UGRID, SGRID,CSDMS Standard Names, etc.) A collection of community-submitted models, written in a variety of programminglanguages, from a variety of process domains - but all usable from within the Python programming language A plug-in framework for adding additional BMI-enabled models to the framework In this presentation we intoduce the basics of the PyMT as well as provide an example of coupling models of different domains and grid types.

Integration of an intelligent systems behavior simulator and a scalable soldier-machine interface

NASA Astrophysics Data System (ADS)

Johnson, Tony; Manteuffel, Chris; Brewster, Benjamin; Tierney, Terry

2007-04-01

As the Army's Future Combat Systems (FCS) introduce emerging technologies and new force structures to the battlefield, soldiers will increasingly face new challenges in workload management. The next generation warfighter will be responsible for effectively managing robotic assets in addition to performing other missions. Studies of future battlefield operational scenarios involving the use of automation, including the specification of existing and proposed technologies, will provide significant insight into potential problem areas regarding soldier workload. The US Army Tank Automotive Research, Development, and Engineering Center (TARDEC) is currently executing an Army technology objective program to analyze and evaluate the effect of automated technologies and their associated control devices with respect to soldier workload. The Human-Robotic Interface (HRI) Intelligent Systems Behavior Simulator (ISBS) is a human performance measurement simulation system that allows modelers to develop constructive simulations of military scenarios with various deployments of interface technologies in order to evaluate operator effectiveness. One such interface is TARDEC's Scalable Soldier-Machine Interface (SMI). The scalable SMI provides a configurable machine interface application that is capable of adapting to several hardware platforms by recognizing the physical space limitations of the display device. This paper describes the integration of the ISBS and Scalable SMI applications, which will ultimately benefit both systems. The ISBS will be able to use the Scalable SMI to visualize the behaviors of virtual soldiers performing HRI tasks, such as route planning, and the scalable SMI will benefit from stimuli provided by the ISBS simulation environment. The paper describes the background of each system and details of the system integration approach.

In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.

PubMed

Bonifazi, Paolo; Difato, Francesco; Massobrio, Paolo; Breschi, Gian L; Pasquale, Valentina; Levi, Timothée; Goldin, Miri; Bornat, Yannick; Tedesco, Mariateresa; Bisio, Marta; Kanner, Sivan; Galron, Ronit; Tessadori, Jacopo; Taverna, Stefano; Chiappalone, Michela

2013-01-01

Brain-machine interfaces (BMI) were born to control "actions from thoughts" in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI-a neuromorphic chip for brain repair-to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary "bottom-up" approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of "finite size networks" which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior

PubMed Central

Hammer, Jiri; Fischer, Jörg; Ruescher, Johanna; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2013-01-01

In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) “copy” of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance. PMID:24198757

Multiprocessor Z-Buffer Architecture for High-Speed, High Complexity Computer Image Generation.

DTIC Science & Technology

1983-12-01

Oversampling 50 17. "Poking Through" Effects 51 18. Sampling Paths 52 19. Triangle Variables 54 20. Intelligent Tiling Algorithm 61 21. Tiler Functional Blocks...64 * 22. HSD Interface 65 23. Tiling Machine Setup 67 24. Tiling Machine 68 25. Tile Accumulate 69 26. A lx$ Sorting Machine 77 27. A 2x8 Sorting...Delay 227 87. Effect of Triangle Size on Tiler Throughput Rates 229 88. Tiling Machine Setup Stage Performance for Oversample Mode 234 89. Tiling

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

A study of speech interfaces for the vehicle environment.

DOT National Transportation Integrated Search

2013-05-01

Over the past few years, there has been a shift in automotive human machine interfaces from : visual-manual interactions (pushing buttons and rotating knobs) to speech interaction. In terms of : distraction, the industry views speech interaction as a...

Man-machine interface issues in space telerobotics: A JPL research and development program

NASA Technical Reports Server (NTRS)

Bejczy, A. K.

1987-01-01

Technology issues related to the use of robots as man-extension or telerobot systems in space are discussed and exemplified. General considerations are presentd on control and information problems in space teleoperation and on the characteristics of Earth orbital teleoperation. The JPL R and D work in the area of man-machine interface devices and techniques for sensing and computer-based control is briefly summarized. The thrust of this R and D effort is to render space teleoperation efficient and safe through the use of devices and techniques which will permit integrated and task-level (intelligent) two-way control communication between human operator and telerobot machine in Earth orbit. Specific control and information display devices and techniques are discussed and exemplified with development results obtained at JPL in recent years.

Method and system for providing work machine multi-functional user interface

DOEpatents

Hoff, Brian D [Peoria, IL; Akasam, Sivaprasad [Peoria, IL; Baker, Thomas M [Peoria, IL

2007-07-10

A method is performed to provide a multi-functional user interface on a work machine for displaying suggested corrective action. The process includes receiving status information associated with the work machine and analyzing the status information to determine an abnormal condition. The process also includes displaying a warning message on the display device indicating the abnormal condition and determining one or more corrective actions to handle the abnormal condition. Further, the process includes determining an appropriate corrective action among the one or more corrective actions and displaying a recommendation message on the display device reflecting the appropriate corrective action. The process may also include displaying a list including the remaining one or more corrective actions on the display device to provide alternative actions to an operator.

The association between worksite physical environment and employee nutrition, and physical activity behavior and weight status

PubMed Central

Almeida, Fabio A.; Wall, Sarah S.; You, Wen; Harden, Samantha M.; Hill, Jennie L.; Krippendorf, Blake E.; Estabrooks, Paul A.

2014-01-01

Objective Explore the relationship between worksite physical environment and employee dietary intake, physical activity behavior, and weight status. Methods Two trained research assistants completed audits (Checklist of Health Promotion Environments at Worksites) at each worksite (n = 28). Employees (n = 6,261) completed a brief health survey prior to participation in a weight loss program. Results Employees’ access to outdoor areas was directly associated with lower BMI, while access to workout facilities within a worksite was associated with higher BMI. The presence of a cafeteria and fewer vending machines were directly associated with better eating habits. Better eating habits and meeting physical activity recommendations were both related to lower BMI. Conclusions Selected environmental factors in worksites were significantly associated with employee behaviors and weight status; providing additional intervention targets to change the worksite environment and promote employee weight loss. PMID:24988105

The association between worksite physical environment and employee nutrition, and physical activity behavior and weight status.

PubMed

Almeida, Fabio A; Wall, Sarah S; You, Wen; Harden, Samantha M; Hill, Jennie L; Krippendorf, Blake E; Estabrooks, Paul A

2014-07-01

To explore the relationship between worksite physical environment and employee dietary intake, physical activity behavior, and weight status. Two trained research assistants completed audits (Checklist of Health Promotion Environments at Worksites) at each worksite (n = 28). Employees (n = 6261) completed a brief health survey before participation in a weight loss program. Employees' access to outdoor areas was directly associated with lower body mass index (BMI), whereas access to workout facilities within a worksite was associated with higher BMI. The presence of a cafeteria and fewer vending machines was directly associated with better eating habits. Better eating habits and meeting physical activity recommendations were both related to lower BMI. Selected environmental factors in worksites were significantly associated with employee behaviors and weight status, providing additional intervention targets to change the worksite environment and promote employee weight loss.

Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

PubMed

Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

2018-01-10

Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders.

PubMed

Mahmoudi, Babak; Principe, Jose C; Sanchez, Justin C

2010-01-01

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.

ODC-Free Solvent Implementation for Phenolics Cleaning

NASA Technical Reports Server (NTRS)

Wurth, Laura; Biegert, Lydia; Lamont, DT; McCool, Alex (Technical Monitor)

2001-01-01

During phenolic liner manufacture, resin-impregnated (pre-preg) bias tape of silica, glass, or carbon cloth is tape-wrapped, cured, machined, and then wiped with 1,1,1 tri-chloroethane (TCA) to remove contaminants that may have been introduced during machining and handling. Following the TCA wipe, the machined surface is given a resin wet-coat and over-wrapped with more prepreg and cured. A TCA replacement solvent for these wiping operations must effectively remove both surface contaminants, and sub-surface oils and greases while not compromising the integrity of this interface. Selection of a TCA replacement solvent for phenolic over-wrap interface cleaning began with sub-scale compatibility tests with cured phenolics. Additional compatibility tests included assessment of solvent retention in machined phenolic surfaces. Results from these tests showed that, while the candidate solvent did not degrade the cured phenolics, it was retained in higher concentrations than TCA in phenolic surfaces. This effect was most pronounced with glass and silica cloth phenolics with steep ply angles relative to the wiped surfaces.

Concurrent Image Processing Executive (CIPE)

NASA Technical Reports Server (NTRS)

Lee, Meemong; Cooper, Gregory T.; Groom, Steven L.; Mazer, Alan S.; Williams, Winifred I.

1988-01-01

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are discussed. The target machine for this software is a JPL/Caltech Mark IIIfp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules; (1) user interface, (2) host-resident executive, (3) hypercube-resident executive, and (4) application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube a data management method which distributes, redistributes, and tracks data set information was implemented.

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements

NASA Astrophysics Data System (ADS)

Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

2017-02-01

Objective. Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Approach. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Main results. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81  ±  0.09, 0.85  ±  0.09, and 0.76  ±  0.13, respectively) and the patients (e.g. 0.91  ±  0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors  <6°. An upper cervical SCI patient, empowered by the exoskeleton, successfully carried a ball to a goal in all 10 trials. Significance. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements.

PubMed

Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

2017-02-01

Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81  ±  0.09, 0.85  ±  0.09, and 0.76  ±  0.13, respectively) and the patients (e.g. 0.91  ±  0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors  <6°. An upper cervical SCI patient, empowered by the exoskeleton, successfully carried a ball to a goal in all 10 trials. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

Considerations for human-machine interfaces in tele-operations

NASA Technical Reports Server (NTRS)

Newport, Curt

1991-01-01

Numerous factors impact on the efficiency of tele-operative manipulative work. Generally, these are related to the physical environment of the tele-operator and how he interfaces with robotic control consoles. The capabilities of the operator can be influenced by considerations such as temperature, eye strain, body fatigue, and boredom created by repetitive work tasks. In addition, the successful combination of man and machine will, in part, be determined by the configuration of the visual and physical interfaces available to the teleoperator. The design and operation of system components such as full-scale and mini-master manipulator controllers, servo joysticks, and video monitors will have a direct impact on operational efficiency. As a result, the local environment and the interaction of the operator with the robotic control console have a substantial effect on mission productivity.

Pellet to Part Manufacturing System for CNCs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roschli, Alex C.; Love, Lonnie J.; Post, Brian K.

Oak Ridge National Laboratory’s Manufacturing Demonstration Facility worked with Hybrid Manufacturing Technologies to develop a compact prototype composite additive manufacturing head that can effectively extrude injection molding pellets. The head interfaces with conventional CNC machine tools enabling rapid conversion of conventional machine tools to additive manufacturing tools. The intent was to enable wider adoption of Big Area Additive Manufacturing (BAAM) technology and combine BAAM technology with conventional machining systems.

Biosleeve Human-Machine Interface

NASA Technical Reports Server (NTRS)

Assad, Christopher (Inventor)

2016-01-01

Systems and methods for sensing human muscle action and gestures in order to control machines or robotic devices are disclosed. One exemplary system employs a tight fitting sleeve worn on a user arm and including a plurality of electromyography (EMG) sensors and at least one inertial measurement unit (IMU). Power, signal processing, and communications electronics may be built into the sleeve and control data may be transmitted wirelessly to the controlled machine or robotic device.

The Python Spectral Analysis Tool (PySAT) for Powerful, Flexible, and Easy Preprocessing and Machine Learning with Point Spectral Data

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S. M.; Graff, T.; Morris, R. V.; Laura, J.

2018-04-01

The PySAT point spectra tool provides a flexible graphical interface, enabling scientists to apply a wide variety of preprocessing and machine learning methods to point spectral data, with an emphasis on multivariate regression.

Development of sensitized pick coal interface detector system

NASA Technical Reports Server (NTRS)

Burchill, R. F.

1979-01-01

One approach for detection of the coal interface is measurement of the pick cutting hoads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telementry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder.

Enhanced/Operator Machine Interface Phase I

DTIC Science & Technology

1997-12-22

investigation proposes an adaptive OMI technique using a cognitive task analysis (CTA) approach derived from research by several experts in the Cognitive...Science field. The research reveals that adaptive interfaces have not been widely implemented due to the difficulty of the cognitive task analysis . Moreover

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces

PubMed Central

Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon

2018-01-01

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas. PMID:29364861

A machine for the preliminary investigation of design features influencing the wear behaviour of knee prostheses.

PubMed

McGloughlin, T M; Murphy, D M; Kavanagh, A G

2004-01-01

Degradation of tibial inserts in vivo has been found to be multifactorial in nature, resulting in a complex interaction of many variables. A range of kinematic conditions occurs at the tibio-femoral interface, giving rise to various degrees of rolling and sliding at this interface. The movement of the tibio-femoral contact point may be an influential factor in the overall wear of ultra-high molecular weight polyethylene (UHMWPE) tibial components. As part of this study a three-station wear-test machine was designed and built to investigate the influence of rolling and sliding on the wear behaviour of specific design aspects of contemporary knee prostheses. Using the machine, it is possible to monitor the effect of various slide roll ratios on the performance of contemporary bearing designs from a geometrical and materials perspective.

Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces.

PubMed

Herbert, Robert; Kim, Jong-Hoon; Kim, Yun Soung; Lee, Hye Moon; Yeo, Woon-Hong

2018-01-24

Flexible hybrid electronics (FHE), designed in wearable and implantable configurations, have enormous applications in advanced healthcare, rapid disease diagnostics, and persistent human-machine interfaces. Soft, contoured geometries and time-dynamic deformation of the targeted tissues require high flexibility and stretchability of the integrated bioelectronics. Recent progress in developing and engineering soft materials has provided a unique opportunity to design various types of mechanically compliant and deformable systems. Here, we summarize the required properties of soft materials and their characteristics for configuring sensing and substrate components in wearable and implantable devices and systems. Details of functionality and sensitivity of the recently developed FHE are discussed with the application areas in medicine, healthcare, and machine interactions. This review concludes with a discussion on limitations of current materials, key requirements for next generation materials, and new application areas.

Research on ARM Numerical Control System

NASA Astrophysics Data System (ADS)

Wei, Xu; JiHong, Chen

Computerized Numerical Control (CNC) machine tools is the foundation of modern manufacturing systems, whose advanced digital technology is the key to solve the problem of sustainable development of machine tool manufacturing industry. The paper is to design CNC system embedded on ARM and indicates the hardware design and the software systems supported. On the hardware side: the driving chip of the motor control unit, as the core of components, is MCX314AL of DSP motion control which is developed by NOVA Electronics Co., Ltd. of Japan. It make convenient to control machine because of its excellent performance, simple interface, easy programming. On the Software side, the uC/OS-2 is selected as the embedded operating system of the open source, which makes a detailed breakdown of the modules of the CNC system. Those priorities are designed according to their actual requirements. The ways of communication between the module and the interrupt response are so different that it guarantees real-time property and reliability of the numerical control system. Therefore, it not only meets the requirements of the current social precision machining, but has good man-machine interface and network support to facilitate a variety of craftsmen use.

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

NASA Astrophysics Data System (ADS)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

Software architecture for time-constrained machine vision applications

NASA Astrophysics Data System (ADS)

Usamentiaga, Rubén; Molleda, Julio; García, Daniel F.; Bulnes, Francisco G.

2013-01-01

Real-time image and video processing applications require skilled architects, and recent trends in the hardware platform make the design and implementation of these applications increasingly complex. Many frameworks and libraries have been proposed or commercialized to simplify the design and tuning of real-time image processing applications. However, they tend to lack flexibility, because they are normally oriented toward particular types of applications, or they impose specific data processing models such as the pipeline. Other issues include large memory footprints, difficulty for reuse, and inefficient execution on multicore processors. We present a novel software architecture for time-constrained machine vision applications that addresses these issues. The architecture is divided into three layers. The platform abstraction layer provides a high-level application programming interface for the rest of the architecture. The messaging layer provides a message-passing interface based on a dynamic publish/subscribe pattern. A topic-based filtering in which messages are published to topics is used to route the messages from the publishers to the subscribers interested in a particular type of message. The application layer provides a repository for reusable application modules designed for machine vision applications. These modules, which include acquisition, visualization, communication, user interface, and data processing, take advantage of the power of well-known libraries such as OpenCV, Intel IPP, or CUDA. Finally, the proposed architecture is applied to a real machine vision application: a jam detector for steel pickling lines.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Design of conveyor type machine with numerical control for manufacturing of extrusion thermoplastic thread

NASA Astrophysics Data System (ADS)

Gorbunova, T. N.; Koltunov, I. I.; Tumanova, M. B.

2018-05-01

The article is devoted to the development of a model and control program for a 3D printer working based on extrusion technology. The article contains descriptions of all components of the machine and blocks of the interface of the control program.

FSW of Aluminum Tailor Welded Blanks across Machine Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hovanski, Yuri; Upadhyay, Piyush; Carlson, Blair

2015-02-16

Development and characterization of friction stir welded aluminum tailor welded blanks was successfully carried out on three separate machine platforms. Each was a commercially available, gantry style, multi-axis machine designed specifically for friction stir welding. Weld parameters were developed to support high volume production of dissimilar thickness aluminum tailor welded blanks at speeds of 3 m/min and greater. Parameters originally developed on an ultra-high stiffness servo driven machine where first transferred to a high stiffness servo-hydraulic friction stir welding machine, and subsequently transferred to a purpose built machine designed to accommodate thin sheet aluminum welding. The inherent beam stiffness, bearingmore » compliance, and control system for each machine were distinctly unique, which posed specific challenges in transferring welding parameters across machine platforms. This work documents the challenges imposed by successfully transferring weld parameters from machine to machine, produced from different manufacturers and with unique control systems and interfaces.« less

An operator interface design for a telerobotic inspection system

NASA Technical Reports Server (NTRS)

Kim, Won S.; Tso, Kam S.; Hayati, Samad

1993-01-01

The operator interface has recently emerged as an important element for efficient and safe interactions between human operators and telerobotics. Advances in graphical user interface and graphics technologies enable us to produce very efficient operator interface designs. This paper describes an efficient graphical operator interface design newly developed for remote surface inspection at NASA-JPL. The interface, designed so that remote surface inspection can be performed by a single operator with an integrated robot control and image inspection capability, supports three inspection strategies of teleoperated human visual inspection, human visual inspection with automated scanning, and machine-vision-based automated inspection.

The reported incidence of man-machine interface issues in Army aviators using the Aviator's Night Vision System (ANVIS) in a combat theatre

NASA Astrophysics Data System (ADS)

Hiatt, Keith L.; Rash, Clarence E.

2011-06-01

Background: Army Aviators rely on the ANVIS for night operations. Human factors literature notes that the ANVIS man-machine interface results in reports of visual and spinal complaints. This is the first study that has looked at these issues in the much harsher combat environment. Last year, the authors reported on the statistically significant (p<0.01) increased complaints of visual discomfort, degraded visual cues, and incidence of static and dynamic visual illusions in the combat environment [Proc. SPIE, Vol. 7688, 76880G (2010)]. In this paper we present the findings regarding increased spinal complaints and other man-machine interface issues found in the combat environment. Methods: A survey was administered to Aircrew deployed in support of Operation Enduring Freedom (OEF). Results: 82 Aircrew (representing an aggregate of >89,000 flight hours of which >22,000 were with ANVIS) participated. Analysis demonstrated high complaints of almost all levels of back and neck pain. Additionally, the use of body armor and other Aviation Life Support Equipment (ALSE) caused significant ergonomic complaints when used with ANVIS. Conclusions: ANVIS use in a combat environment resulted in higher and different types of reports of spinal symptoms and other man-machine interface issues over what was previously reported. Data from this study may be more operationally relevant than that of the peacetime literature as it is derived from actual combat and not from training flights, and it may have important implications about making combat predictions based on performance in training scenarios. Notably, Aircrew remarked that they could not execute the mission without ANVIS and ALSE and accepted the degraded ergonomic environment.

Coupling of snow and permafrost processes using the Basic Modeling Interface (BMI)

NASA Astrophysics Data System (ADS)

Wang, K.; Overeem, I.; Jafarov, E. E.; Piper, M.; Stewart, S.; Clow, G. D.; Schaefer, K. M.

2017-12-01

We developed a permafrost modeling tool based by implementing the Kudryavtsev empirical permafrost active layer depth model (the so-called "Ku" component). The model is specifically set up to have a basic model interface (BMI), which enhances the potential coupling to other earth surface processes model components. This model is accessible through the Web Modeling Tool in Community Surface Dynamics Modeling System (CSDMS). The Kudryavtsev model has been applied for entire Alaska to model permafrost distribution at high spatial resolution and model predictions have been verified by Circumpolar Active Layer Monitoring (CALM) in-situ observations. The Ku component uses monthly meteorological forcing, including air temperature, snow depth, and snow density, and predicts active layer thickness (ALT) and temperature on the top of permafrost (TTOP), which are important factors in snow-hydrological processes. BMI provides an easy approach to couple the models with each other. Here, we provide a case of coupling the Ku component to snow process components, including the Snow-Degree-Day (SDD) method and Snow-Energy-Balance (SEB) method, which are existing components in the hydrological model TOPOFLOW. The work flow is (1) get variables from meteorology component, set the values to snow process component, and advance the snow process component, (2) get variables from meteorology and snow component, provide these to the Ku component and advance, (3) get variables from snow process component, set the values to meteorology component, and advance the meteorology component. The next phase is to couple the permafrost component with fully BMI-compliant TOPOFLOW hydrological model, which could provide a useful tool to investigate the permafrost hydrological effect.

A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.

PubMed

Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

2013-01-01

Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology. Copyright © 2013 Elsevier Inc. All rights reserved.

Film riding seals for rotary machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bidkar, Rahul Anil; Sarawate, Neelesh Nandkumar; Wolfe, Christopher Edward

A seal assembly for a rotary machine is provided. The seal assembly includes multiple sealing device segments disposed circumferentially intermediate to a stationary housing and a rotor. Each of the segments includes a shoe plate with a forward-shoe section and an aft-shoe section having one or more labyrinth teeth therebetween facing the rotor. The sealing device includes a stator interface element having a groove or slot for allowing disposal of a spline seal for preventing segment leakages. The sealing device segment also includes multiple bellow springs or flexures connected to the shoe plate and to the stator interface element. Further,more » the sealing device segments include a secondary seal integrated with the stator interface element at one end and positioned about the multiple bellow springs or flexures and the shoe plate at the other end.« less

[Human machines--mechanical humans? The industrial arrangement of the relation between human being and machine on the basis of psychotechnik and Georg Schlesingers work with disabled soldiers].

PubMed

Patzel-Mattern, Katja

2005-01-01

The 20th Century is the century of of technical artefacts. With their existance and use they create an artificial reality, within which humans have to position themselves. Psychotechnik is an attempt to enable humans for this positioning. It gained importance in Germany after World War I and had its heyday between 1919 and 1926. On the basis of the activity of the engineer and supporter of Psychotechnik Georg Schlesinger, whose particular interest were disabled soldiers, the essay on hand will investigate the understanding of the body and the human being of Psychotechnik as an applied science. It turned out, that the biggest achievement of Psychotechnik was to establish a new view of the relation between human being and machine. Thus it helped to show that the human-machine-interface is a shapable unit. Psychotechnik sees the human body and its physique as the last instance for the design of machines. Its main concern is to optimize the relation between human being and machine rather than to standardize human beings according to the construction of machines. After her splendid rise during the Weimar Republic and her rapid decline since the late 1920s Psychotechnik nowadays gains scientifical attention as a historical phenomenon. The main attention in the current discourse lies on the aspects conserning philosophy of science: the unity of body and soul, the understanding of the human-machine-interface as a shapable unit and the human being as a last instance of this unit.

Digital Systems Validation Handbook. Volume 2. Chapter 19. Pilot - Vehicle Interface

DTIC Science & Technology

1993-11-01

checklists, and other status messages. Voice interactive systems are defi-ed as "the interface between a cooperative human and a machine, which involv -he...Pilot-Vehicle Interface 19-85 5.6.1 Crew Interaction and the Cockpit 19-85 5.6.2 Crew Resource Management and Safety 19-87 5.6.3 Pilot and Crew Training...systems was a "stand-alone" component performing its intended function. Systems and their cockpit interfaces were added as technological advances were

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.

Sustainable cooling method for machining titanium alloy

NASA Astrophysics Data System (ADS)

Boswell, B.; Islam, M. N.

2016-02-01

Hard to machine materials such as Titanium Alloy TI-6AI-4V Grade 5 are notoriously known to generate high temperatures and adverse reactions between the workpiece and the tool tip materials. These conditions all contribute to an increase in the wear mechanisms, reducing tool life. Titanium Alloy, for example always requires coolant to be used during machining. However, traditional flood cooling needs to be replaced due to environmental issues, and an alternative cooling method found that has minimum impact on the environment. For true sustainable cooling of the tool it is necessary to account for all energy used in the cooling process, including the energy involved in producing the coolant. Previous research has established that efficient cooling of the tool interface improves the tool life and cutting action. The objective of this research is to determine the most appropriate sustainable cooling method that can also reduce the rate of wear at the tool interface.

Human factors in space telepresence

NASA Technical Reports Server (NTRS)

Akin, D. L.; Howard, R. D.; Oliveria, J. S.

1983-01-01

The problems of interfacing a human with a teleoperation system, for work in space are discussed. Much of the information presented here is the result of experience gained by the M.I.T. Space Systems Laboratory during the past two years of work on the ARAMIS (Automation, Robotics, and Machine Intelligence Systems) project. Many factors impact the design of the man-machine interface for a teleoperator. The effects of each are described in turn. An annotated bibliography gives the key references that were used. No conclusions are presented as a best design, since much depends on the particular application desired, and the relevant technology is swiftly changing.

PubMed Central

Solazzi, Massimiliano; Loconsole, Claudio; Barsotti, Michele

2016-01-01

This paper illustrates the application of emerging technologies and human-machine interfaces to the neurorehabilitation and motor assistance fields. The contribution focuses on wearable technologies and in particular on robotic exoskeleton as tools for increasing freedom to move and performing Activities of Daily Living (ADLs). This would result in a deep improvement in quality of life, also in terms of improved function of internal organs and general health status. Furthermore, the integration of these robotic systems with advanced bio-signal driven human-machine interface can increase the degree of participation of patient in robotic training allowing to recognize user's intention and assisting the patient in rehabilitation tasks, thus representing a fundamental aspect to elicit motor learning PMID:28484314

Teleoperator system man-machine interface requirements for satellite retrieval and satellite servicing. Volume 1: Requirements

NASA Technical Reports Server (NTRS)

Malone, T. B.

1972-01-01

Requirements were determined analytically for the man machine interface for a teleoperator system performing on-orbit satellite retrieval and servicing. Requirements are basically of two types; mission/system requirements, and design requirements or design criteria. Two types of teleoperator systems were considered: a free flying vehicle, and a shuttle attached manipulator. No attempt was made to evaluate the relative effectiveness or efficiency of the two system concepts. The methodology used entailed an application of the Essex Man-Systems analysis technique as well as a complete familiarization with relevant work being performed at government agencies and by private industry.

Spikes, Local Field Potentials, and Electrocorticogram Characterization during Motor Learning in Rats for Brain Machine Interface Tasks.

PubMed

Marzullo, T C; Dudley, J R; Miller, C R; Trejo, L; Kipke, D R

2005-01-01

Brain machine interface development typically falls into two arenas, invasive extracellular recording and non-invasive electroencephalogram recording methods. The relationship between action potentials and field potentials is not well understood, and investigation of interrelationships may improve design of neuroprosthetic control systems. Rats were trained on a motor learning task whereby they had to insert their noses into an aperture while simultaneously pressing down on levers with their forepaws; spikes, local field potentials (LFPs), and electrocorticograms (ECoGs) over the motor cortex were recorded and characterized. Preliminary results suggest that the LFP activity in lower cortical layers oscillates with the ECoG.

Quadcopter control using a BCI

NASA Astrophysics Data System (ADS)

Rosca, S.; Leba, M.; Ionica, A.; Gamulescu, O.

2018-01-01

The paper presents how there can be interconnected two ubiquitous elements nowadays. On one hand, the drones, which are increasingly present and integrated into more and more fields of activity, beyond the military applications they come from, moving towards entertainment, real-estate, delivery and so on. On the other hand, unconventional man-machine interfaces, which are generous topics to explore now and in the future. Of these, we chose brain computer interface (BCI), which allows human-machine interaction without requiring any moving elements. The research consists of mathematical modeling and numerical simulation of a drone and a BCI. Then there is presented an application using a Parrot mini-drone and an Emotiv Insight BCI.

Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP.

PubMed

Deng, Li; Wang, Guohua; Chen, Bo

2015-01-01

In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

PubMed

Krepkovich, Eileen T; Perreault, Eric J

2008-01-01

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.

Operating Comfort Prediction Model of Human-Machine Interface Layout for Cabin Based on GEP

PubMed Central

Wang, Guohua; Chen, Bo

2015-01-01

In view of the evaluation and decision-making problem of human-machine interface layout design for cabin, the operating comfort prediction model is proposed based on GEP (Gene Expression Programming), using operating comfort to evaluate layout scheme. Through joint angles to describe operating posture of upper limb, the joint angles are taken as independent variables to establish the comfort model of operating posture. Factor analysis is adopted to decrease the variable dimension; the model's input variables are reduced from 16 joint angles to 4 comfort impact factors, and the output variable is operating comfort score. The Chinese virtual human body model is built by CATIA software, which will be used to simulate and evaluate the operators' operating comfort. With 22 groups of evaluation data as training sample and validation sample, GEP algorithm is used to obtain the best fitting function between the joint angles and the operating comfort; then, operating comfort can be predicted quantitatively. The operating comfort prediction result of human-machine interface layout of driller control room shows that operating comfort prediction model based on GEP is fast and efficient, it has good prediction effect, and it can improve the design efficiency. PMID:26448740

Review of "Conceptual Structures: Information Processing in Mind and Machine."

ERIC Educational Resources Information Center

Smoliar, Stephen W.

This review of the book, "Conceptual Structures: Information Processing in Mind and Machine," by John F. Sowa, argues that anyone who plans to get involved with issues of knowledge representation should have at least a passing acquaintance with Sowa's conceptual graphs for a database interface. (Used to model the underlying semantics of…

Gesture-controlled interfaces for self-service machines and other applications

NASA Technical Reports Server (NTRS)

Cohen, Charles J. (Inventor); Jacobus, Charles J. (Inventor); Paul, George (Inventor); Beach, Glenn (Inventor); Foulk, Gene (Inventor); Obermark, Jay (Inventor); Cavell, Brook (Inventor)

2004-01-01

A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.

Performance Comparison of a Matrix Solver on a Heterogeneous Network Using Two Implementations of MPI: MPICH and LAM

NASA Technical Reports Server (NTRS)

Phillips, Jennifer K.

1995-01-01

Two of the current and most popular implementations of the Message-Passing Standard, Message Passing Interface (MPI), were contrasted: MPICH by Argonne National Laboratory, and LAM by the Ohio Supercomputer Center at Ohio State University. A parallel skyline matrix solver was adapted to be run in a heterogeneous environment using MPI. The Message-Passing Interface Forum was held in May 1994 which lead to a specification of library functions that implement the message-passing model of parallel communication. LAM, which creates it's own environment, is more robust in a highly heterogeneous network. MPICH uses the environment native to the machine architecture. While neither of these free-ware implementations provides the performance of native message-passing or vendor's implementations, MPICH begins to approach that performance on the SP-2. The machines used in this study were: IBM RS6000, 3 Sun4, SGI, and the IBM SP-2. Each machine is unique and a few machines required specific modifications during the installation. When installed correctly, both implementations worked well with only minor problems.

Intelligent Adaptive Interface: A Design Tool for Enhancing Human-Machine System Performances

DTIC Science & Technology

2009-10-01

and customizable. Thus, an intelligent interface should tailor its parameters to certain prescribed specifications or convert itself and adjust to...Computer Interaction 3(2): 87-122. [51] Schereiber, G., Akkermans, H., Anjewierden, A., de Hoog , R., Shadbolt, N., Van de Velde, W., & Wielinga, W

Goal Tracking in a Natural Language Interface: Towards Achieving Adjustable Autonomy

DTIC Science & Technology

1999-01-01

communication , we believe that human/machine interfaces that share some of the characteristics of human- human communication can be friendlier and easier...natural means of communicating with a mobile robot. Although we are not claiming that communication with robotic agents must be patterned after human

A natural language interface to databases

NASA Technical Reports Server (NTRS)

Ford, D. R.

1988-01-01

The development of a Natural Language Interface which is semantic-based and uses Conceptual Dependency representation is presented. The system was developed using Lisp and currently runs on a Symbolics Lisp machine. A key point is that the parser handles morphological analysis, which expands its capabilities of understanding more words.

Toward FRP-Based Brain-Machine Interfaces—Single-Trial Classification of Fixation-Related Potentials

PubMed Central

Finke, Andrea; Essig, Kai; Marchioro, Giuseppe; Ritter, Helge

2016-01-01

The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant’s body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction. PMID:26812487

Cooperative processing user interfaces for AdaNET

NASA Technical Reports Server (NTRS)

Gutzmann, Kurt M.

1991-01-01

A cooperative processing user interface (CUI) system shares the task of graphical display generation and presentation between the user's computer and a remote host. The communications link between the two computers is typically a modem or Ethernet. The two main purposes of a CUI are reduction of the amount of data transmitted between user and host machines, and provision of a graphical user interface system to make the system easier to use.

A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control.

PubMed

Ma, Jiaxin; Zhang, Yu; Cichocki, Andrzej; Matsuno, Fumitoshi

2015-03-01

This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interface works in two modes: an EOG mode recognizes eye movements such as blinks, and an EEG mode detects event related potentials (ERPs) like P300. While both eye movements and ERPs have been separately used for implementing assistive interfaces, which help patients with motor disabilities in performing daily tasks, the proposed hybrid interface integrates them together. In this way, both the eye movements and ERPs complement each other. Therefore, it can provide a better efficiency and a wider scope of application. In this study, we design a threshold algorithm that can recognize four kinds of eye movements including blink, wink, gaze, and frown. In addition, an oddball paradigm with stimuli of inverted faces is used to evoke multiple ERP components including P300, N170, and VPP. To verify the effectiveness of the proposed system, two different online experiments are carried out. One is to control a multifunctional humanoid robot, and the other is to control four mobile robots. In both experiments, the subjects can complete tasks effectively by using the proposed interface, whereas the best completion time is relatively short and very close to the one operated by hand.

ClearTK 2.0: Design Patterns for Machine Learning in UIMA

PubMed Central

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework. PMID:29104966

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

PubMed

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

ODISEES: A New Paradigm in Data Access

NASA Astrophysics Data System (ADS)

Huffer, E.; Little, M. M.; Kusterer, J.

2013-12-01

As part of its ongoing efforts to improve access to data, the Atmospheric Science Data Center has developed a high-precision Earth Science domain ontology (the 'ES Ontology') implemented in a graph database ('the Semantic Metadata Repository') that is used to store detailed, semantically-enhanced, parameter-level metadata for ASDC data products. The ES Ontology provides the semantic infrastructure needed to drive the ASDC's Ontology-Driven Interactive Search Environment for Earth Science ('ODISEES'), a data discovery and access tool, and will support additional data services such as analytics and visualization. The ES ontology is designed on the premise that naming conventions alone are not adequate to provide the information needed by prospective data consumers to assess the suitability of a given dataset for their research requirements; nor are current metadata conventions adequate to support seamless machine-to-machine interactions between file servers and end-user applications. Data consumers need information not only about what two data elements have in common, but also about how they are different. End-user applications need consistent, detailed metadata to support real-time data interoperability. The ES ontology is a highly precise, bottom-up, queriable model of the Earth Science domain that focuses on critical details about the measurable phenomena, instrument techniques, data processing methods, and data file structures. Earth Science parameters are described in detail in the ES Ontology and mapped to the corresponding variables that occur in ASDC datasets. Variables are in turn mapped to well-annotated representations of the datasets that they occur in, the instrument(s) used to create them, the instrument platforms, the processing methods, etc., creating a linked-data structure that allows both human and machine users to access a wealth of information critical to understanding and manipulating the data. The mappings are recorded in the Semantic Metadata Repository as RDF-triples. An off-the-shelf Ontology Development Environment and a custom Metadata Conversion Tool comprise a human-machine/machine-machine hybrid tool that partially automates the creation of metadata as RDF-triples by interfacing with existing metadata repositories and providing a user interface that solicits input from a human user, when needed. RDF-triples are pushed to the Ontology Development Environment, where a reasoning engine executes a series of inference rules whose antecedent conditions can be satisfied by the initial set of RDF-triples, thereby generating the additional detailed metadata that is missing in existing repositories. A SPARQL Endpoint, a web-based query service and a Graphical User Interface allow prospective data consumers - even those with no familiarity with NASA data products - to search the metadata repository to find and order data products that meet their exact specifications. A web-based API will provide an interface for machine-to-machine transactions.

Using machine learning to emulate human hearing for predictive maintenance of equipment

NASA Astrophysics Data System (ADS)

Verma, Dinesh; Bent, Graham

2017-05-01

At the current time, interfaces between humans and machines use only a limited subset of senses that humans are capable of. The interaction among humans and computers can become much more intuitive and effective if we are able to use more senses, and create other modes of communicating between them. New machine learning technologies can make this type of interaction become a reality. In this paper, we present a framework for a holistic communication between humans and machines that uses all of the senses, and discuss how a subset of this capability can allow machines to talk to humans to indicate their health for various tasks such as predictive maintenance.

Diamond turning machine controller implementation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garrard, K.P.; Taylor, L.W.; Knight, B.F.

The standard controller for a Pnuemo ASG 2500 Diamond Turning Machine, an Allen Bradley 8200, has been replaced with a custom high-performance design. This controller consists of four major components. Axis position feedback information is provided by a Zygo Axiom 2/20 laser interferometer with 0.1 micro-inch resolution. Hardware interface logic couples the computers digital and analog I/O channels to the diamond turning machine`s analog motor controllers, the laser interferometer, and other machine status and control information. It also provides front panel switches for operator override of the computer controller and implement the emergency stop sequence. The remaining two components, themore » control computer hardware and software, are discussed in detail below.« less

Interfacing insect brain for space applications.

PubMed

Di Pino, Giovanni; Seidl, Tobias; Benvenuto, Antonella; Sergi, Fabrizio; Campolo, Domenico; Accoto, Dino; Maria Rossini, Paolo; Guglielmelli, Eugenio

2009-01-01

Insects exhibit remarkable navigation capabilities that current control architectures are still far from successfully mimic and reproduce. In this chapter, we present the results of a study on conceptualizing insect/machine hybrid controllers for improving autonomy of exploratory vehicles. First, the different principally possible levels of interfacing between insect and machine are examined followed by a review of current approaches towards hybridity and enabling technologies. Based on the insights of this activity, we propose a double hybrid control architecture which hinges around the concept of "insect-in-a-cockpit." It integrates both biological/artificial (insect/robot) modules and deliberative/reactive behavior. The basic assumption is that "low-level" tasks are managed by the robot, while the "insect intelligence" is exploited whenever high-level problem solving and decision making is required. Both neural and natural interfacing have been considered to achieve robustness and redundancy of exchanged information.

Man-machine interfaces in LACIE/ERIPS

NASA Technical Reports Server (NTRS)

Duprey, B. B. (Principal Investigator)

1979-01-01

One of the most important aspects of the interactive portion of the LACIE/ERIPS software system is the way in which the analysis and decision-making capabilities of a human being are integrated with the speed and accuracy of a computer to produce a powerful analysis system. The three major man-machine interfaces in the system are (1) the use of menus for communications between the software and the interactive user; (2) the checkpoint/restart facility to recreate in one job the internal environment achieved in an earlier one; and (3) the error recovery capability which would normally cause job termination. This interactive system, which executes on an IBM 360/75 mainframe, was adapted for use in noninteractive (batch) mode. A case study is presented to show how the interfaces work in practice by defining some fields based on an image screen display, noting the field definitions, and obtaining a film product of the classification map.

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

A Graphical Operator Interface for a Telerobotic Inspection System

NASA Technical Reports Server (NTRS)

Kim, W. S.; Tso, K. S.; Hayati, S.

1993-01-01

Operator interface has recently emerged as an important element for efficient and safe operatorinteractions with the telerobotic system. Recent advances in graphical user interface (GUI) andgraphics/video merging technologies enable development of more efficient, flexible operatorinterfaces. This paper describes an advanced graphical operator interface newly developed for aremote surface inspection system at Jet Propulsion Laboratory. The interface has been designed sothat remote surface inspection can be performed by a single operator with an integrated robot controland image inspection capability. It supports three inspection strategies of teleoperated human visual inspection, human visual inspection with automated scanning, and machine-vision-based automated inspection.

Advances in Machine Technology.

PubMed

Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

2018-01-01

Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

Real English: A Translator to Enable Natural Language Man-Machine Conversation.

ERIC Educational Resources Information Center

Gautin, Harvey

This dissertation presents a pragmatic interpreter/translator called Real English to serve as a natural language man-machine communication interface in a multi-mode on-line information retrieval system. This multi-mode feature affords the user a library-like searching tool by giving him access to a dictionary, lexicon, thesaurus, synonym table,…

Spatial Brain Control Interface using Optical and Electrophysiological Measures

DTIC Science & Technology

2013-08-27

appropriate for implementing a reliable brain-computer interface ( BCI ). The LSVM method 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 27-08-2013 13...Machine (LSVM) was the most appropriate for implementing a reliable brain-computer interface ( BCI ). The LSVM method was applied to the imaging data...local field potentials proved to be fast and strongly tuned for the spatial parameters of the task. Thus, a reliable BCI that can predict upcoming

State transition storyboards: A tool for designing the Goldstone solar system radar data acquisition system user interface software

NASA Technical Reports Server (NTRS)

Howard, S. D.

1987-01-01

Effective user interface design in software systems is a complex task that takes place without adequate modeling tools. By combining state transition diagrams and the storyboard technique of filmmakers, State Transition Storyboards were developed to provide a detailed modeling technique for the Goldstone Solar System Radar Data Acquisition System human-machine interface. Illustrations are included with a description of the modeling technique.

Intelligent Adaptive Systems: Literature Research of Design Guidance for Intelligent Adaptive Automation and Interfaces

DTIC Science & Technology

2007-09-01

behaviour based on past experience of interacting with the operator), and mobile (i.e., can move themselves from one machine to another). Edwards argues that...Sofge, D., Bugajska, M., Adams, W., Perzanowski, D., and Schultz, A. (2003). Agent-based Multimodal Interface for Dynamically Autonomous Mobile Robots...based architecture can provide a natural and scalable approach to implementing a multimodal interface to control mobile robots through dynamic

myChEMBL: a virtual machine implementation of open data and cheminformatics tools.

PubMed

Ochoa, Rodrigo; Davies, Mark; Papadatos, George; Atkinson, Francis; Overington, John P

2014-01-15

myChEMBL is a completely open platform, which combines public domain bioactivity data with open source database and cheminformatics technologies. myChEMBL consists of a Linux (Ubuntu) Virtual Machine featuring a PostgreSQL schema with the latest version of the ChEMBL database, as well as the latest RDKit cheminformatics libraries. In addition, a self-contained web interface is available, which can be modified and improved according to user specifications. The VM is available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/VM/myChEMBL/current. The web interface and web services code is available at: https://github.com/rochoa85/myChEMBL.

Tactual interfaces: The human perceiver

NASA Technical Reports Server (NTRS)

Srinivasan, M. A.

1991-01-01

Increasingly complex human-machine interactions, such as in teleoperation or in virtual environments, have necessitated the optimal use of the human tactual channel for information transfer. This need leads to a demand for a basic understanding of how the human tactual system works, so that the tactual interface between the human and the machine can receive the command signals from the human, as well as display the information to the human, in a manner that appears natural to the human. The tactual information consists of two components: (1) contact information which specifies the nature of direct contact with the object; and (2) kinesthetic information which refers to the position and motion of the limbs. This paper is mostly concerned with contact information.

Analysis of a display and control system man-machine interface concept. Volume 1: Final technical report

NASA Technical Reports Server (NTRS)

Karl, D. R.

1972-01-01

An evaluation was made of the feasibility of utilizing a simplified man machine interface concept to manage and control a complex space system involving multiple redundant computers that control multiple redundant subsystems. The concept involves the use of a CRT for display and a simple keyboard for control, with a tree-type control logic for accessing and controlling mission, systems, and subsystem elements. The concept was evaluated in terms of the Phase B space shuttle orbiter, to utilize the wide scope of data management and subsystem control inherent in the central data management subsystem provided by the Phase B design philosophy. Results of these investigations are reported in four volumes.

New generation emerging technologies for neurorehabilitation and motor assistance.

PubMed

Frisoli, Antonio; Solazzi, Massimiliano; Loconsole, Claudio; Barsotti, Michele

2016-12-01

This paper illustrates the application of emerging technologies and human-machine interfaces to the neurorehabilitation and motor assistance fields. The contribution focuses on wearable technologies and in particular on robotic exoskeleton as tools for increasing freedom to move and performing Activities of Daily Living (ADLs). This would result in a deep improvement in quality of life, also in terms of improved function of internal organs and general health status. Furthermore, the integration of these robotic systems with advanced bio-signal driven human-machine interface can increase the degree of participation of patient in robotic training allowing to recognize user's intention and assisting the patient in rehabilitation tasks, thus representing a fundamental aspect to elicit motor learning.

Calibrated thermal microscopy of the tool-chip interface in machining

NASA Astrophysics Data System (ADS)

Yoon, Howard W.; Davies, Matthew A.; Burns, Timothy J.; Kennedy, M. D.

2000-03-01

A critical parameter in predicting tool wear during machining and in accurate computer simulations of machining is the spatially-resolved temperature at the tool-chip interface. We describe the development and the calibration of a nearly diffraction-limited thermal-imaging microscope to measure the spatially-resolved temperatures during the machining of an AISI 1045 steel with a tungsten-carbide tool bit. The microscope has a target area of 0.5 mm X 0.5 mm square region with a < 5 micrometers spatial resolution and is based on a commercial InSb 128 X 128 focal plane array with an all reflective microscope objective. The minimum frame image acquisition time is < 1 ms. The microscope is calibrated using a standard blackbody source from the radiance temperature calibration laboratory at the National Institute of Standards and Technology, and the emissivity of the machined material is deduced from the infrared reflectivity measurements. The steady-state thermal images from the machining of 1045 steel are compared to previous determinations of tool temperatures from micro-hardness measurements and are found to be in agreement with those studies. The measured average chip temperatures are also in agreement with the temperature rise estimated from energy balance considerations. From these calculations and the agreement between the experimental and the calculated determinations of the emissivity of the 1045 steel, the standard uncertainty of the temperature measurements is estimated to be about 45 degree(s)C at 900 degree(s)C.

Associations between maternal early pregnancy body mass index (BMI) and placental DNA methylation at term

USDA-ARS?s Scientific Manuscript database

The placenta serves as the definitive maternal-fetal interface and mediates exchange of nutrients, gases, and waste between mother and the developing fetus. The placenta integrates signals from both mother and baby, coordinating maternal nutrient supply with fetal demand and development. In epidemio...

Integrating robotic action with biologic perception: A brain-machine symbiosis theory

NASA Astrophysics Data System (ADS)

Mahmoudi, Babak

In patients with motor disability the natural cyclic flow of information between the brain and external environment is disrupted by their limb impairment. Brain-Machine Interfaces (BMIs) aim to provide new communication channels between the brain and environment by direct translation of brain's internal states into actions. For enabling the user in a wide range of daily life activities, the challenge is designing neural decoders that autonomously adapt to different tasks, environments, and to changes in the pattern of neural activity. In this dissertation, a novel decoding framework for BMIs is developed in which a computational agent autonomously learns how to translate neural states into action based on maximization of a measure of shared goal between user and the agent. Since the agent and brain share the same goal, a symbiotic relationship between them will evolve therefore this decoding paradigm is called a Brain-Machine Symbiosis (BMS) framework. A decoding agent was implemented within the BMS framework based on the Actor-Critic method of Reinforcement Learning. The rule of the Actor as a neural decoder was to find mapping between the neural representation of motor states in the primary motor cortex (MI) and robot actions in order to solve reaching tasks. The Actor learned the optimal control policy using an evaluative feedback that was estimated by the Critic directly from the user's neural activity of the Nucleus Accumbens (NAcc). Through a series of computational neuroscience studies in a cohort of rats it was demonstrated that NAcc could provide a useful evaluative feedback by predicting the increase or decrease in the probability of earning reward based on the environmental conditions. Using a closed-loop BMI simulator it was demonstrated the Actor-Critic decoding architecture was able to adapt to different tasks as well as changes in the pattern of neural activity. The custom design of a dual micro-wire array enabled simultaneous implantation of MI and NAcc for the development of a full closed-loop system. The Actor-Critic decoding architecture was able to solve the brain-controlled reaching task using a robotic arm by capturing the interdependency between the simultaneous action representation in MI and reward expectation in NAcc.

Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface.

PubMed

Chang, G C; Kang, W J; Luh, J J; Cheng, C K; Lai, J S; Chen, J J; Kuo, T S

1996-10-01

The purpose of this study was to develop a real-time electromyogram (EMG) discrimination system to provide control commands for man-machine interface applications. A host computer with a plug-in data acquisition and processing board containing a TMS320 C31 floating-point digital signal processor was used to attain real-time EMG classification. Two-channel EMG signals were collected by two pairs of surface electrodes located bilaterally between the sternocleidomastoid and the upper trapezius. Five motions of the neck and shoulders were discriminated for each subject. The zero-crossing rate was employed to detect the onset of muscle contraction. The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, were used as the recognition features. These features were then discriminated using a modified maximum likelihood distance classifier. The total response time of this EMG discrimination system was achieved about within 0.17 s. Four able bodied and two C5/6 quadriplegic subjects took part in the experiment, and achieved 95% mean recognition rate in discrimination between the five specific motions. The response time and the reliability of recognition indicate that this system has the potential to discriminate body motions for man-machine interface applications.

Concurrent Image Processing Executive (CIPE). Volume 1: Design overview

NASA Technical Reports Server (NTRS)

Lee, Meemong; Groom, Steven L.; Mazer, Alan S.; Williams, Winifred I.

1990-01-01

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are described. The target machine for this software is a JPL/Caltech Mark 3fp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules: user interface, host-resident executive, hypercube-resident executive, and application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube, a data management method which distributes, redistributes, and tracks data set information was implemented. The data management also allows data sharing among application programs. The CIPE software architecture provides a flexible environment for scientific analysis of complex remote sensing image data, such as planetary data and imaging spectrometry, utilizing state-of-the-art concurrent computation capabilities.

Mechanically Compliant Electronic Materials for Wearable Photovoltaics and Human-Machine Interfaces

NASA Astrophysics Data System (ADS)

O'Connor, Timothy Francis, III

Applications of stretchable electronic materials for human-machine interfaces are described herein. Intrinsically stretchable organic conjugated polymers and stretchable electronic composites were used to develop stretchable organic photovoltaics (OPVs), mechanically robust wearable OPVs, and human-machine interfaces for gesture recognition, American Sign Language Translation, haptic control of robots, and touch emulation for virtual reality, augmented reality, and the transmission of touch. The stretchable and wearable OPVs comprise active layers of poly-3-alkylthiophene:phenyl-C61-butyric acid methyl ester (P3AT:PCBM) and transparent conductive electrodes of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) and devices could only be fabricated through a deep understanding of the connection between molecular structure and the co-engineering of electronic performance with mechanical resilience. The talk concludes with the use of composite piezoresistive sensors two smart glove prototypes. The first integrates stretchable strain sensors comprising a carbon-elastomer composite, a wearable microcontroller, low energy Bluetooth, and a 6-axis accelerometer/gyroscope to construct a fully functional gesture recognition glove capable of wirelessly translating American Sign Language to text on a cell phone screen. The second creates a system for the haptic control of a 3D printed robot arm, as well as the transmission of touch and temperature information.

Toward a mathematical formalism of performance, task difficulty, and activation

NASA Technical Reports Server (NTRS)

Samaras, George M.

1988-01-01

The rudiments of a mathematical formalism for handling operational, physiological, and psychological concepts are developed for use by the man-machine system design engineer. The formalism provides a framework for developing a structured, systematic approach to the interface design problem, using existing mathematical tools, and simplifying the problem of telling a machine how to measure and use performance.

Optical HMI with biomechanical energy harvesters integrated in textile supports

NASA Astrophysics Data System (ADS)

De Pasquale, G.; Kim, SG; De Pasquale, D.

2015-12-01

This paper reports the design, prototyping and experimental validation of a human-machine interface (HMI), named GoldFinger, integrated into a glove with energy harvesting from fingers motion. The device is addressed to medical applications, design tools, virtual reality field and to industrial applications where the interaction with machines is restricted by safety procedures. The HMI prototype includes four piezoelectric transducers applied to the fingers backside at PIP (proximal inter-phalangeal) joints, electric wires embedded in the fabric connecting the transducers, aluminum case for the electronics, wearable switch made with conductive fabrics to turn the communication channel on and off, and a LED. The electronic circuit used to manage the power and to control the light emitter includes a diodes bridge, leveling capacitors, storage battery and switch made by conductive fabric. The communication with the machine is managed by dedicated software, which includes the user interface, the optical tracking, and the continuous updating of the machine microcontroller. The energetic benefit of energy harvester on the battery lifetime is inversely proportional to the activation time of the optical emitter. In most applications, the optical port is active for 1 to 5% of the time, corresponding to battery lifetime increasing between about 14% and 70%.

Visualization tool for human-machine interface designers

NASA Astrophysics Data System (ADS)

Prevost, Michael P.; Banda, Carolyn P.

1991-06-01

As modern human-machine systems continue to grow in capabilities and complexity, system operators are faced with integrating and managing increased quantities of information. Since many information components are highly related to each other, optimizing the spatial and temporal aspects of presenting information to the operator has become a formidable task for the human-machine interface (HMI) designer. The authors describe a tool in an early stage of development, the Information Source Layout Editor (ISLE). This tool is to be used for information presentation design and analysis; it uses human factors guidelines to assist the HMI designer in the spatial layout of the information required by machine operators to perform their tasks effectively. These human factors guidelines address such areas as the functional and physical relatedness of information sources. By representing these relationships with metaphors such as spring tension, attractors, and repellers, the tool can help designers visualize the complex constraint space and interacting effects of moving displays to various alternate locations. The tool contains techniques for visualizing the relative 'goodness' of a configuration, as well as mechanisms such as optimization vectors to provide guidance toward a more optimal design. Also available is a rule-based design checker to determine compliance with selected human factors guidelines.

A machine learning system to improve heart failure patient assistance.

PubMed

Guidi, Gabriele; Pettenati, Maria Chiara; Melillo, Paolo; Iadanza, Ernesto

2014-11-01

In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.

The Strength of the Metal. Aluminum Oxide Interface

NASA Technical Reports Server (NTRS)

Pepper, S. V.

1984-01-01

The strength of the interface between metals and aluminum oxide is an important factor in the successful operation of devices found throughout modern technology. One finds the interface in machine tools, jet engines, and microelectronic integrated circuits. The strength of the interface, however, should be strong or weak depending on the application. The diverse technological demands have led to some general ideas concerning the origin of the interfacial strength, and have stimulated fundamental research on the problem. Present status of our understanding of the source of the strength of the metal - aluminum oxide interface in terms of interatomic bonds are reviewed. Some future directions for research are suggested.

Simulation of the «COSMONAUT-ROBOT» System Interaction on the Lunar Surface Based on Methods of Machine Vision and Computer Graphics

NASA Astrophysics Data System (ADS)

Kryuchkov, B. I.; Usov, V. M.; Chertopolokhov, V. A.; Ronzhin, A. L.; Karpov, A. A.

2017-05-01

Extravehicular activity (EVA) on the lunar surface, necessary for the future exploration of the Moon, involves extensive use of robots. One of the factors of safe EVA is a proper interaction between cosmonauts and robots in extreme environments. This requires a simple and natural man-machine interface, e.g. multimodal contactless interface based on recognition of gestures and cosmonaut's poses. When travelling in the "Follow Me" mode (master/slave), a robot uses onboard tools for tracking cosmonaut's position and movements, and on the basis of these data builds its itinerary. The interaction in the system "cosmonaut-robot" on the lunar surface is significantly different from that on the Earth surface. For example, a man, dressed in a space suit, has limited fine motor skills. In addition, EVA is quite tiring for the cosmonauts, and a tired human being less accurately performs movements and often makes mistakes. All this leads to new requirements for the convenient use of the man-machine interface designed for EVA. To improve the reliability and stability of human-robot communication it is necessary to provide options for duplicating commands at the task stages and gesture recognition. New tools and techniques for space missions must be examined at the first stage of works in laboratory conditions, and then in field tests (proof tests at the site of application). The article analyzes the methods of detection and tracking of movements and gesture recognition of the cosmonaut during EVA, which can be used for the design of human-machine interface. A scenario for testing these methods by constructing a virtual environment simulating EVA on the lunar surface is proposed. Simulation involves environment visualization and modeling of the use of the "vision" of the robot to track a moving cosmonaut dressed in a spacesuit.

DNA Bipedal Motor Achieves a Large Number of Steps Due to Operation Using Microfluidics-Based Interface.

PubMed

Tomov, Toma E; Tsukanov, Roman; Glick, Yair; Berger, Yaron; Liber, Miran; Avrahami, Dorit; Gerber, Doron; Nir, Eyal

2017-04-25

Realization of bioinspired molecular machines that can perform many and diverse operations in response to external chemical commands is a major goal in nanotechnology, but current molecular machines respond to only a few sequential commands. Lack of effective methods for introduction and removal of command compounds and low efficiencies of the reactions involved are major reasons for the limited performance. We introduce here a user interface based on a microfluidics device and single-molecule fluorescence spectroscopy that allows efficient introduction and removal of chemical commands and enables detailed study of the reaction mechanisms involved in the operation of synthetic molecular machines. The microfluidics provided 64 consecutive DNA strand commands to a DNA-based motor system immobilized inside the microfluidics, driving a bipedal walker to perform 32 steps on a DNA origami track. The microfluidics enabled removal of redundant strands, resulting in a 6-fold increase in processivity relative to an identical motor operated without strand removal and significantly more operations than previously reported for user-controlled DNA nanomachines. In the motor operated without strand removal, redundant strands interfere with motor operation and reduce its performance. The microfluidics also enabled computer control of motor direction and speed. Furthermore, analysis of the reaction kinetics and motor performance in the absence of redundant strands, made possible by the microfluidics, enabled accurate modeling of the walker processivity. This enabled identification of dynamic boundaries and provided an explanation, based on the "trap state" mechanism, for why the motor did not perform an even larger number of steps. This understanding is very important for the development of future motors with significantly improved performance. Our universal interface enables two-way communication between user and molecular machine and, relying on concepts similar to that of solid-phase synthesis, removes limitations on the number of external stimuli. This interface, therefore, is an important step toward realization of reliable, processive, reproducible, and useful externally controlled DNA nanomachines.

The remapping of space in motor learning and human-machine interfaces

PubMed Central

Mussa-Ivaldi, F.A.; Danziger, Z.

2009-01-01

Studies of motor adaptation to patterns of deterministic forces have revealed the ability of the motor control system to form and use predictive representations of the environment. One of the most fundamental elements of our environment is space itself. This article focuses on the notion of Euclidean space as it applies to common sensory motor experiences. Starting from the assumption that we interact with the world through a system of neural signals, we observe that these signals are not inherently endowed with metric properties of the ordinary Euclidean space. The ability of the nervous system to represent these properties depends on adaptive mechanisms that reconstruct the Euclidean metric from signals that are not Euclidean. Gaining access to these mechanisms will reveal the process by which the nervous system handles novel sophisticated coordinate transformation tasks, thus highlighting possible avenues to create functional human-machine interfaces that can make that task much easier. A set of experiments is presented that demonstrate the ability of the sensory-motor system to reorganize coordination in novel geometrical environments. In these environments multiple degrees of freedom of body motions are used to control the coordinates of a point in a two-dimensional Euclidean space. We discuss how practice leads to the acquisition of the metric properties of the controlled space. Methods of machine learning based on the reduction of reaching errors are tested as a means to facilitate learning by adaptively changing he map from body motions to controlled device. We discuss the relevance of the results to the development of adaptive human machine interfaces and optimal control. PMID:19665553

Screen-Printed Washable Electronic Textiles as Self-Powered Touch/Gesture Tribo-Sensors for Intelligent Human-Machine Interaction.

PubMed

Cao, Ran; Pu, Xianjie; Du, Xinyu; Yang, Wei; Wang, Jiaona; Guo, Hengyu; Zhao, Shuyu; Yuan, Zuqing; Zhang, Chi; Li, Congju; Wang, Zhong Lin

2018-05-22

Multifunctional electronic textiles (E-textiles) with embedded electric circuits hold great application prospects for future wearable electronics. However, most E-textiles still have critical challenges, including air permeability, satisfactory washability, and mass fabrication. In this work, we fabricate a washable E-textile that addresses all of the concerns and shows its application as a self-powered triboelectric gesture textile for intelligent human-machine interfacing. Utilizing conductive carbon nanotubes (CNTs) and screen-printing technology, this kind of E-textile embraces high conductivity (0.2 kΩ/sq), high air permeability (88.2 mm/s), and can be manufactured on common fabric at large scales. Due to the advantage of the interaction between the CNTs and the fabrics, the electrode shows excellent stability under harsh mechanical deformation and even after being washed. Moreover, based on a single-electrode mode triboelectric nanogenerator and electrode pattern design, our E-textile exhibits highly sensitive touch/gesture sensing performance and has potential applications for human-machine interfacing.

A comparison of optimal MIMO linear and nonlinear models for brain machine interfaces

NASA Astrophysics Data System (ADS)

Kim, S.-P.; Sanchez, J. C.; Rao, Y. N.; Erdogmus, D.; Carmena, J. M.; Lebedev, M. A.; Nicolelis, M. A. L.; Principe, J. C.

2006-06-01

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

PubMed

Kim, S-P; Sanchez, J C; Rao, Y N; Erdogmus, D; Carmena, J M; Lebedev, M A; Nicolelis, M A L; Principe, J C

2006-06-01

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

Software Independent Verification and Validation (SIV&V) Simplified

DTIC Science & Technology

2006-12-01

Configuration Item I/O Input/Output I2V2 Independent Integrated Verification and Validation IBM International Business Machines ICD Interface...IPT Integrated Product Team IRS Interface Requirements Specification ISD Integrated System Diagram ITD Integrated Test Description ITP ...programming languages such as COBOL (Common Business Oriented Language) (Codasyl committee 1960), and FORTRAN (FORmula TRANslator) ( IBM 1952) (Robat 11

Software Engineering for User Interfaces. Technical Report.

ERIC Educational Resources Information Center

Draper, Stephen W.; Norman, Donald A.

The discipline of software engineering can be extended in a natural way to deal with the issues raised by a systematic approach to the design of human-machine interfaces. The user should be treated as part of the system being designed and projects should be organized to take into account the current lack of a priori knowledge of user interface…

State policies targeting junk food in schools: racial/ethnic differences in the effect of policy change on soda consumption.

PubMed

Taber, Daniel R; Stevens, June; Evenson, Kelly R; Ward, Dianne S; Poole, Charles; Maciejewski, Matthew L; Murray, David M; Brownson, Ross C

2011-09-01

We estimated the association between state policy changes and adolescent soda consumption and body mass index (BMI) percentile, overall and by race/ethnicity. We obtained data on whether states required or recommended that schools prohibit junk food in vending machines, snack bars, concession stands, and parties from the 2000 and 2006 School Health Policies and Programs Study. We used linear mixed models to estimate the association between 2000-2006 policy changes and 2007 soda consumption and BMI percentile, as reported by 90 730 students in 33 states and the District of Columbia in the Youth Risk Behavior Survey, and to test for racial/ethnic differences in the associations. Policy changes targeting concession stands were associated with 0.09 fewer servings of soda per day among students (95% confidence interval [CI] = -0.17, -0.01); the association was more pronounced among non-Hispanic Blacks (0.19 fewer servings per day). Policy changes targeting parties were associated with 0.07 fewer servings per day (95% CI = -0.13, 0.00). Policy changes were not associated with BMI percentile in any group. State policies targeting junk food in schools may reduce racial/ethnic disparities in adolescent soda consumption, but their impact appears to be too weak to reduce adolescent BMI percentile.

Intersecting vane machines

DOEpatents

Bailey, H. Sterling; Chomyszak, Stephen M.

2007-01-16

The invention provides a toroidal intersecting vane machine incorporating intersecting rotors to form primary and secondary chambers whose porting configurations minimize friction and maximize efficiency. Specifically, it is an object of the invention to provide a toroidal intersecting vane machine that greatly reduces the frictional losses through meshing surfaces without the need for external gearing by modifying the function of one or the other of the rotors from that of "fluid moving" to that of "valving" thereby reducing the pressure loads and associated inefficiencies at the interface of the meshing surfaces. The inventions described herein relate to these improvements.

Delivering key signals to the machine: seeking the electric signal that muscles emanate

NASA Astrophysics Data System (ADS)

Bani Hashim, A. Y.; Maslan, M. N.; Izamshah, R.; Mohamad, I. S.

2014-11-01

Due to the limitation of electric power generation in the human body, present human-machine interfaces have not been successful because of the nature of standard electronics circuit designs, which do not consider the specifications of signals that resulted from the skin. In general, the outcomes and applications of human-machine interfaces are limited to custom-designed subsystems, such as neuroprosthesis. We seek to model the bio dynamical of sub skin into equivalent mathematical definitions, descriptions, and theorems. Within the human skin, there are networks of nerves that permit the skin to function as a multi dimension transducer. We investigate the nature of structural skin. Apart from multiple networks of nerves, there are other segments within the skin such as minute muscles. We identify the segments that are active when there is an electromyography activity. When the nervous system is firing signals, the muscle is being stimulated. We evaluate the phenomena of biodynamic of the muscles that is concerned with the electromyography activity of the nervous system. In effect, we design a relationship between the human somatosensory and synthetic systems sensory as the union of a complete set of the new domain of the functional system. This classifies electromyogram waveforms linked to intent thought of an operator. The system will become the basis for delivering key signals to machine such that the machine is under operator's intent, hence slavery.

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.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (HP9000 SERIES 700/800 VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), (3) HP9000 Series 700/800 computers running HP-UX 9.x and X11/R5 (HP 4mm DDS DAT tape cartridge in UNIX tar format), (4) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and (5) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2. Version 5.1 of TAE Plus remains available for DEC VAX computers running VMS, HP9000 Series 300/400 computers running HP-UX, and HP 9000 Series 700/800 computers running HP-UX 8.x and X11/R4. Please contact COSMIC for details on these versions of TAE Plus.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (IBM RS/6000 VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), (3) HP9000 Series 700/800 computers running HP-UX 9.x and X11/R5 (HP 4mm DDS DAT tape cartridge in UNIX tar format), (4) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and (5) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2. Version 5.1 of TAE Plus remains available for DEC VAX computers running VMS, HP9000 Series 300/400 computers running HP-UX, and HP 9000 Series 700/800 computers running HP-UX 8.x and X11/R4. Please contact COSMIC for details on these versions of TAE Plus.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (SUN4 VERSION WITH MOTIF)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), (3) HP9000 Series 700/800 computers running HP-UX 9.x and X11/R5 (HP 4mm DDS DAT tape cartridge in UNIX tar format), (4) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and (5) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2. Version 5.1 of TAE Plus remains available for DEC VAX computers running VMS, HP9000 Series 300/400 computers running HP-UX, and HP 9000 Series 700/800 computers running HP-UX 8.x and X11/R4. Please contact COSMIC for details on these versions of TAE Plus.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (SILICON GRAPHICS VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), (3) HP9000 Series 700/800 computers running HP-UX 9.x and X11/R5 (HP 4mm DDS DAT tape cartridge in UNIX tar format), (4) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and (5) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2. Version 5.1 of TAE Plus remains available for DEC VAX computers running VMS, HP9000 Series 300/400 computers running HP-UX, and HP 9000 Series 700/800 computers running HP-UX 8.x and X11/R4. Please contact COSMIC for details on these versions of TAE Plus.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (SUN4 VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), (3) HP9000 Series 700/800 computers running HP-UX 9.x and X11/R5 (HP 4mm DDS DAT tape cartridge in UNIX tar format), (4) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and (5) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2. Version 5.1 of TAE Plus remains available for DEC VAX computers running VMS, HP9000 Series 300/400 computers running HP-UX, and HP 9000 Series 700/800 computers running HP-UX 8.x and X11/R4. Please contact COSMIC for details on these versions of TAE Plus.

TAE+ 5.2 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.2 (DEC RISC ULTRIX VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System and the Open Software Foundation's Motif. The HP 9000 Series 700/800 version of TAE 5.2 requires Version 11 Release 5 of the X Window System. All other machine versions of TAE 5.2 require Version 11, Release 4 of the X Window System. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is available on media suitable for five different machine platforms: (1) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), (2) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), (3) HP9000 Series 700/800 computers running HP-UX 9.x and X11/R5 (HP 4mm DDS DAT tape cartridge in UNIX tar format), (4) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and (5) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2. Version 5.1 of TAE Plus remains available for DEC VAX computers running VMS, HP9000 Series 300/400 computers running HP-UX, and HP 9000 Series 700/800 computers running HP-UX 8.x and X11/R4. Please contact COSMIC for details on these versions of TAE Plus.

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Emma M.; Hendrix, Val; Chertkov, Michael

This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper wemore » consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop.« less

Orthogonal cutting modeling of hybrid CFRP/Ti toward specific cutting energy and induced damage analyses

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2016-10-01

This paper studied the machinability of hybrid CFRP/Ti stack via the numerical approach. To this aim, an original FE model consisting of three fundamental physical constituents, i.e., CFRP phase, interface and Ti phase, was established in the Abaqus Explicit/code to construct the machining behavior of the composite-to-metal alliance. The CFRP phase was modeled as an equivalent homogeneous material (EHM) by considering its anisotropic behavior relative to the fiber orientation (θ) while the Ti alloy phase was assumed to exhibit isotropic and elastic-plastic behavior. The "interface" linking the "CFRP-to-Ti" contact boundary was physically modeled as an intermediate transition region through the concept of cohesive zone (CZ). Different constitutive laws and damage criteria were implemented to simulate the chip separation process of the bi-material system. The key cutting responses including specific cutting energy consumption, induced subsurface damage, and interface delamination were precisely addressed via the comprehensive FE analyses, and several key conclusions were drawn from this study.

FwWebViewPlus: integration of web technologies into WinCC OA based Human-Machine Interfaces at CERN

NASA Astrophysics Data System (ADS)

Golonka, Piotr; Fabian, Wojciech; Gonzalez-Berges, Manuel; Jasiun, Piotr; Varela-Rodriguez, Fernando

2014-06-01

The rapid growth in popularity of web applications gives rise to a plethora of reusable graphical components, such as Google Chart Tools and JQuery Sparklines, implemented in JavaScript and run inside a web browser. In the paper we describe the tool that allows for seamless integration of web-based widgets into WinCC Open Architecture, the SCADA system used commonly at CERN to build complex Human-Machine Interfaces. Reuse of widely available widget libraries and pushing the development efforts to a higher abstraction layer based on a scripting language allow for significant reduction in maintenance of the code in multi-platform environments compared to those currently used in C++ visualization plugins. Adequately designed interfaces allow for rapid integration of new web widgets into WinCC OA. At the same time, the mechanisms familiar to HMI developers are preserved, making the use of new widgets "native". Perspectives for further integration between the realms of WinCC OA and Web development are also discussed.

Sensing Pressure Distribution on a Lower-Limb Exoskeleton Physical Human-Machine Interface

PubMed Central

De Rossi, Stefano Marco Maria; Vitiello, Nicola; Lenzi, Tommaso; Ronsse, Renaud; Koopman, Bram; Persichetti, Alessandro; Vecchi, Fabrizio; Ijspeert, Auke Jan; van der Kooij, Herman; Carrozza, Maria Chiara

2011-01-01

A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer’s skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented. PMID:22346574

Integrated intelligent sensor for the textile industry

NASA Astrophysics Data System (ADS)

Peltie, Philippe; David, Dominique

1996-08-01

A new sensor has been developed for pantyhose inspection. Unlike a first complete inspection machine devoted to post- manufacturing control of the whole panty, this sensor will be directly integrated on currently existing manufacturing machines, and will combine advantages of miniaturization is to design an intelligent, compact and very cheap product, which should be integrated without requiring any modifications of host machines. The sensor part was designed to achieve closed acquisition, and various solutions have been explored to maintain adequate depth of field. The illumination source will be integrated in the device. The processing part will include correction facilities and electronic processing. Finally, high-level information will be output in order to interface directly with the manufacturing machine automate.

Are Mash-Ups the Future for Online Learning Platforms? Psychology A-Level Students' Judgements about VLE and MUPPLE Interfaces

ERIC Educational Resources Information Center

Jarvis, Matt; Gauntlett, Lizzie; Collins, Hayley

2011-01-01

Virtual Learning Environments (VLEs) have become ubiquitous in colleges and universities but have failed to consistently improve learning (Machin, 2007). An alternative interface can be provided in the form of a mashed-up personal learning environment (MUPPLE). The aim of this study was to investigate student perceptions of its desirability and…

Metaphors for the Nature of Human-Computer Interaction in an Empowering Environment: Interaction Style Influences the Manner of Human Accomplishment.

ERIC Educational Resources Information Center

Weller, Herman G.; Hartson, H. Rex

1992-01-01

Describes human-computer interface needs for empowering environments in computer usage in which the machine handles the routine mechanics of problem solving while the user concentrates on its higher order meanings. A closed-loop model of interaction is described, interface as illusion is discussed, and metaphors for human-computer interaction are…

TAE+ 5.1 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.1 (HP9000 SERIES 300/400 VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System, Version 11 Release 4, and the Open Software Foundation's Motif. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is expected to be available on media suitable for seven different machine platforms: 1) DEC VAX computers running VMS (TK50 cartridge in VAX BACKUP format), 2) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), 3) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), 4) HP9000 Series 300/400 computers running HP-UX (.25 inch HP-preformatted tape cartridge in UNIX tar format), 5) HP9000 Series 700 computers running HP-UX (HP 4mm DDS DAT tape cartridge in UNIX tar format), 6) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and 7) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2.

TAE+ 5.1 - TRANSPORTABLE APPLICATIONS ENVIRONMENT PLUS, VERSION 5.1 (VAX VMS VERSION)

NASA Technical Reports Server (NTRS)

TAE SUPPORT OFFICE

1994-01-01

TAE (Transportable Applications Environment) Plus is an integrated, portable environment for developing and running interactive window, text, and graphical object-based application systems. The program allows both programmers and non-programmers to easily construct their own custom application interface and to move that interface and application to different machine environments. TAE Plus makes both the application and the machine environment transparent, with noticeable improvements in the learning curve. The main components of TAE Plus are as follows: (1) the WorkBench, a What You See Is What You Get (WYSIWYG) tool for the design and layout of a user interface; (2) the Window Programming Tools Package (WPT), a set of callable subroutines that control an application's user interface; and (3) TAE Command Language (TCL), an easy-to-learn command language that provides an easy way to develop an executable application prototype with a run-time interpreted language. The WorkBench tool allows the application developer to interactively construct the layout of an application's display screen by manipulating a set of interaction objects including input items such as buttons, icons, and scrolling text lists. User interface interactive objects include data-driven graphical objects such as dials, thermometers, and strip charts as well as menubars, option menus, file selection items, message items, push buttons, and color loggers. The WorkBench user specifies the windows and interaction objects that will make up the user interface, then specifies the sequence of the user interface dialogue. The description of the designed user interface is then saved into resource files. For those who desire to develop the designed user interface into an operational application, the WorkBench tool also generates source code (C, C++, Ada, and TCL) which fully controls the application's user interface through function calls to the WPTs. The WPTs are the runtime services used by application programs to display and control the user interfaces. Since the WPTs access the workbench-generated resource files during each execution, details such as color, font, location, and object type remain independent from the application code, allowing changes to the user interface without recompiling and relinking. In addition to WPTs, TAE Plus can control interaction of objects from the interpreted TAE Command Language. TCL provides a means for the more experienced developer to quickly prototype an application's use of TAE Plus interaction objects and add programming logic without the overhead of compiling or linking. TAE Plus requires MIT's X Window System, Version 11 Release 4, and the Open Software Foundation's Motif. The Workbench and WPTs are written in C++ and the remaining code is written in C. TAE Plus is available by license for an unlimited time period. The licensed program product includes the TAE Plus source code and one set of supporting documentation. Additional documentation may be purchased separately at the price indicated below. The amount of disk space required to load the TAE Plus tar format tape is between 35Mb and 67Mb depending on the machine version. The recommended minimum memory is 12Mb. Each TAE Plus platform delivery tape includes pre-built libraries and executable binary code for that particular machine, as well as source code, so users do not have to do an installation. Users wishing to recompile the source will need both a C compiler and either GNU's C++ Version 1.39 or later, or a C++ compiler based on AT&T 2.0 cfront. TAE Plus was developed in 1989 and version 5.2 was released in 1993. TAE Plus 5.2 is expected to be available on media suitable for seven different machine platforms: 1) DEC VAX computers running VMS (TK50 cartridge in VAX BACKUP format), 2) IBM RS/6000 series workstations running AIX (.25 inch tape cartridge in UNIX tar format), 3) DEC RISC workstations running ULTRIX (TK50 cartridge in UNIX tar format), 4) HP9000 Series 300/400 computers running HP-UX (.25 inch HP-preformatted tape cartridge in UNIX tar format), 5) HP9000 Series 700 computers running HP-UX (HP 4mm DDS DAT tape cartridge in UNIX tar format), 6) Sun4 (SPARC) series computers running SunOS (.25 inch tape cartridge in UNIX tar format), and 7) SGI Indigo computers running IRIX (.25 inch IRIS tape cartridge in UNIX tar format). Please contact COSMIC to obtain detailed information about the supported operating system and OSF/Motif releases required for each of these machine versions. An optional Motif Object Code License is available for the Sun4 version of TAE Plus 5.2.

Preprocessor with spline interpolation for converting stereolithography into cutter location source data

NASA Astrophysics Data System (ADS)

Nagata, Fusaomi; Okada, Yudai; Sakamoto, Tatsuhiko; Kusano, Takamasa; Habib, Maki K.; Watanabe, Keigo

2017-06-01

The authors have developed earlier an industrial machining robotic system for foamed polystyrene materials. The developed robotic CAM system provided a simple and effective interface without the need to use any robot language between operators and the machining robot. In this paper, a preprocessor for generating Cutter Location Source data (CLS data) from Stereolithography (STL data) is first proposed for robotic machining. The preprocessor enables to control the machining robot directly using STL data without using any commercially provided CAM system. The STL deals with a triangular representation for a curved surface geometry. The preprocessor allows machining robots to be controlled through a zigzag or spiral path directly calculated from STL data. Then, a smart spline interpolation method is proposed and implemented for smoothing coarse CLS data. The effectiveness and potential of the developed approaches are demonstrated through experiments on actual machining and interpolation.

Transfer Learning to Accelerate Interface Structure Searches

NASA Astrophysics Data System (ADS)

Oda, Hiromi; Kiyohara, Shin; Tsuda, Koji; Mizoguchi, Teruyasu

2017-12-01

Interfaces have atomic structures that are significantly different from those in the bulk, and play crucial roles in material properties. The central structures at the interfaces that provide properties have been extensively investigated. However, determination of even one interface structure requires searching for the stable configuration among many thousands of candidates. Here, a powerful combination of machine learning techniques based on kriging and transfer learning (TL) is proposed as a method for unveiling the interface structures. Using the kriging+TL method, thirty-three grain boundaries were systematically determined from 1,650,660 candidates in only 462 calculations, representing an increase in efficiency over conventional all-candidate calculation methods, by a factor of approximately 3,600.

Toward an autonomous brain machine interface: integrating sensorimotor reward modulation and reinforcement learning.

PubMed

Marsh, Brandi T; Tarigoppula, Venkata S Aditya; Chen, Chen; Francis, Joseph T

2015-05-13

For decades, neurophysiologists have worked on elucidating the function of the cortical sensorimotor control system from the standpoint of kinematics or dynamics. Recently, computational neuroscientists have developed models that can emulate changes seen in the primary motor cortex during learning. However, these simulations rely on the existence of a reward-like signal in the primary sensorimotor cortex. Reward modulation of the primary sensorimotor cortex has yet to be characterized at the level of neural units. Here we demonstrate that single units/multiunits and local field potentials in the primary motor (M1) cortex of nonhuman primates (Macaca radiata) are modulated by reward expectation during reaching movements and that this modulation is present even while subjects passively view cursor motions that are predictive of either reward or nonreward. After establishing this reward modulation, we set out to determine whether we could correctly classify rewarding versus nonrewarding trials, on a moment-to-moment basis. This reward information could then be used in collaboration with reinforcement learning principles toward an autonomous brain-machine interface. The autonomous brain-machine interface would use M1 for both decoding movement intention and extraction of reward expectation information as evaluative feedback, which would then update the decoding algorithm as necessary. In the work presented here, we show that this, in theory, is possible. Copyright © 2015 the authors 0270-6474/15/357374-14$15.00/0.

Programmable Pulse-Position-Modulation Encoder

NASA Technical Reports Server (NTRS)

Zhu, David; Farr, William

2006-01-01

A programmable pulse-position-modulation (PPM) encoder has been designed for use in testing an optical communication link. The encoder includes a programmable state machine and an electronic code book that can be updated to accommodate different PPM coding schemes. The encoder includes a field-programmable gate array (FPGA) that is programmed to step through the stored state machine and code book and that drives a custom high-speed serializer circuit board that is capable of generating subnanosecond pulses. The stored state machine and code book can be updated by means of a simple text interface through the serial port of a personal computer.

[Daedalus sive mechanicus--automated equipment and machines at the interface between mechanics and medicine].

PubMed

Bondio, Mariacarla Gadebusch

2009-01-01

Automata have always held a particular fascination. Their history leads back to their mythical ancestors, whose destinies raise considerable ethical questions about the sense of technology and about the boundaries between nature and art. In the 16th century engineers, architects and also physicians discussed the status of the ,artes mechanicae' and the machines they produced or used. Useful, sometimes dangerous, amusing and elaborate artefacts liven up their texts. Together with wonderful automata we find there also orthopaedic stretching machines and artificial limbs, whose acceptance by medical practice was anything but a matter of course.

Method for machining steel with diamond tools

DOEpatents

Casstevens, J.M.

1984-01-01

The present invention is directed to a method for machine optical quality finishes and contour accuracies of workpieces of carbon-containing metals such as steel with diamond tooling. The wear rate of the diamond tooling is significantly reduced by saturating the atmosphere at the interface of the workpiece and the diamond tool with a gaseous hydrocarbon during the machining operation. The presence of the gaseous hydrocarbon effectively eliminates the deterioration of the diamond tool by inhibiting or preventing the conversion of the diamond carbon to graphite carbon at the point of contact between the cutting tool and the workpiece.

Method for machining steel with diamond tools

DOEpatents

Casstevens, John M.

1986-01-01

The present invention is directed to a method for machining optical quality inishes and contour accuracies of workpieces of carbon-containing metals such as steel with diamond tooling. The wear rate of the diamond tooling is significantly reduced by saturating the atmosphere at the interface of the workpiece and the diamond tool with a gaseous hydrocarbon during the machining operation. The presence of the gaseous hydrocarbon effectively eliminates the deterioration of the diamond tool by inhibiting or preventing the conversion of the diamond carbon to graphite carbon at the point of contact between the cutting tool and the workpiece.

Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands

PubMed Central

Gonzalez-Vargas, Jose; Dosen, Strahinja; Amsuess, Sebastian; Yu, Wenwei; Farina, Dario

2015-01-01

Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system’s complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs. PMID:26069961

Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

PubMed

Gonzalez-Vargas, Jose; Dosen, Strahinja; Amsuess, Sebastian; Yu, Wenwei; Farina, Dario

2015-01-01

Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.

Validation results of specifications for motion control interoperability

NASA Astrophysics Data System (ADS)

Szabo, Sandor; Proctor, Frederick M.

1997-01-01

The National Institute of Standards and Technology (NIST) is participating in the Department of Energy Technologies Enabling Agile Manufacturing (TEAM) program to establish interface standards for machine tool, robot, and coordinate measuring machine controllers. At NIST, the focus is to validate potential application programming interfaces (APIs) that make it possible to exchange machine controller components with a minimal impact on the rest of the system. This validation is taking place in the enhanced machine controller (EMC) consortium and is in cooperation with users and vendors of motion control equipment. An area of interest is motion control, including closed-loop control of individual axes and coordinated path planning. Initial tests of the motion control APIs are complete. The APIs were implemented on two commercial motion control boards that run on two different machine tools. The results for a baseline set of APIs look promising, but several issues were raised. These include resolving differing approaches in how motions are programmed and defining a standard measurement of performance for motion control. This paper starts with a summary of the process used in developing a set of specifications for motion control interoperability. Next, the EMC architecture and its classification of motion control APIs into two classes, Servo Control and Trajectory Planning, are reviewed. Selected APIs are presented to explain the basic functionality and some of the major issues involved in porting the APIs to other motion controllers. The paper concludes with a summary of the main issues and ways to continue the standards process.

Aerodynamic seals for rotary machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bidkar, Rahul Anil; Cirri, Massimiliano; Thatte, Azam Mihir

2016-02-09

An aerodynamic seal assembly for a rotary machine includes multiple sealing device segments disposed circumferentially intermediate to a stationary housing and a rotor. Each of the segments includes a shoe plate with a forward-shoe section and an aft-shoe section having multiple labyrinth teeth therebetween facing the rotor. The sealing device segment also includes multiple flexures connected to the shoe plate and to a top interface element, wherein the multiple flexures are configured to allow the high pressure fluid to occupy a forward cavity and the low pressure fluid to occupy an aft cavity. Further, the sealing device segments include amore » secondary seal attached to the top interface element at one first end and positioned about the flexures and the shoe plate at one second end.« less

The Multimission Image Processing Laboratory's virtual frame buffer interface

NASA Technical Reports Server (NTRS)

Wolfe, T.

1984-01-01

Large image processing systems use multiple frame buffers with differing architectures and vendor supplied interfaces. This variety of architectures and interfaces creates software development, maintenance and portability problems for application programs. Several machine-dependent graphics standards such as ANSI Core and GKS are available, but none of them are adequate for image processing. Therefore, the Multimission Image Processing laboratory project has implemented a programmer level virtual frame buffer interface. This interface makes all frame buffers appear as a generic frame buffer with a specified set of characteristics. This document defines the virtual frame uffer interface and provides information such as FORTRAN subroutine definitions, frame buffer characteristics, sample programs, etc. It is intended to be used by application programmers and system programmers who are adding new frame buffers to a system.

BIRD: A general interface for sparse distributed memory simulators

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.

A High Performance Micro Channel Interface for Real-Time Industrial Image Processing

Treesearch

Thomas H. Drayer; Joseph G. Tront; Richard W. Conners

1995-01-01

Data collection and transfer devices are critical to the performance of any machine vision system. The interface described in this paper collects image data from a color line scan camera and transfers the data obtained into the system memory of a Micro Channel-based host computer. A maximum data transfer rate of 20 Mbytes/sec can be achieved using the DMA capabilities...

Open-Box Muscle-Computer Interface: Introduction to Human-Computer Interactions in Bioengineering, Physiology, and Neuroscience Courses

ERIC Educational Resources Information Center

Landa-Jiménez, M. A.; González-Gaspar, P.; Pérez-Estudillo, C.; López-Meraz, M. L.; Morgado-Valle, C.; Beltran-Parrazal, L.

2016-01-01

A Muscle-Computer Interface (muCI) is a human-machine system that uses electromyographic (EMG) signals to communicate with a computer. Surface EMG (sEMG) signals are currently used to command robotic devices, such as robotic arms and hands, and mobile robots, such as wheelchairs. These signals reflect the motor intention of a user before the…

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

Machine learning for Big Data analytics in plants.

PubMed

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

Low optical-loss facet preparation for silica-on-silicon photonics using the ductile dicing regime

NASA Astrophysics Data System (ADS)

Carpenter, Lewis G.; Rogers, Helen L.; Cooper, Peter A.; Holmes, Christopher; Gates, James C.; Smith, Peter G. R.

2013-11-01

The efficient production of high-quality facets for low-loss coupling is a significant production issue in integrated optics, usually requiring time consuming and manually intensive lapping and polishing steps, which add considerably to device fabrication costs. The development of precision dicing saws with diamond impregnated blades has allowed optical grade surfaces to be machined in crystalline materials such as lithium niobate and garnets. In this report we investigate the optimization of dicing machine parameters to obtain optical quality surfaces in a silica-on-silicon planar device demonstrating high optical quality in a commercially important glassy material. We achieve a surface roughness of 4.9 nm (Sa) using the optimized dicing conditions. By machining a groove across a waveguide, using the optimized dicing parameters, a grating based loss measurement technique is used to measure precisely the average free space interface loss per facet caused by scattering as a consequence of surface roughness. The average interface loss per facet was calculated to be: -0.63 dB and -0.76 dB for the TE and TM polarizations, respectively.

State Policies Targeting Junk Food in Schools: Racial/Ethnic Differences in the Effect of Policy Change on Soda Consumption

PubMed Central

Stevens, June; Evenson, Kelly R.; Ward, Dianne S.; Poole, Charles; Maciejewski, Matthew L.; Murray, David M.; Brownson, Ross C.

2011-01-01

Objectives. We estimated the association between state policy changes and adolescent soda consumption and body mass index (BMI) percentile, overall and by race/ethnicity. Methods. We obtained data on whether states required or recommended that schools prohibit junk food in vending machines, snack bars, concession stands, and parties from the 2000 and 2006 School Health Policies and Programs Study. We used linear mixed models to estimate the association between 2000–2006 policy changes and 2007 soda consumption and BMI percentile, as reported by 90 730 students in 33 states and the District of Columbia in the Youth Risk Behavior Survey, and to test for racial/ethnic differences in the associations. Results. Policy changes targeting concession stands were associated with 0.09 fewer servings of soda per day among students (95% confidence interval [CI] = −0.17, −0.01); the association was more pronounced among non-Hispanic Blacks (0.19 fewer servings per day). Policy changes targeting parties were associated with 0.07 fewer servings per day (95% CI = −0.13, 0.00). Policy changes were not associated with BMI percentile in any group. Conclusions. State policies targeting junk food in schools may reduce racial/ethnic disparities in adolescent soda consumption, but their impact appears to be too weak to reduce adolescent BMI percentile. PMID:21778484

Role-Based And Adaptive User Interface Designs In A Teledermatology Consult System: A Way To Secure And A Way To Enhance

PubMed Central

Lin, Yi-Jung; Speedie, Stuart

2003-01-01

User interface design is one of the most important parts of developing applications. Nowadays, a quality user interface must not only accommodate interaction between machines and users, but also needs to recognize the differences and provide functionalities for users from role-to-role or even individual-to-individual. With the web-based application of our Teledermatology consult system, the development environment provides us highly useful opportunities to create dynamic user interfaces, which lets us to gain greater access control and has the potential to increase efficiency of the system. We will describe the two models of user interfaces in our system: Role-based and Adaptive. PMID:14728419

A training platform for many-dimensional prosthetic devices using a virtual reality environment

PubMed Central

Putrino, David; Wong, Yan T.; Weiss, Adam; Pesaran, Bijan

2014-01-01

Brain machine interfaces (BMIs) have the potential to assist in the rehabilitation of millions of patients worldwide. Despite recent advancements in BMI technology for the restoration of lost motor function, a training environment to restore full control of the anatomical segments of an upper limb extremity has not yet been presented. Here, we develop a virtual upper limb prosthesis with 27 independent dimensions, the anatomical dimensions of the human arm and hand, and deploy the virtual prosthesis as an avatar in a virtual reality environment (VRE) that can be controlled in real-time. The prosthesis avatar accepts kinematic control inputs that can be captured from movements of the arm and hand as well as neural control inputs derived from processed neural signals. We characterize the system performance under kinematic control using a commercially available motion capture system. We also present the performance under kinematic control achieved by two non-human primates (Macaca Mulatta) trained to use the prosthetic avatar to perform reaching and grasping tasks. This is the first virtual prosthetic device that is capable of emulating all the anatomical movements of a healthy upper limb in real-time. Since the system accepts both neural and kinematic inputs for a variety of many-dimensional skeletons, we propose it provides a customizable training platform for the acquisition of many-dimensional neural prosthetic control. PMID:24726625

An Intention-Driven Semi-autonomous Intelligent Robotic System for Drinking.

PubMed

Zhang, Zhijun; Huang, Yongqian; Chen, Siyuan; Qu, Jun; Pan, Xin; Yu, Tianyou; Li, Yuanqing

2017-01-01

In this study, an intention-driven semi-autonomous intelligent robotic (ID-SIR) system is designed and developed to assist the severely disabled patients to live independently. The system mainly consists of a non-invasive brain-machine interface (BMI) subsystem, a robot manipulator and a visual detection and localization subsystem. Different from most of the existing systems remotely controlled by joystick, head- or eye tracking, the proposed ID-SIR system directly acquires the intention from users' brain. Compared with the state-of-art system only working for a specific object in a fixed place, the designed ID-SIR system can grasp any desired object in a random place chosen by a user and deliver it to his/her mouth automatically. As one of the main advantages of the ID-SIR system, the patient is only required to send one intention command for one drinking task and the autonomous robot would finish the rest of specific controlling tasks, which greatly eases the burden on patients. Eight healthy subjects attended our experiment, which contained 10 tasks for each subject. In each task, the proposed ID-SIR system delivered the desired beverage container to the mouth of the subject and then put it back to the original position. The mean accuracy of the eight subjects was 97.5%, which demonstrated the effectiveness of the ID-SIR system.

Thermal oxidation induced degradation of carbon fiber reinforced composites and carbon nanotube sheet enhanced fiber/matrix interface for high temperature aerospace structural applications

NASA Astrophysics Data System (ADS)

Haque, Mohammad Hamidul

Recent increase in the use of carbon fiber reinforced polymer matrix composite, especially for high temperature applications in aerospace primary and secondary structures along with wind energy and automotive industries, have generated new challenges to predict its failure mechanisms and service life. This dissertation reports the experimental study of a unidirectional carbon fiber reinforced bismaleimide (BMI) composites (CFRC), an excellent candidate for high temperature aerospace components, undergoing thermal oxidation at 260 °C in air for over 3000 hours. The key focus of the work is to investigate the mechanical properties of the carbon fiber BMI composite subjected to thermal aging in three key aspects - first, studying its bulk flexural properties (in macro scale), second, characterizing the crack propagation along the fiber direction, representing the interfacial bonding strength between fiber and matrix (in micro scale), and third, introducing nano-structured materials to modify the interface (in nano scale) between the carbon fiber and BMI resin and mechanical characterization to study its influence on mitigating the aging effect. Under the first category, weight loss and flexural properties have been monitored as the oxidation propagates through the fiber/matrix interface. Dynamic mechanical analysis and micro-computed tomography analysis have been performed to analyze the aging effects. In the second category, the long-term effects of thermal oxidation on the delamination (between the composite plies) and debonding (between fiber and matrix) type fracture toughness have been characterized by preparing two distinct types of double cantilever beam specimens. Digital image correlation has been used to determine the deformation field and strain distribution around the crack propagation path. Finally the resin system and the fiber/matrix interface have been modified using nanomaterials to mitigate the degradations caused by oxidation. Nanoclay modified epoxy resin has been characterized for hardness and modulus using nanoindentation technique. A significant reduction of oxidation, which is anticipated to eventually translate into improvement in mechanical properties, has been observed as the nanoclay particles have worked as a retarding agent for the oxidation propagation. Carbon nanotube sheet scrolled carbon fiber tows embedded in epoxy matrix have been investigated for interfacial properties using nanoindentation (push-out test), in micro scale, and using tensile testing (pull-out test), in macro scale. A significant increase in interfacial shear strength has been achieved by this unique materials combination.

A Python Interface for the Dakota Iterative Systems Analysis Toolkit

NASA Astrophysics Data System (ADS)

Piper, M.; Hutton, E.; Syvitski, J. P.

2016-12-01

Uncertainty quantification is required to improve the accuracy, reliability, and accountability of Earth science models. Dakota is a software toolkit, developed at Sandia National Laboratories, that provides an interface between models and a library of analysis methods, including support for sensitivity analysis, uncertainty quantification, optimization, and calibration techniques. Dakota is a powerful tool, but its learning curve is steep: the user not only must understand the structure and syntax of the Dakota input file, but also must develop intermediate code, called an analysis driver, that allows Dakota to run a model. The CSDMS Dakota interface (CDI) is a Python package that wraps and extends Dakota's user interface. It simplifies the process of configuring and running a Dakota experiment. A user can program to the CDI, allowing a Dakota experiment to be scripted. The CDI creates Dakota input files and provides a generic analysis driver. Any model written in Python that exposes a Basic Model Interface (BMI), as well as any model componentized in the CSDMS modeling framework, automatically works with the CDI. The CDI has a plugin architecture, so models written in other languages, or those that don't expose a BMI, can be accessed by the CDI by programmatically extending a template; an example is provided in the CDI distribution. Currently, six Dakota analysis methods have been implemented for examples from the much larger Dakota library. To demonstrate the CDI, we performed an uncertainty quantification experiment with the HydroTrend hydrological water balance and transport model. In the experiment, we evaluated the response of long-term suspended sediment load at the river mouth (Qs) to uncertainty in two input parameters, annual mean temperature (T) and precipitation (P), over a series of 100-year runs, using the polynomial chaos method. Through Dakota, we calculated moments, local and global (Sobol') sensitivity indices, and probability density and cumulative distribution functions for the response.

Humans and machines in space: The vision, the challenge, the payoff; AAS Goddard Memorial Symposium, 29th, Washington, DC, March 14-15, 1991

NASA Astrophysics Data System (ADS)

Johnson, Bradley; May, Gayle L.; Korn, Paula

A recent symposium produced papers in the areas of solar system exploration, man machine interfaces, cybernetics, virtual reality, telerobotics, life support systems and the scientific and technology spinoff from the NASA space program. A number of papers also addressed the social and economic impacts of the space program. For individual titles, see A95-87468 through A95-87479.

Direct Digital Manufacturing of Integrated Naval Systems Using Ultrasonic Consolidation, Support Material Deposition and Direct Write Technologies

DTIC Science & Technology

2012-02-17

tool should be combined with a user-friendly Windows-based software interface that utilizes the best practices for process planning developed by us and...best practices developed through this project, resulting in the commercial availability of machines for the Navy and others. These machines will...research 2011 Outstanding Paper Award, VRAP 2011, for paper "Some Studies on Dislocation Density based Finite Element Modeling of Ultrasonic

Strategic Studies Quarterly. Volume 7, Number 4. Winter 2013

DTIC Science & Technology

2013-01-01

databases to bridge the man-machine interface, thereby mak- ing both machines and man more capable of complex thought, independent assessment, and...Edward, “China Steps up Effort to Diversify FX Reserves,” Re- uters, 13 January 2013, http://www.reuters.com/article/2013/01/14/us-china- forex ...of attacks in Israel, Russia, and the United States from 1989 to 2008 (see fig. 2). The analysis combines data from the Global Terrorism Database

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Ellerbrock, Philip J. (Inventor); Winkelmann, Joseph P. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor)

2006-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface is a state machine, such as an ASIC, that operates independent of a processor in communicating with the bus controller and data channels.

A Toolkit for Designing User Interfaces

DTIC Science & Technology

1990-03-01

as the NPS IB can provide prototyping capability. Interface generators are available commercially for nearly every computing machine on the market ...structure which holds attributes of the message buffer window is shown in Figure 4.2. The variables nlines and nchars hold the number of lines in the...window its appearance of scrolling 46 /* define a type and structure for the message buffer */ struct messbuf( long nlines ; /* number of lines in the

[A novel serial port auto trigger system for MOSFET dose acquisition].

PubMed

Luo, Guangwen; Qi, Zhenyu

2013-01-01

To synchronize the radiation of microSelectron-HDR (Nucletron afterloading machine) and measurement of MOSFET dose system, a trigger system based on interface circuit was designed and corresponding monitor and trigger program were developed on Qt platform. This interface and control system was tested and showed stable operate and reliable work. This adopted serial port detect technique may expand to trigger application of other medical devices.

2D or not 2D? The impact of nanoscale roughness and substrate interactions on the tribological properties of graphene and MoS2

NASA Astrophysics Data System (ADS)

Elinski, Meagan B.; Liu, Zhuotong; Spear, Jessica C.; Batteas, James D.

2017-03-01

The use of 2D nanomaterials for controlling friction and wear at interfaces has received increased attention over the past few years due to their unique structural, thermal, electrical and mechanical properties. These materials proffer potential critical solutions to challenges in boundary lubrication across numerous platforms ranging from engines, to biomedical implants and micro- and nano-scaled machines that will play a major role in the Internet of Things. There has been significant work on a range of 2D nanomaterials, such as graphene and molybdenum disulfide (MoS2). From these studies, their frictional properties have been shown to be highly dependent on numerous factors, such as substrate structure, strain, and competing chemical interactions between the interfaces in sliding contact. Moreover, when considering real contacts in machined interfaces, these surfaces are often composed of nanoscaled asperities, whose intermittent contact dominates the tribochemical processes that result in wear. In this review we aim to capture recent work on the tribological properties of graphene and MoS2 and to discuss the impacts of surface roughness (from the atomic scale to the nanoscale) and chemical interactions at interfaces on their frictional properties, and their use in designing advanced boundary lubrication schemes.

Microgripper construction kit

NASA Astrophysics Data System (ADS)

Gengenbach, Ulrich K.; Hofmann, Andreas; Engelhardt, Friedhelm; Scharnowell, Rudolf; Koehler, Bernd

2001-10-01

A large number of microgrippers has been developed in industry and academia. Although the importance of hybrid integration techniques and hence the demand for assembly tools grows continuously a large part of these developments has not yet been used in industrial production. The first grippers developed for microassembly were basically vacuum grippers and downscaled tweezers. Due to increasingly complex assembly tasks more and more functionality such as sensing or additional functions such as adhesive dispensing has been integrated into gripper systems over the last years. Most of these gripper systems are incompatible since there exists no standard interface to the assembly machine and no standard for the internal modules and interfaces. Thus these tools are not easily interchangeable between assembly machines and not easily adaptable to assembly tasks. In order to alleviate this situation a construction kit for modular microgrippers is being developed. It is composed of modules with well defined interfaces that can be combined to build task specific grippers. An abstract model of a microgripper is proposed as a tool to structure the development of the construction kit. The modular concept is illustrated with prototypes.

Damage of actively cooled plasma facing components of magnetic confinement controlled fusion machines

NASA Astrophysics Data System (ADS)

Chevet, G.; Schlosser, J.; Martin, E.; Herb, V.; Camus, G.

2009-03-01

Plasma facing components (PFCs) of magnetic fusion machines have high manufactured residual stresses and have to withstand important stress ranges during operation. These actively cooled PFCs have a carbon fibre composite (CFC) armour and a copper alloy heat sink. Cracks mainly appear in the CFC near the composite/copper interface. In order to analyse damage mechanisms, it is important to well simulate the damage mechanisms both of the CFC and the CFC/Cu interface. This study focuses on the mechanical behaviour of the N11 material for which the scalar ONERA damage model was used. The damage parameters of this model were identified by similarity to a neighbour material, which was extensively analysed, according to the few characterization test results available for the N11. The finite elements calculations predict a high level of damage of the CFC at the interface zone explaining the encountered difficulties in the PFCs fabrication. These results suggest that the damage state of the CFC cells is correlated with a conductivity decrease to explain the temperature increase of the armour surface under fatigue heat load.

OMV mission simulator

NASA Technical Reports Server (NTRS)

Cok, Keith E.

1989-01-01

The Orbital Maneuvering Vehicle (OMV) will be remotely piloted during rendezvous, docking, or proximity operations with target spacecraft from a ground control console (GCC). The real-time mission simulator and graphics being used to design a console pilot-machine interface are discussed. A real-time orbital dynamics simulator drives the visual displays. The dynamics simulator includes a J2 oblate earth gravity model and a generalized 1962 rotating atmospheric and drag model. The simulator also provides a variable-length communication delay to represent use of the Tracking and Data Relay Satellite System (TDRSS) and NASA Communications (NASCOM). Input parameter files determine the graphics display. This feature allows rapid prototyping since displays can be easily modified from pilot recommendations. A series of pilot reviews are being held to determine an effective pilot-machine interface. Pilots fly missions with nominal to 3-sigma dispersions in translational or rotational axes. Console dimensions, switch type and layout, hand controllers, and graphic interfaces are evaluated by the pilots and the GCC simulator is modified for subsequent runs. Initial results indicate a pilot preference for analog versus digital displays and for two 3-degree-of-freedom hand controllers.

CESAR research in intelligent machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weisbin, C.R.

1986-01-01

The Center for Engineering Systems Advanced Research (CESAR) was established in 1983 as a national center for multidisciplinary, long-range research and development in machine intelligence and advanced control theory for energy-related applications. Intelligent machines of interest here are artificially created operational systems that are capable of autonomous decision making and action. The initial emphasis for research is remote operations, with specific application to dexterous manipulation in unstructured dangerous environments where explosives, toxic chemicals, or radioactivity may be present, or in other environments with significant risk such as coal mining or oceanographic missions. Potential benefits include reduced risk to man inmore » hazardous situations, machine replication of scarce expertise, minimization of human error due to fear or fatigue, and enhanced capability using high resolution sensors and powerful computers. A CESAR goal is to explore the interface between the advanced teleoperation capability of today, and the autonomous machines of the future.« less

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

Data mining in bioinformatics using Weka.

PubMed

Frank, Eibe; Hall, Mark; Trigg, Len; Holmes, Geoffrey; Witten, Ian H

2004-10-12

The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.

Natural Language Processing.

ERIC Educational Resources Information Center

Chowdhury, Gobinda G.

2003-01-01

Discusses issues related to natural language processing, including theoretical developments; natural language understanding; tools and techniques; natural language text processing systems; abstracting; information extraction; information retrieval; interfaces; software; Internet, Web, and digital library applications; machine translation for…

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel. PMID:27051414

Development of an automatic subsea blowout preventer stack control system using PLC based SCADA.

PubMed

Cai, Baoping; Liu, Yonghong; Liu, Zengkai; Wang, Fei; Tian, Xiaojie; Zhang, Yanzhen

2012-01-01

An extremely reliable remote control system for subsea blowout preventer stack is developed based on the off-the-shelf triple modular redundancy system. To meet a high reliability requirement, various redundancy techniques such as controller redundancy, bus redundancy and network redundancy are used to design the system hardware architecture. The control logic, human-machine interface graphical design and redundant databases are developed by using the off-the-shelf software. A series of experiments were performed in laboratory to test the subsea blowout preventer stack control system. The results showed that the tested subsea blowout preventer functions could be executed successfully. For the faults of programmable logic controllers, discrete input groups and analog input groups, the control system could give correct alarms in the human-machine interface. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Development of sensitized pick coal interface detector system

NASA Technical Reports Server (NTRS)

Burchill, R. F.

1982-01-01

One approach for detection of the coal interface is measurement of pick cutting loads and shock through the use of pick strain gage load cells and accelerometers. The cutting drum of a long wall mining machine contains a number of cutting picks. In order to measure pick loads and shocks, one pick was instrumented and telemetry used to transmit the signals from the drum to an instrument-type tape recorder. A data system using FM telemetry was designed to transfer cutting bit load and shock information from the drum of a longwall shearer coal mining machine to a chassis mounted data recorder. The design of components in the test data system were finalized, the required instruments were assembled, the instrument system was evaluated in an above-ground simulation test, and an underground test series to obtain tape recorded sensor data was conducted.