Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern.

PubMed

Cheng, Jiao; Jin, Jing; Wang, Xingyu

2017-01-01

Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P ( p < 0.05).

Control of a nursing bed based on a hybrid brain-computer interface.

PubMed

Nengneng Peng; Rui Zhang; Haihua Zeng; Fei Wang; Kai Li; Yuanqing Li; Xiaobin Zhuang

2016-08-01

In this paper, we propose an intelligent nursing bed system which is controlled by a hybrid brain-computer interface (BCI) involving steady-state visual evoked potential (SSVEP) and P300. Specifically, the hybrid BCI includes an asynchronous brain switch based on SSVEP and P300, and a P300-based BCI. The brain switch is used to turn on/off the control system of the electric nursing bed through idle/control state detection, whereas the P300-based BCI is for operating the nursing bed. At the beginning, the user may focus on one group of flashing buttons in the graphic user interface (GUI) of the brain switch, which can simultaneously evoke SSVEP and P300, to switch on the control system. Here, the combination of SSVEP and P300 is used for improving the performance of the brain switch. Next, the user can control the nursing bed using the P300-based BCI. The GUI of the P300-based BCI includes 10 flashing buttons, which correspond to 10 functional operations, namely, left-side up, left-side down, back up, back down, bedpan open, bedpan close, legs up, legs down, right-side up, and right-side down. For instance, he/she can focus on the flashing button "back up" in the GUI of the P300-based BCI to activate the corresponding control such that the nursing bed is adjusted up. Eight healthy subjects participated in our experiment, and obtained an average accuracy of 93.75% and an average false positive rate (FPR) of 0.15 event/min. The effectiveness of our system was thus demonstrated.

Fast attainment of computer cursor control with noninvasively acquired brain signals

NASA Astrophysics Data System (ADS)

Bradberry, Trent J.; Gentili, Rodolphe J.; Contreras-Vidal, José L.

2011-06-01

Brain-computer interface (BCI) systems are allowing humans and non-human primates to drive prosthetic devices such as computer cursors and artificial arms with just their thoughts. Invasive BCI systems acquire neural signals with intracranial or subdural electrodes, while noninvasive BCI systems typically acquire neural signals with scalp electroencephalography (EEG). Some drawbacks of invasive BCI systems are the inherent risks of surgery and gradual degradation of signal integrity. A limitation of noninvasive BCI systems for two-dimensional control of a cursor, in particular those based on sensorimotor rhythms, is the lengthy training time required by users to achieve satisfactory performance. Here we describe a novel approach to continuously decoding imagined movements from EEG signals in a BCI experiment with reduced training time. We demonstrate that, using our noninvasive BCI system and observational learning, subjects were able to accomplish two-dimensional control of a cursor with performance levels comparable to those of invasive BCI systems. Compared to other studies of noninvasive BCI systems, training time was substantially reduced, requiring only a single session of decoder calibration (~20 min) and subject practice (~20 min). In addition, we used standardized low-resolution brain electromagnetic tomography to reveal that the neural sources that encoded observed cursor movement may implicate a human mirror neuron system. These findings offer the potential to continuously control complex devices such as robotic arms with one's mind without lengthy training or surgery.

Brain-computer interface based on intermodulation frequency

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Chen, Zhikai; Gao, Shangkai; Gao, Xiaorong

2013-12-01

Objective. Most recent steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have used a single frequency for each target, so that a large number of targets require a large number of stimulus frequencies and therefore a wider frequency band. However, human beings show good SSVEP responses only in a limited range of frequencies. Furthermore, this issue is especially problematic if the SSVEP-based BCI takes a PC monitor as a stimulator, which is only capable of generating a limited range of frequencies. To mitigate this issue, this study presents an innovative coding method for SSVEP-based BCI by means of intermodulation frequencies. Approach. Simultaneous modulations of stimulus luminance and color at different frequencies were utilized to induce intermodulation frequencies. Luminance flickered at relatively large frequency (10, 12, 15 Hz), while color alternated at low frequency (0.5, 1 Hz). An attractive feature of the proposed method was that it would substantially increase the number of targets at a single flickering frequency by altering color modulated frequencies. Based on this method, the BCI system presented in this study realized eight targets merely using three flickering frequencies. Main results. The online results obtained from 15 subjects (14 healthy and 1 with stroke) revealed that an average classification accuracy of 93.83% and information transfer rate (ITR) of 33.80 bit min-1 were achieved using our proposed SSVEP-based BCI system. Specifically, 5 out of the 15 subjects exhibited an ITR of 40.00 bit min-1 with a classification accuracy of 100%. Significance. These results suggested that intermodulation frequencies could be adopted as steady responses in BCI, for which our system could be used as a practical BCI system.

Classification of binary intentions for individuals with impaired oculomotor function: ‘eyes-closed’ SSVEP-based brain-computer interface (BCI)

NASA Astrophysics Data System (ADS)

Lim, Jeong-Hwan; Hwang, Han-Jeong; Han, Chang-Hee; Jung, Ki-Young; Im, Chang-Hwan

2013-04-01

Objective. Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function. Approach. The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS). Main results. Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min-1. A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%. Significance. The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our ‘eyes-closed’ SSVEP-based BCI system can be potentially used for communication of disabled individuals with impaired oculomotor function.

Classification of binary intentions for individuals with impaired oculomotor function: 'eyes-closed' SSVEP-based brain-computer interface (BCI).

PubMed

Lim, Jeong-Hwan; Hwang, Han-Jeong; Han, Chang-Hee; Jung, Ki-Young; Im, Chang-Hwan

2013-04-01

Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function. The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS). Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min(-1). A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%. The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our 'eyes-closed' SSVEP-based BCI system can be potentially used for communication of disabled individuals with impaired oculomotor function.

An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

NASA Astrophysics Data System (ADS)

Tonin, L.; Leeb, R.; Sobolewski, A.; Millán, J. del R.

2013-10-01

Objective. In this work we present—for the first time—the online operation of an electroencephalogram (EEG) brain-computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI. Such systems are expected to share the ease of use of stimulus-driven BCIs (e.g. P300, steady state visually evoked potential) with the autonomy afforded by decoding voluntary modulations of ongoing activity (e.g. motor imagery). Approach. Eight healthy subjects participated in the study. EEG signals were acquired with an active 64-channel system. The classification method was based on a time-dependent approach tuned to capture the most discriminant spectral features of the temporal evolution of attentional processes. The system was used by all subjects over two days without retraining, to verify its robustness and reliability. Main results. We report a mean online accuracy across the group of 70.6 ± 1.5%, and 88.8 ± 5.8% for the best subject. Half of the participants produced stable features over the entire duration of the study. Additionally, we explain drops in performance in subjects showing stable features in terms of known electrophysiological correlates of fatigue, suggesting the prospect of online monitoring of mental states in BCI systems. Significance. This work represents the first demonstration of the feasibility of an online EEG BCI based on CVSA. The results achieved suggest the CVSA BCI as a promising alternative to standard BCI modalities.

A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation.

PubMed

Coffey, Aodhán L; Leamy, Darren J; Ward, Tomás E

2014-01-01

Commercially available devices for Brain-Computer Interface (BCI)-controlled robotic stroke rehabilitation are prohibitively expensive for many researchers who are interested in the topic and physicians who would utilize such a device. Additionally, they are cumbersome and require a technician to operate, increasing the inaccessibility of such devices for home-based robotic stroke rehabilitation therapy. Presented here is the design, implementation and test of an inexpensive, portable and adaptable BCI-controlled hand therapy device. The system utilizes a soft, flexible, pneumatic glove which can be used to deflect the subject's wrist and fingers. Operation is provided by a custom-designed pneumatic circuit. Air flow is controlled by an embedded system, which receives serial port instruction from a PC running real-time BCI software. System tests demonstrate that glove control can be successfully driven by a real-time BCI. A system such as the one described here may be used to explore closed loop neurofeedback rehabilitation in stroke relatively inexpensively and potentially in home environments.

Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

PubMed

Spüler, Martin; Rosenstiel, Wolfgang; Bogdan, Martin

2012-01-01

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

A Wearable Home BCI system: preliminary results with SSVEP protocol.

PubMed

Piccini, Luca; Parini, Sergio; Maggi, Luca; Andreoni, Giuseppe

2005-01-01

This paper presents and discusses the realization and the performances of a wearable system for EEG-based BCI applications. The system (called Kimera) consists of a two-layer hardware architecture (the wireless acquisition and transmission board based on a Bluetooth ® ARM chip, and a low power miniaturized biosignal acquisition analog front end) together with a software suite (called Bellerophonte) for the Graphic User Interface management, protocol execution, data recording, transmission and processing. The implemented BCI system was based on the SSVEP protocol, applied to a two state selection by using standards display/monitor with a couple of high efficiency LEDs. The frequency features of the signal were computed and used in the intention detection. The BCI algorithm is based on a supervised classifier implemented through a multi-class Canonical Discriminant Analysis (CDA) with a continuous realtime feedback based on the mahalanobis distance parameter. Five healthy subjects participated in the first phase for a preliminary device validation. The obtained results are very interesting and promising, being lined out to the most recent performance reported in literature with a significant improvement both in system and in classification capabilities. The user-friendliness and low cost of the Kimera& Bellerophonte platform make it suitable for the development of home BCI applications.

Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.

PubMed

Chen, Xiaogang; Zhao, Bing; Wang, Yijun; Xu, Shengpu; Gao, Xiaorong

2018-04-12

Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift task without user training. These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.

A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training.

PubMed

Tsui, Chun Sing Louis; Gan, John Q; Roberts, Stephen J

2009-03-01

Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.

Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state.

PubMed

Kaufmann, Tobias; Holz, Elisa M; Kübler, Andrea

2013-01-01

This paper describes a case study with a patient in the classic locked-in state, who currently has no means of independent communication. Following a user-centered approach, we investigated event-related potentials (ERP) elicited in different modalities for use in brain-computer interface (BCI) systems. Such systems could provide her with an alternative communication channel. To investigate the most viable modality for achieving BCI based communication, classic oddball paradigms (1 rare and 1 frequent stimulus, ratio 1:5) in the visual, auditory and tactile modality were conducted (2 runs per modality). Classifiers were built on one run and tested offline on another run (and vice versa). In these paradigms, the tactile modality was clearly superior to other modalities, displaying high offline accuracy even when classification was performed on single trials only. Consequently, we tested the tactile paradigm online and the patient successfully selected targets without any error. Furthermore, we investigated use of the visual or tactile modality for different BCI systems with more than two selection options. In the visual modality, several BCI paradigms were tested offline. Neither matrix-based nor so-called gaze-independent paradigms constituted a means of control. These results may thus question the gaze-independence of current gaze-independent approaches to BCI. A tactile four-choice BCI resulted in high offline classification accuracies. Yet, online use raised various issues. Although performance was clearly above chance, practical daily life use appeared unlikely when compared to other communication approaches (e.g., partner scanning). Our results emphasize the need for user-centered design in BCI development including identification of the best stimulus modality for a particular user. Finally, the paper discusses feasibility of EEG-based BCI systems for patients in classic locked-in state and compares BCI to other AT solutions that we also tested during the study.

Language Model Applications to Spelling with Brain-Computer Interfaces

PubMed Central

Mora-Cortes, Anderson; Manyakov, Nikolay V.; Chumerin, Nikolay; Van Hulle, Marc M.

2014-01-01

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies. PMID:24675760

Spatial-temporal discriminant analysis for ERP-based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Zhao, Qibin; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2013-03-01

Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.

Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications.

PubMed

Panoulas, Konstantinos I; Hadjileontiadis, Leontios J; Panas, Stavros M

2008-01-01

Brain Computer Interfaces (BCI) usually utilize the suppression of mu-rhythm during actual or imagined motor activity. In order to create a BCI system, a signal processing method is required to extract features upon which the discrimination is based. In this article, the Empirical Mode Decomposition along with the Hilbert-Huang Spectrum (HHS) is found to contain the necessary information to be considered as an input to a discriminator. Also, since the HHS defines amplitude and instantaneous frequency for each sample, it can be used for an online BCI system. Experimental results when the HHS applied to EEG signals from an on-line database (BCI Competition III) show the potentiality of the proposed analysis to capture the imagined motor activity, contributing to a more enhanced BCI performance.

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

PubMed

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

2017-01-01

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

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

PubMed Central

Gao, Qiang

2017-01-01

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

Attention-level transitory response: a novel hybrid BCI approach

NASA Astrophysics Data System (ADS)

Diez, Pablo F.; Garcés Correa, Agustina; Orosco, Lorena; Laciar, Eric; Mut, Vicente

2015-10-01

Objective. People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the ‘Midas touch effect’, i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Approach. Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. Main results. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min-1 are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. Significance. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

Attention-level transitory response: a novel hybrid BCI approach.

PubMed

Diez, Pablo F; Garcés Correa, Agustina; Orosco, Lorena; Laciar, Eric; Mut, Vicente

2015-10-01

People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the 'Midas touch effect', i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min(-1) are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

L1 norm based common spatial patterns decomposition for scalp EEG BCI.

PubMed

Li, Peiyang; Xu, Peng; Zhang, Rui; Guo, Lanjin; Yao, Dezhong

2013-08-06

Brain computer interfaces (BCI) is one of the most popular branches in biomedical engineering. It aims at constructing a communication between the disabled persons and the auxiliary equipments in order to improve the patients' life. In motor imagery (MI) based BCI, one of the popular feature extraction strategies is Common Spatial Patterns (CSP). In practical BCI situation, scalp EEG inevitably has the outlier and artifacts introduced by ocular, head motion or the loose contact of electrodes in scalp EEG recordings. Because outlier and artifacts are usually observed with large amplitude, when CSP is solved in view of L2 norm, the effect of outlier and artifacts will be exaggerated due to the imposing of square to outliers, which will finally influence the MI based BCI performance. While L1 norm will lower the outlier effects as proved in other application fields like EEG inverse problem, face recognition, etc. In this paper, we present a new CSP implementation using the L1 norm technique, instead of the L2 norm, to solve the eigen problem for spatial filter estimation with aim to improve the robustness of CSP to outliers. To evaluate the performance of our method, we applied our method as well as the standard CSP and the regularized CSP with Tikhonov regularization (TR-CSP), on both the peer BCI dataset with simulated outliers and the dataset from the MI BCI system developed in our group. The McNemar test is used to investigate whether the difference among the three CSPs is of statistical significance. The results of both the simulation and real BCI datasets consistently reveal that the proposed method has much higher classification accuracies than the conventional CSP and the TR-CSP. By combining L1 norm based Eigen decomposition into Common Spatial Patterns, the proposed approach can effectively improve the robustness of BCI system to EEG outliers and thus be potential for the actual MI BCI application, where outliers are inevitably introduced into EEG recordings.

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

An embedded implementation based on adaptive filter bank for brain-computer interface systems.

PubMed

Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui

2018-07-15

Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates. Copyright © 2018 Elsevier B.V. All rights reserved.

DTU BCI speller: an SSVEP-based spelling system with dictionary support.

PubMed

Vilic, Adnan; Kjaer, Troels W; Thomsen, Carsten E; Puthusserypady, S; Sorensen, Helge B D

2013-01-01

In this paper, a new brain computer interface (BCI) speller, named DTU BCI speller, is introduced. It is based on the steady-state visual evoked potential (SSVEP) and features dictionary support. The system focuses on simplicity and user friendliness by using a single electrode for the signal acquisition and displays stimuli on a liquid crystal display (LCD). Nine healthy subjects participated in writing full sentences after a five minutes introduction to the system, and obtained an information transfer rate (ITR) of 21.94 ± 15.63 bits/min. The average amount of characters written per minute (CPM) is 4.90 ± 3.84 with a best case of 8.74 CPM. All subjects reported systematically on different user friendliness measures, and the overall results indicated the potentials of the DTU BCI Speller system. For subjects with high classification accuracies, the introduced dictionary approach greatly reduced the time it took to write full sentences.

Evaluating true BCI communication rate through mutual information and language models.

PubMed

Speier, William; Arnold, Corey; Pouratian, Nader

2013-01-01

Brain-computer interface (BCI) systems are a promising means for restoring communication to patients suffering from "locked-in" syndrome. Research to improve system performance primarily focuses on means to overcome the low signal to noise ratio of electroencephalogric (EEG) recordings. However, the literature and methods are difficult to compare due to the array of evaluation metrics and assumptions underlying them, including that: 1) all characters are equally probable, 2) character selection is memoryless, and 3) errors occur completely at random. The standardization of evaluation metrics that more accurately reflect the amount of information contained in BCI language output is critical to make progress. We present a mutual information-based metric that incorporates prior information and a model of systematic errors. The parameters of a system used in one study were re-optimized, showing that the metric used in optimization significantly affects the parameter values chosen and the resulting system performance. The results of 11 BCI communication studies were then evaluated using different metrics, including those previously used in BCI literature and the newly advocated metric. Six studies' results varied based on the metric used for evaluation and the proposed metric produced results that differed from those originally published in two of the studies. Standardizing metrics to accurately reflect the rate of information transmission is critical to properly evaluate and compare BCI communication systems and advance the field in an unbiased manner.

Prototype of an auto-calibrating, context-aware, hybrid brain-computer interface.

PubMed

Faller, J; Torrellas, S; Miralles, F; Holzner, C; Kapeller, C; Guger, C; Bund, J; Müller-Putz, G R; Scherer, R

2012-01-01

We present the prototype of a context-aware framework that allows users to control smart home devices and to access internet services via a Hybrid BCI system of an auto-calibrating sensorimotor rhythm (SMR) based BCI and another assistive device (Integra Mouse mouth joystick). While there is extensive literature that describes the merit of Hybrid BCIs, auto-calibrating and co-adaptive ERD BCI training paradigms, specialized BCI user interfaces, context-awareness and smart home control, there is up to now, no system that includes all these concepts in one integrated easy-to-use framework that can truly benefit individuals with severe functional disabilities by increasing independence and social inclusion. Here we integrate all these technologies in a prototype framework that does not require expert knowledge or excess time for calibration. In a first pilot-study, 3 healthy volunteers successfully operated the system using input signals from an ERD BCI and an Integra Mouse and reached average positive predictive values (PPV) of 72 and 98% respectively. Based on what we learned here we are planning to improve the system for a test with a larger number of healthy volunteers so we can soon bring the system to benefit individuals with severe functional disability.

Real-time mobile phone dialing system based on SSVEP

NASA Astrophysics Data System (ADS)

Wang, Dongsheng; Kobayashi, Toshiki; Cui, Gaochao; Watabe, Daishi; Cao, Jianting

2017-03-01

Brain computer interface (BCI) systems based on the steady state visual evoked potential (SSVEP) provide higher information transfer rates and require shorter training time than BCI systems using other brain signals. It has been widely used in brain science, rehabilitation engineering, biomedical engineering and intelligent information processing. In this paper, we present a real-time mobile phone dialing system based on SSVEP, and it is more portable than other dialing system because the flashing dial interface is set on a small tablet. With this online BCI system, we can take advantage of this system based on SSVEP to identify the specific frequency on behalf of a number using canonical correlation analysis (CCA) method and dialed out successfully without using any physical movements such as finger tapping. This phone dialing system will be promising to help disable patients to improve the quality of lives.

A Low-Cost EEG System-Based Hybrid Brain-Computer Interface for Humanoid Robot Navigation and Recognition

PubMed Central

Choi, Bongjae; Jo, Sungho

2013-01-01

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system. PMID:24023953

A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.

PubMed

Choi, Bongjae; Jo, Sungho

2013-01-01

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.

Learning to control an SSVEP-based BCI speller in naïve subjects.

PubMed

Zhihua Tang; Yijun Wang; Guoya Dong; Weihua Pei; Hongda Chen

2017-07-01

High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been demonstrated in several recent studies. This study aimed to investigate some issues regarding feasibility of learning to control an SSVEP-based BCI speller in naïve subjects. An experiment with new BCI users was designed to answer the following questions: (1) How many people can use the SSVEP-BCI speller? (2) How much time is required to train the user? (3) Does continuous system use lead to user fatigue and deteriorated BCI performance? The experiment consisted of three tasks including a 40-class BCI spelling task, a psychomotor vigilance test (PVT) task, and a test of sleepiness scale. Subjects' reaction time (RT) in the PVT task and the fatigue rank in the sleepiness scale test were used as objective and subjective parameters to evaluate subjects' alertness level. Among 11 naïve subjects, 10 of them fulfilled the 9-block experiment. Four of them showed clear learning effects (i.e., an increasing trend of classification accuracy and information transfer rate (ITR)) over time. The remaining subjects showed stable BCI performance during the whole experiment. The results of RT and fatigue rank showed a gradually increasing trend, which is not significant across blocks. In summary, the results of this study suggest that controlling an SSVEP-based BCI speller is in general feasible to learn by naïve subjects after a short training procedure, showing no clear performance deterioration related to fatigue.

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.

PubMed

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

2013-09-01

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

Investigating the feasibility of a BCI-driven robot-based writing agent for handicapped individuals

NASA Astrophysics Data System (ADS)

Syan, Chanan S.; Harnarinesingh, Randy E. S.; Beharry, Rishi

2014-07-01

Brain-Computer Interfaces (BCIs) predominantly employ output actuators such as virtual keyboards and wheelchair controllers to enable handicapped individuals to interact and communicate with their environment. However, BCI-based assistive technologies are limited in their application. There is minimal research geared towards granting disabled individuals the ability to communicate using written words. This is a drawback because involving a human attendant in writing tasks can entail a breach of personal privacy where the task entails sensitive and private information such as banking matters. BCI-driven robot-based writing however can provide a safeguard for user privacy where it is required. This study investigated the feasibility of a BCI-driven writing agent using the 3 degree-of- freedom Phantom Omnibot. A full alphanumerical English character set was developed and validated using a teach pendant program in MATLAB. The Omnibot was subsequently interfaced to a P300-based BCI. Three subjects utilised the BCI in the online context to communicate words to the writing robot over a Local Area Network (LAN). The average online letter-wise classification accuracy was 91.43%. The writing agent legibly constructed the communicated letters with minor errors in trajectory execution. The developed system therefore provided a feasible platform for BCI-based writing.

An experimental model of an indigenous BCI based system to help disabled people to communicate

NASA Astrophysics Data System (ADS)

Kabir, Kazi Sadman; Rahman, Chowdhury M. Abid; Farayez, Araf; Ferdous, Mahbuba

2017-12-01

In this paper a Brain Computer Interface (BCI) system has been proposed to help patients suffering from motor disease, paralysis or locked in syndrome to communicate via eye blinking. In this proposed BCI system EEG data is fetched by NeuroSky Headset and then analyzed by the help of WPF (Windows Presentation Foundation) based serial monitor to detect the EEG signal when the eye gives a blink. This detection of eye blinking can be used to select predefined texts and those texts can be converted to speech. The experimental result shows that this system can be used as an effective and efficient tool to communicate through brain.

A high-speed BCI based on code modulation VEP

NASA Astrophysics Data System (ADS)

Bin, Guangyu; Gao, Xiaorong; Wang, Yijun; Li, Yun; Hong, Bo; Gao, Shangkai

2011-04-01

Recently, electroencephalogram-based brain-computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, the low communication speed of current BCI systems greatly limits their practical application. In this paper, we present a high-speed BCI based on code modulation of visual evoked potentials (c-VEP). Thirty-two target stimuli were modulated by a time-shifted binary pseudorandom sequence. A multichannel identification method based on canonical correlation analysis (CCA) was used for target identification. The online system achieved an average information transfer rate (ITR) of 108 ± 12 bits min-1 on five subjects with a maximum ITR of 123 bits min-1 for a single subject.

An Auditory BCI System for Assisting CRS-R Behavioral Assessment in Patients with Disorders of Consciousness

NASA Astrophysics Data System (ADS)

Xiao, Jun; Xie, Qiuyou; He, Yanbin; Yu, Tianyou; Lu, Shenglin; Huang, Ningmeng; Yu, Ronghao; Li, Yuanqing

2016-09-01

The Coma Recovery Scale-Revised (CRS-R) is a consistent and sensitive behavioral assessment standard for disorders of consciousness (DOC) patients. However, the CRS-R has limitations due to its dependence on behavioral markers, which has led to a high rate of misdiagnosis. Brain-computer interfaces (BCIs), which directly detect brain activities without any behavioral expression, can be used to evaluate a patient’s state. In this study, we explored the application of BCIs in assisting CRS-R assessments of DOC patients. Specifically, an auditory passive EEG-based BCI system with an oddball paradigm was proposed to facilitate the evaluation of one item of the auditory function scale in the CRS-R - the auditory startle. The results obtained from five healthy subjects validated the efficacy of the BCI system. Nineteen DOC patients participated in the CRS-R and BCI assessments, of which three patients exhibited no responses in the CRS-R assessment but were responsive to auditory startle in the BCI assessment. These results revealed that a proportion of DOC patients who have no behavioral responses in the CRS-R assessment can generate neural responses, which can be detected by our BCI system. Therefore, the proposed BCI may provide more sensitive results than the CRS-R and thus assist CRS-R behavioral assessments.

An Auditory BCI System for Assisting CRS-R Behavioral Assessment in Patients with Disorders of Consciousness.

PubMed

Xiao, Jun; Xie, Qiuyou; He, Yanbin; Yu, Tianyou; Lu, Shenglin; Huang, Ningmeng; Yu, Ronghao; Li, Yuanqing

2016-09-13

The Coma Recovery Scale-Revised (CRS-R) is a consistent and sensitive behavioral assessment standard for disorders of consciousness (DOC) patients. However, the CRS-R has limitations due to its dependence on behavioral markers, which has led to a high rate of misdiagnosis. Brain-computer interfaces (BCIs), which directly detect brain activities without any behavioral expression, can be used to evaluate a patient's state. In this study, we explored the application of BCIs in assisting CRS-R assessments of DOC patients. Specifically, an auditory passive EEG-based BCI system with an oddball paradigm was proposed to facilitate the evaluation of one item of the auditory function scale in the CRS-R - the auditory startle. The results obtained from five healthy subjects validated the efficacy of the BCI system. Nineteen DOC patients participated in the CRS-R and BCI assessments, of which three patients exhibited no responses in the CRS-R assessment but were responsive to auditory startle in the BCI assessment. These results revealed that a proportion of DOC patients who have no behavioral responses in the CRS-R assessment can generate neural responses, which can be detected by our BCI system. Therefore, the proposed BCI may provide more sensitive results than the CRS-R and thus assist CRS-R behavioral assessments.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms.

PubMed

Athanasiou, Alkinoos; Xygonakis, Ioannis; Pandria, Niki; Kartsidis, Panagiotis; Arfaras, George; Kavazidi, Kyriaki Rafailia; Foroglou, Nicolas; Astaras, Alexander; Bamidis, Panagiotis D

2017-01-01

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

PubMed Central

Xygonakis, Ioannis; Pandria, Niki; Kartsidis, Panagiotis; Arfaras, George; Kavazidi, Kyriaki Rafailia; Foroglou, Nicolas

2017-01-01

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality. PMID:28948168

Effects of Background Music on Objective and Subjective Performance Measures in an Auditory BCI.

PubMed

Zhou, Sijie; Allison, Brendan Z; Kübler, Andrea; Cichocki, Andrzej; Wang, Xingyu; Jin, Jing

2016-01-01

Several studies have explored brain computer interface (BCI) systems based on auditory stimuli, which could help patients with visual impairments. Usability and user satisfaction are important considerations in any BCI. Although background music can influence emotion and performance in other task environments, and many users may wish to listen to music while using a BCI, auditory, and other BCIs are typically studied without background music. Some work has explored the possibility of using polyphonic music in auditory BCI systems. However, this approach requires users with good musical skills, and has not been explored in online experiments. Our hypothesis was that an auditory BCI with background music would be preferred by subjects over a similar BCI without background music, without any difference in BCI performance. We introduce a simple paradigm (which does not require musical skill) using percussion instrument sound stimuli and background music, and evaluated it in both offline and online experiments. The result showed that subjects preferred the auditory BCI with background music. Different performance measures did not reveal any significant performance effect when comparing background music vs. no background. Since the addition of background music does not impair BCI performance but is preferred by users, auditory (and perhaps other) BCIs should consider including it. Our study also indicates that auditory BCIs can be effective even if the auditory channel is simultaneously otherwise engaged.

A self-paced motor imagery based brain-computer interface for robotic wheelchair control.

PubMed

Tsui, Chun Sing Louis; Gan, John Q; Hu, Huosheng

2011-10-01

This paper presents a simple self-paced motor imagery based brain-computer interface (BCI) to control a robotic wheelchair. An innovative control protocol is proposed to enable a 2-class self-paced BCI for wheelchair control, in which the user makes path planning and fully controls the wheelchair except for the automatic obstacle avoidance based on a laser range finder when necessary. In order for the users to train their motor imagery control online safely and easily, simulated robot navigation in a specially designed environment was developed. This allowed the users to practice motor imagery control with the core self-paced BCI system in a simulated scenario before controlling the wheelchair. The self-paced BCI can then be applied to control a real robotic wheelchair using a protocol similar to that controlling the simulated robot. Our emphasis is on allowing more potential users to use the BCI controlled wheelchair with minimal training; a simple 2-class self paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control. Experimental results have demonstrated the usefulness of the online practice under the simulated scenario, and the effectiveness of the proposed self-paced BCI for robotic wheelchair control.

BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?

PubMed

Volosyak, Ivan; Valbuena, Diana; Lüth, Thorsten; Malechka, Tatsiana; Gräser, Axel

2011-06-01

Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

PubMed Central

2012-01-01

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

Beyond intuitive anthropomorphic control: recent achievements using brain computer interface technologies

NASA Astrophysics Data System (ADS)

Pohlmeyer, Eric A.; Fifer, Matthew; Rich, Matthew; Pino, Johnathan; Wester, Brock; Johannes, Matthew; Dohopolski, Chris; Helder, John; D'Angelo, Denise; Beaty, James; Bensmaia, Sliman; McLoughlin, Michael; Tenore, Francesco

2017-05-01

Brain-computer interface (BCI) research has progressed rapidly, with BCIs shifting from animal tests to human demonstrations of controlling computer cursors and even advanced prosthetic limbs, the latter having been the goal of the Revolutionizing Prosthetics (RP) program. These achievements now include direct electrical intracortical microstimulation (ICMS) of the brain to provide human BCI users feedback information from the sensors of prosthetic limbs. These successes raise the question of how well people would be able to use BCIs to interact with systems that are not based directly on the body (e.g., prosthetic arms), and how well BCI users could interpret ICMS information from such devices. If paralyzed individuals could use BCIs to effectively interact with such non-anthropomorphic systems, it would offer them numerous new opportunities to control novel assistive devices. Here we explore how well a participant with tetraplegia can detect infrared (IR) sources in the environment using a prosthetic arm mounted camera that encodes IR information via ICMS. We also investigate how well a BCI user could transition from controlling a BCI based on prosthetic arm movements to controlling a flight simulator, a system with different physical dynamics than the arm. In that test, the BCI participant used environmental information encoded via ICMS to identify which of several upcoming flight routes was the best option. For both tasks, the BCI user was able to quickly learn how to interpret the ICMSprovided information to achieve the task goals.

Remapping cortical modulation for electrocorticographic brain-computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis

NASA Astrophysics Data System (ADS)

Degenhart, Alan D.; Hiremath, Shivayogi V.; Yang, Ying; Foldes, Stephen; Collinger, Jennifer L.; Boninger, Michael; Tyler-Kabara, Elizabeth C.; Wang, Wei

2018-04-01

Objective. Brain-computer interface (BCI) technology aims to provide individuals with paralysis a means to restore function. Electrocorticography (ECoG) uses disc electrodes placed on either the surface of the dura or the cortex to record field potential activity. ECoG has been proposed as a viable neural recording modality for BCI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previously we have demonstrated that a subject with spinal cord injury (SCI) could control an ECoG-based BCI system with up to three degrees of freedom (Wang et al 2013 PLoS One). Here, we expand upon these findings by including brain-control results from two additional subjects with upper-limb paralysis due to amyotrophic lateral sclerosis and brachial plexus injury, and investigate the potential of motor and somatosensory cortical areas to enable BCI control. Approach. Individuals were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for less than 30 d. Subjects were trained to control a BCI by employing a somatotopic control strategy where high-gamma activity from attempted arm and hand movements drove the velocity of a cursor. Main results. Participants were capable of generating robust cortical modulation that was differentiable across attempted arm and hand movements of their paralyzed limb. Furthermore, all subjects were capable of voluntarily modulating this activity to control movement of a computer cursor with up to three degrees of freedom using the somatotopic control strategy. Additionally, for those subjects with electrode coverage of somatosensory cortex, we found that somatosensory cortex was capable of supporting ECoG-based BCI control. Significance. These results demonstrate the feasibility of ECoG-based BCI systems for individuals with paralysis as well as highlight some of the key challenges that must be overcome before such systems are translated to the clinical realm. ClinicalTrials.gov Identifier: NCT01393444.

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

PubMed

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness

NASA Astrophysics Data System (ADS)

Wang, Fei; He, Yanbin; Qu, Jun; Xie, Qiuyou; Lin, Qing; Ni, Xiaoxiao; Chen, Yan; Pan, Jiahui; Laureys, Steven; Yu, Ronghao; Li, Yuanqing

2017-08-01

Objective. The JFK coma recovery scale-revised (JFK CRS-R), a behavioral observation scale, is widely used in the clinical diagnosis/assessment of patients with disorders of consciousness (DOC). However, the JFK CRS-R is associated with a high rate of misdiagnosis (approximately 40%) because DOC patients cannot provide sufficient behavioral responses. A brain-computer interface (BCI) that detects command/intention-specific changes in electroencephalography (EEG) signals without the need for behavioral expression may provide an alternative method. Approach. In this paper, we proposed an audiovisual BCI communication system based on audiovisual ‘yes’ and ‘no’ stimuli to supplement the JFK CRS-R for assessing the communication ability of DOC patients. Specifically, patients were given situation-orientation questions as in the JFK CRS-R and instructed to select the answers using the BCI. Main results. Thirteen patients (eight vegetative state (VS) and five minimally conscious state (MCS)) participated in our experiments involving both the BCI- and JFK CRS-R-based assessments. One MCS patient who received a score of 1 in the JFK CRS-R achieved an accuracy of 86.5% in the BCI-based assessment. Seven patients (four VS and three MCS) obtained unresponsive results in the JFK CRS-R-based assessment but responsive results in the BCI-based assessment, and 4 of those later improved scores in the JFK CRS-R-based assessment. Five patients (four VS and one MCS) obtained usresponsive results in both assessments. Significance. The experimental results indicated that the audiovisual BCI could provide more sensitive results than the JFK CRS-R and therefore supplement the JFK CRS-R.

Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness.

PubMed

Wang, Fei; He, Yanbin; Qu, Jun; Xie, Qiuyou; Lin, Qing; Ni, Xiaoxiao; Chen, Yan; Pan, Jiahui; Laureys, Steven; Yu, Ronghao; Li, Yuanqing

2017-08-01

The JFK coma recovery scale-revised (JFK CRS-R), a behavioral observation scale, is widely used in the clinical diagnosis/assessment of patients with disorders of consciousness (DOC). However, the JFK CRS-R is associated with a high rate of misdiagnosis (approximately 40%) because DOC patients cannot provide sufficient behavioral responses. A brain-computer interface (BCI) that detects command/intention-specific changes in electroencephalography (EEG) signals without the need for behavioral expression may provide an alternative method. In this paper, we proposed an audiovisual BCI communication system based on audiovisual 'yes' and 'no' stimuli to supplement the JFK CRS-R for assessing the communication ability of DOC patients. Specifically, patients were given situation-orientation questions as in the JFK CRS-R and instructed to select the answers using the BCI. Thirteen patients (eight vegetative state (VS) and five minimally conscious state (MCS)) participated in our experiments involving both the BCI- and JFK CRS-R-based assessments. One MCS patient who received a score of 1 in the JFK CRS-R achieved an accuracy of 86.5% in the BCI-based assessment. Seven patients (four VS and three MCS) obtained unresponsive results in the JFK CRS-R-based assessment but responsive results in the BCI-based assessment, and 4 of those later improved scores in the JFK CRS-R-based assessment. Five patients (four VS and one MCS) obtained usresponsive results in both assessments. The experimental results indicated that the audiovisual BCI could provide more sensitive results than the JFK CRS-R and therefore supplement the JFK CRS-R.

Classification of Non-Time-Locked Rapid Serial Visual Presentation Events for Brain-Computer Interaction Using Deep Learning

DTIC Science & Technology

2014-07-08

internction ( BCI ) system allows h uman subjects to communicate with or control an extemal device with their brain signals [1], or to use those brain...signals to interact with computers, environments, or even other humans [2]. One application of BCI is to use brnin signals to distinguish target...images within a large collection of non-target images [2]. Such BCI -based systems can drastically increase the speed of target identification in

Robot Control Through Brain Computer Interface For Patterns Generation

NASA Astrophysics Data System (ADS)

Belluomo, P.; Bucolo, M.; Fortuna, L.; Frasca, M.

2011-09-01

A Brain Computer Interface (BCI) system processes and translates neuronal signals, that mainly comes from EEG instruments, into commands for controlling electronic devices. This system can allow people with motor disabilities to control external devices through the real-time modulation of their brain waves. In this context an EEG-based BCI system that allows creative luminous artistic representations is here presented. The system that has been designed and realized in our laboratory interfaces the BCI2000 platform performing real-time analysis of EEG signals with a couple of moving luminescent twin robots. Experiments are also presented.

An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials.

PubMed

Li, Rui; Zhang, Xiaodong; Li, Hanzhe; Zhang, Liming; Lu, Zhufeng; Chen, Jiangcheng

2018-08-01

Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ± 2.64 dB, versus 3.38 ± 0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ± 7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ± 3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Classification of unconscious like/dislike decisions: First results towards a novel application for BCI technology.

PubMed

Wriessnegger, S C; Hackhofer, D; Muller-Putz, G R

2015-01-01

More and more applications for BCI technology emerge that are not restricted to communication or control, like gaming, rehabilitation, Neuro-IS research, neuro-economics or security. In this context a so called passive BCI, a system that derives its outputs from arbitrary brain activity for enriching a human-machine interaction with implicit information on the actual user state will be used. Concretely EEG-based BCI technology enables the use of signals related to attention, intentions and mental state, without relying on indirect measures based on overt behavior or other physiological signals which is an important point e.g. in Neuromarketing research. The scope of this pilot EEG-study was to detect like/dislike decisions on car stimuli just by means of ERP analysis. Concretely to define user preferences concerning different car designs by implementing an offline BCI based on shrinkage LDA classification. Although classification failed in the majority of participants the elicited early (sub) conscious ERP components reflect user preferences for cars. In a broader sense this study should pave the way towards a "product design BCI" suitable for neuromarketing research.

A fresh look at functional link neural network for motor imagery-based brain-computer interface.

PubMed

Hettiarachchi, Imali T; Babaei, Toktam; Nguyen, Thanh; Lim, Chee P; Nahavandi, Saeid

2018-05-04

Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons per layer, the ANN can act as a universal approximator. However, due to the low signal-to-noise ratio of EEG signal data, overtraining problem may become an inherent issue, causing these universal approximators to fail in real-time applications. In this study we introduce a higher order neural network, namely the functional link neural network (FLNN) as a classifier for motor imagery (MI)-based BCI systems, to remedy the drawbacks in MLP. We compare the proposed method with competing classifiers such as linear decomposition analysis, naïve Bayes, k-nearest neighbours, support vector machine and three MLP architectures. Two multi-class benchmark datasets from the BCI competitions are used. Common spatial pattern algorithm is utilized for feature extraction to build classification models. FLNN reports the highest average Kappa value over multiple subjects for both the BCI competition datasets, under similarly preprocessed data and extracted features. Further, statistical comparison results over multiple subjects show that the proposed FLNN classification method yields the best performance among the competing classifiers. Findings from this study imply that the proposed method, which has less computational complexity compared to the MLP, can be implemented effectively in practical MI-based BCI systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar

NASA Astrophysics Data System (ADS)

Phat Luu, Trieu; He, Yongtian; Brown, Samuel; Nakagome, Sho; Contreras-Vidal, Jose L.

2016-06-01

Objective. The control of human bipedal locomotion is of great interest to the field of lower-body brain-computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences. Approach. In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Main results. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson’s r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31 Knee: 0.23 ± 0.33 Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24 Knee: 0.55 ± 0.20 Ankle: 0.29 ± 0.22) on Day 8. Significance. These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.

The Effects of Working Memory on Brain-Computer Interface Performance

PubMed Central

Sprague, Samantha A.; McBee, Matthew; Sellers, Eric W.

2015-01-01

Objective The purpose of the present study is to evaluate the relationship between working memory and BCI performance. Methods Participants took part in two separate sessions. The first session consisted of three computerized tasks. The LSWM was used to measure working memory, the TPVT was used to measure general intelligence, and the DCCS was used to measure executive function, specifically cognitive flexibility. The second session consisted of a P300-based BCI copy-spelling task. Results The results indicate that both working memory and general intelligence are significant predictors of BCI performance. Conclusions This suggests that working memory training could be used to improve performance on a BCI task. Significance Working memory training may help to reduce a portion of the individual differences that exist in BCI performance allowing for a wider range of users to successfully operate the BCI system as well as increase the BCI performance of current users. PMID:26620822

The effects of semantic congruency: a research of audiovisual P300-speller.

PubMed

Cao, Yong; An, Xingwei; Ke, Yufeng; Jiang, Jin; Yang, Hanjun; Chen, Yuqian; Jiao, Xuejun; Qi, Hongzhi; Ming, Dong

2017-07-25

Over the past few decades, there have been many studies of aspects of brain-computer interface (BCI). Of particular interests are event-related potential (ERP)-based BCI spellers that aim at helping mental typewriting. Nowadays, audiovisual unimodal stimuli based BCI systems have attracted much attention from researchers, and most of the existing studies of audiovisual BCIs were based on semantic incongruent stimuli paradigm. However, no related studies had reported that whether there is difference of system performance or participant comfort between BCI based on semantic congruent paradigm and that based on semantic incongruent paradigm. The goal of this study was to investigate the effects of semantic congruency in system performance and participant comfort in audiovisual BCI. Two audiovisual paradigms (semantic congruent and incongruent) were adopted, and 11 healthy subjects participated in the experiment. High-density electrical mapping of ERPs and behavioral data were measured for the two stimuli paradigms. The behavioral data indicated no significant difference between congruent and incongruent paradigms for offline classification accuracy. Nevertheless, eight of the 11 participants reported their priority to semantic congruent experiment, two reported no difference between the two conditions, and only one preferred the semantic incongruent paradigm. Besides, the result indicted that higher amplitude of ERP was found in incongruent stimuli based paradigm. In a word, semantic congruent paradigm had a better participant comfort, and maintained the same recognition rate as incongruent paradigm. Furthermore, our study suggested that the paradigm design of spellers must take both system performance and user experience into consideration rather than merely pursuing a larger ERP response.

Fusion with Language Models Improves Spelling Accuracy for ERP-based Brain Computer Interface Spellers

PubMed Central

Orhan, Umut; Erdogmus, Deniz; Roark, Brian; Purwar, Shalini; Hild, Kenneth E.; Oken, Barry; Nezamfar, Hooman; Fried-Oken, Melanie

2013-01-01

Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system. PMID:22255652

Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment.

PubMed

Li, Ting; Zhang, Jinhua; Xue, Tao; Wang, Baozeng

2017-01-01

We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player's BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player's event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players' scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.

Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game

PubMed Central

de Carvalho, Sarah Negreiros; Costa, Thiago Bulhões da Silva; Attux, Romis; Hornung, Heiko Horst; Arantes, Dalton Soares

2018-01-01

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named “Get Coins,” through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user. PMID:29849549

Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game.

PubMed

Leite, Harlei Miguel de Arruda; de Carvalho, Sarah Negreiros; Costa, Thiago Bulhões da Silva; Attux, Romis; Hornung, Heiko Horst; Arantes, Dalton Soares

2018-01-01

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named "Get Coins," through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

PubMed Central

Lee, Yushin; Yun, Myung Hwan

2017-01-01

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation. PMID:28453547

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.

PubMed

Choi, Inchul; Rhiu, Ilsun; Lee, Yushin; Yun, Myung Hwan; Nam, Chang S

2017-01-01

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.

User-centered design in brain-computer interfaces-a case study.

PubMed

Schreuder, Martijn; Riccio, Angela; Risetti, Monica; Dähne, Sven; Ramsay, Andrew; Williamson, John; Mattia, Donatella; Tangermann, Michael

2013-10-01

The array of available brain-computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and - if necessary - adapted to him/her until a suitable BCI system is found. Described is the case of a 48-year-old woman who suffered from an ischemic brain stem stroke, leading to a severe motor- and communication deficit. She was enrolled in studies with two different BCI systems before a suitable system was found. The first was an auditory event-related potential (ERP) paradigm and the second a visual ERP paradigm, both of which are established in literature. The auditory paradigm did not work successfully, despite favorable preconditions. The visual paradigm worked flawlessly, as found over several sessions. This discrepancy in performance can possibly be explained by the user's clinical deficit in several key neuropsychological indicators, such as attention and working memory. While the auditory paradigm relies on both categories, the visual paradigm could be used with lower cognitive workload. Besides attention and working memory, several other neurophysiological and -psychological indicators - and the role they play in the BCIs at hand - are discussed. The user's performance on the first BCI paradigm would typically have excluded her from further ERP-based BCI studies. However, this study clearly shows that, with the numerous paradigms now at our disposal, the pursuit for a functioning BCI system should not be stopped after an initial failed attempt. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Coherent Activity in Bilateral Parieto-Occipital Cortices during P300-BCI Operation.

PubMed

Takano, Kouji; Ora, Hiroki; Sekihara, Kensuke; Iwaki, Sunao; Kansaku, Kenji

2014-01-01

The visual P300 brain-computer interface (BCI), a popular system for electroencephalography (EEG)-based BCI, uses the P300 event-related potential to select an icon arranged in a flicker matrix. In earlier studies, we used green/blue (GB) luminance and chromatic changes in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray (WG) luminance flicker matrix. To highlight areas involved in improved P300-BCI performance, we used simultaneous EEG-fMRI recordings and showed enhanced activities in bilateral and right lateralized parieto-occipital areas. Here, to capture coherent activities of the areas during P300-BCI, we collected whole-head 306-channel magnetoencephalography data. When comparing functional connectivity between the right and left parieto-occipital channels, significantly greater functional connectivity in the alpha band was observed under the GB flicker matrix condition than under the WG flicker matrix condition. Current sources were estimated with a narrow-band adaptive spatial filter, and mean imaginary coherence was computed in the alpha band. Significantly greater coherence was observed in the right posterior parietal cortex under the GB than under the WG condition. Re-analysis of previous EEG-based P300-BCI data showed significant correlations between the power of the coherence of the bilateral parieto-occipital cortices and their performance accuracy. These results suggest that coherent activity in the bilateral parieto-occipital cortices plays a significant role in effectively driving the P300-BCI.

Speaking and cognitive distractions during EEG-based brain control of a virtual neuroprosthesis-arm.

PubMed

Foldes, Stephen T; Taylor, Dawn M

2013-12-21

Brain-computer interface (BCI) systems have been developed to provide paralyzed individuals the ability to command the movements of an assistive device using only their brain activity. BCI systems are typically tested in a controlled laboratory environment were the user is focused solely on the brain-control task. However, for practical use in everyday life people must be able to use their brain-controlled device while mentally engaged with the cognitive responsibilities of daily activities and while compensating for any inherent dynamics of the device itself. BCIs that use electroencephalography (EEG) for movement control are often assumed to require significant mental effort, thus preventing users from thinking about anything else while using their BCI. This study tested the impact of cognitive load as well as speaking on the ability to use an EEG-based BCI. Six participants controlled the two-dimensional (2D) movements of a simulated neuroprosthesis-arm under three different levels of cognitive distraction. The two higher cognitive load conditions also required simultaneously speaking during BCI use. On average, movement performance declined during higher levels of cognitive distraction, but only by a limited amount. Movement completion time increased by 7.2%, the percentage of targets successfully acquired declined by 11%, and path efficiency declined by 8.6%. Only the decline in percentage of targets acquired and path efficiency were statistically significant (p < 0.05). People who have relatively good movement control of an EEG-based BCI may be able to speak and perform other cognitively engaging activities with only a minor drop in BCI-control performance.

Performance variation in motor imagery brain-computer interface: a brief review.

PubMed

Ahn, Minkyu; Jun, Sung Chan

2015-03-30

Brain-computer interface (BCI) technology has attracted significant attention over recent decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in that performance varies greatly across and even within subjects, an obstacle that degrades the reliability of BCI systems. Understanding the causes of these problems is important if we are to create more stable systems. In this short review, we report the most recent studies and findings on performance variation, especially in motor imagery-based BCI, which has found that low-performance groups have a less-developed brain network that is incapable of motor imagery. Further, psychological and physiological states influence performance variation within subjects. We propose a possible strategic approach to deal with this variation, which may contribute to improving the reliability of BCI. In addition, the limitations of current work and opportunities for future studies are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.

PubMed

Guger, C; Schlögl, A; Walterspacher, D; Pfurtscheller, G

1999-01-01

An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.

A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface.

PubMed

Cavrini, Francesco; Bianchi, Luigi; Quitadamo, Lucia Rita; Saggio, Giovanni

2016-01-01

We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.

Near infrared spectroscopy based brain-computer interface

NASA Astrophysics Data System (ADS)

Ranganatha, Sitaram; Hoshi, Yoko; Guan, Cuntai

2005-04-01

A brain-computer interface (BCI) provides users with an alternative output channel other than the normal output path of the brain. BCI is being given much attention recently as an alternate mode of communication and control for the disabled, such as patients suffering from Amyotrophic Lateral Sclerosis (ALS) or "locked-in". BCI may also find applications in military, education and entertainment. Most of the existing BCI systems which rely on the brain's electrical activity use scalp EEG signals. The scalp EEG is an inherently noisy and non-linear signal. The signal is detrimentally affected by various artifacts such as the EOG, EMG, ECG and so forth. EEG is cumbersome to use in practice, because of the need for applying conductive gel, and the need for the subject to be immobile. There is an urgent need for a more accessible interface that uses a more direct measure of cognitive function to control an output device. The optical response of Near Infrared Spectroscopy (NIRS) denoting brain activation can be used as an alternative to electrical signals, with the intention of developing a more practical and user-friendly BCI. In this paper, a new method of brain-computer interface (BCI) based on NIRS is proposed. Preliminary results of our experiments towards developing this system are reported.

A visual parallel-BCI speller based on the time-frequency coding strategy.

PubMed

Xu, Minpeng; Chen, Long; Zhang, Lixin; Qi, Hongzhi; Ma, Lan; Tang, Jiabei; Wan, Baikun; Ming, Dong

2014-04-01

Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min(-1), with an average of 54.0 bit min(-1) and 43.0 bit min(-1) in the three rounds and five rounds, respectively. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.

Effects of Continuous Kinaesthetic Feedback Based on Tendon Vibration on Motor Imagery BCI Performance.

PubMed

Barsotti, Michele; Leonardis, Daniele; Vanello, Nicola; Bergamasco, Massimo; Frisoli, Antonio

2018-01-01

Feedback plays a crucial role for using brain computer interface systems. This paper proposes the use of vibration-evoked kinaesthetic illusions as part of a novel multisensory feedback for a motor imagery (MI)-based BCI and investigates its contributions in terms of BCI performance and electroencephalographic (EEG) correlates. sixteen subjects performed two different right arm MI-BCI sessions: with the visual feedback only and with both visual and vibration-evoked kinaesthetic feedback, conveyed by the stimulation of the biceps brachi tendon. In both conditions, the sensory feedback was driven by the MI-BCI. The rich and more natural multisensory feedback was expected to facilitate the execution of MI, and thus to improve the performance of the BCI. The EEG correlates of the proposed feedback were also investigated with and without the performing of MI. the contribution of vibration-evoked kinaesthetic feedback led to statistically higher BCI performance (Anova, F (1,14) = 18.1, p < .01) and more stable EEG event-related-desynchronization. Obtained results suggest promising application of the proposed method in neuro-rehabilitation scenarios: the advantage of an improved usability could make the MI-BCIs more applicable for those patients having difficulties in performing kinaesthetic imagery.

A review of classification algorithms for EEG-based brain-computer interfaces.

PubMed

Lotte, F; Congedo, M; Lécuyer, A; Lamarche, F; Arnaldi, B

2007-06-01

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

Hybrid P300-based brain-computer interface to improve usability for people with severe motor disability: electromyographic signals for error correction during a spelling task.

PubMed

Riccio, Angela; Holz, Elisa Mira; Aricò, Pietro; Leotta, Francesco; Aloise, Fabio; Desideri, Lorenzo; Rimondini, Matteo; Kübler, Andrea; Mattia, Donatella; Cincotti, Febo

2015-03-01

To evaluate the impact of a hybrid control on usability of a P300-based brain-computer interface (BCI) system that was designed to control an assistive technology software and was integrated with an electromyographic channel for error correction. Proof-of-principle study with a convenience sample. Neurologic rehabilitation hospital. Participants (N=11) in this pilot study included healthy (n=8) and severely motor impaired (n=3) persons. The 3 people with severe motor disability were identified as potential candidates to benefit from the proposed hybrid BCI system for communication and environmental interaction. To eventually investigate the improvement in usability, we compared 2 modalities of BCI system control: a P300-based and a hybrid P300 electromyographic-based mode of control. System usability was evaluated according to the following outcome measures within 3 domains: (1) effectiveness (overall system accuracy and P300-based BCI accuracy); (2) efficiency (throughput time and users' workload); and (3) satisfaction (users' satisfaction). We also considered the information transfer rate and time for selection. Findings obtained in healthy participants were in favor of a higher usability of the hybrid control as compared with the nonhybrid. A similar trend was indicated by the observational results gathered from each of the 3 potential end-users. The proposed hybrid BCI control modality could provide end-users with severe motor disability with an option to exploit some residual muscular activity, which could not be fully reliable for properly controlling an assistive technology device. The findings reported in this pilot study encourage the implementation of a clinical trial involving a large cohort of end-users. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A novel hybrid auditory BCI paradigm combining ASSR and P300.

PubMed

Kaongoen, Netiwit; Jo, Sungho

2017-03-01

Brain-computer interface (BCI) is a technology that provides an alternative way of communication by translating brain activities into digital commands. Due to the incapability of using the vision-dependent BCI for patients who have visual impairment, auditory stimuli have been used to substitute the conventional visual stimuli. This paper introduces a hybrid auditory BCI that utilizes and combines auditory steady state response (ASSR) and spatial-auditory P300 BCI to improve the performance for the auditory BCI system. The system works by simultaneously presenting auditory stimuli with different pitches and amplitude modulation (AM) frequencies to the user with beep sounds occurring randomly between all sound sources. Attention to different auditory stimuli yields different ASSR and beep sounds trigger the P300 response when they occur in the target channel, thus the system can utilize both features for classification. The proposed ASSR/P300-hybrid auditory BCI system achieves 85.33% accuracy with 9.11 bits/min information transfer rate (ITR) in binary classification problem. The proposed system outperformed the P300 BCI system (74.58% accuracy with 4.18 bits/min ITR) and the ASSR BCI system (66.68% accuracy with 2.01 bits/min ITR) in binary-class problem. The system is completely vision-independent. This work demonstrates that combining ASSR and P300 BCI into a hybrid system could result in a better performance and could help in the development of the future auditory BCI. Copyright © 2017 Elsevier B.V. All rights reserved.

Brain-computer interface on the basis of EEG system Encephalan

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir; Badarin, Artem; Nedaivozov, Vladimir; Kirsanov, Daniil; Hramov, Alexander

2018-04-01

We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.

Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

PubMed

Lin, Yuan-Pin; Wang, Yijun; Jung, Tzyy-Ping

2014-08-09

Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking. This study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment. Despite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s). SSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications.

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

PubMed

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

[Research of controlling of smart home system based on P300 brain-computer interface].

PubMed

Wang, Jinjia; Yang, Chengjie

2014-08-01

Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.

The effects of working memory on brain-computer interface performance.

PubMed

Sprague, Samantha A; McBee, Matthew T; Sellers, Eric W

2016-02-01

The purpose of the present study is to evaluate the relationship between working memory and BCI performance. Participants took part in two separate sessions. The first session consisted of three computerized tasks. The List Sorting Working Memory Task was used to measure working memory, the Picture Vocabulary Test was used to measure general intelligence, and the Dimensional Change Card Sort Test was used to measure executive function, specifically cognitive flexibility. The second session consisted of a P300-based BCI copy-spelling task. The results indicate that both working memory and general intelligence are significant predictors of BCI performance. This suggests that working memory training could be used to improve performance on a BCI task. Working memory training may help to reduce a portion of the individual differences that exist in BCI performance allowing for a wider range of users to successfully operate the BCI system as well as increase the BCI performance of current users. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

PubMed

Lo, Chi-Chun; Chien, Tsung-Yi; Chen, Yu-Chun; Tsai, Shang-Ho; Fang, Wai-Chi; Lin, Bor-Shyh

2016-02-06

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

A hybrid brain-computer interface-based mail client.

PubMed

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method.

A Hybrid Brain-Computer Interface-Based Mail Client

PubMed Central

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method. PMID:23690880

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain computer interface to a virtual reality avatar

PubMed Central

Luu, Trieu Phat; He, Yongtian; Brown, Samuel; Nakagame, Sho; Contreras-Vidal, Jose L.

2017-01-01

Objective The control of human bipedal locomotion is of great interest to the field of lower-body brain computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences. Approach In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1 – 3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated (AM) potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Main results Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson’s r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8. Significance These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system. PMID:27064824

Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar.

PubMed

Luu, Trieu Phat; He, Yongtian; Brown, Samuel; Nakagame, Sho; Contreras-Vidal, Jose L

2016-06-01

The control of human bipedal locomotion is of great interest to the field of lower-body brain-computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences. In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson's r values) in real-time BCI across all subjects increased from (Hip: 0.18 ± 0.31; Knee: 0.23 ± 0.33; Ankle: 0.14 ± 0.22) on Day 1 to (Hip: 0.40 ± 0.24; Knee: 0.55 ± 0.20; Ankle: 0.29 ± 0.22) on Day 8. These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.

Usage of drip drops as stimuli in an auditory P300 BCI paradigm.

PubMed

Huang, Minqiang; Jin, Jing; Zhang, Yu; Hu, Dewen; Wang, Xingyu

2018-02-01

Recently, many auditory BCIs are using beeps as auditory stimuli, while beeps sound unnatural and unpleasant for some people. It is proved that natural sounds make people feel comfortable, decrease fatigue, and improve the performance of auditory BCI systems. Drip drop is a kind of natural sounds that makes humans feel relaxed and comfortable. In this work, three kinds of drip drops were used as stimuli in an auditory-based BCI system to improve the user-friendness of the system. This study explored whether drip drops could be used as stimuli in the auditory BCI system. The auditory BCI paradigm with drip-drop stimuli, which was called the drip-drop paradigm (DP), was compared with the auditory paradigm with beep stimuli, also known as the beep paradigm (BP), in items of event-related potential amplitudes, online accuracies and scores on the likability and difficulty to demonstrate the advantages of DP. DP obtained significantly higher online accuracy and information transfer rate than the BP ( p  < 0.05, Wilcoxon signed test; p  < 0.05, Wilcoxon signed test). Besides, DP obtained higher scores on the likability with no significant difference on the difficulty ( p  < 0.05, Wilcoxon signed test). The results showed that the drip drops were reliable acoustic materials as stimuli in an auditory BCI system.

A survey of the dummy face and human face stimuli used in BCI paradigm.

PubMed

Chen, Long; Jin, Jing; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

2015-01-15

It was proved that the human face stimulus were superior to the flash only stimulus in BCI system. However, human face stimulus may lead to copyright infringement problems and was hard to be edited according to the requirement of the BCI study. Recently, it was reported that facial expression changes could be done by changing a curve in a dummy face which could obtain good performance when it was applied to visual-based P300 BCI systems. In this paper, four different paradigms were presented, which were called dummy face pattern, human face pattern, inverted dummy face pattern and inverted human face pattern, to evaluate the performance of the dummy faces stimuli compared with the human faces stimuli. The key point that determined the value of dummy faces in BCI systems were whether dummy faces stimuli could obtain as good performance as human faces stimuli. Online and offline results of four different paradigms would have been obtained and comparatively analyzed. Online and offline results showed that there was no significant difference among dummy faces and human faces in ERPs, classification accuracy and information transfer rate when they were applied in BCI systems. Dummy faces stimuli could evoke large ERPs and obtain as high classification accuracy and information transfer rate as the human faces stimuli. Since dummy faces were easy to be edited and had no copyright infringement problems, it would be a good choice for optimizing the stimuli of BCI systems. Copyright © 2014 Elsevier B.V. All rights reserved.

Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses

NASA Astrophysics Data System (ADS)

Baek, Hyun Jae; Kim, Hyun Seok; Heo, Jeong; Lim, Yong Gyu; Park, Kwang Suk

2013-04-01

Objective. Brain-computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode-scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment

PubMed Central

Xue, Tao

2017-01-01

We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player's BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player's event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players' scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills. PMID:28572817

A brain computer interface-based explorer.

PubMed

Bai, Lijuan; Yu, Tianyou; Li, Yuanqing

2015-04-15

In recent years, various applications of brain computer interfaces (BCIs) have been studied. In this paper, we present a hybrid BCI combining P300 and motor imagery to operate an explorer. Our system is mainly composed of a BCI mouse, a BCI speller and an explorer. Through this system, the user can access his computer and manipulate (open, close, copy, paste, and delete) files such as documents, pictures, music, movies and so on. The system has been tested with five subjects, and the experimental results show that the explorer can be successfully operated according to subjects' intentions. Copyright © 2014 Elsevier B.V. All rights reserved.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme

PubMed Central

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

PubMed

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

Programming an offline-analyzer of motor imagery signals via python language.

PubMed

Alonso-Valerdi, Luz María; Sepulveda, Francisco

2011-01-01

Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not enough information related to the available programming languages that could be suitable to develop a specific-purpose MI-based BCI. The present paper describes the development of an offline-analysis system based on MI-EEG signals via open-source programming languages, and the assessment of the system using electrical activity recorded from three subjects. The analyzer recognized at least 63% of the MI signals corresponding to three classes. The results of the offline analysis showed a promising performance considering that the subjects have never undergone MI trainings.

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

PubMed

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

A visual parallel-BCI speller based on the time-frequency coding strategy

NASA Astrophysics Data System (ADS)

Xu, Minpeng; Chen, Long; Zhang, Lixin; Qi, Hongzhi; Ma, Lan; Tang, Jiabei; Wan, Baikun; Ming, Dong

2014-04-01

Objective. Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. Approach. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Main results. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min-1, with an average of 54.0 bit min-1 and 43.0 bit min-1 in the three rounds and five rounds, respectively. Significance. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.

An improved P300 pattern in BCI to catch user’s attention

NASA Astrophysics Data System (ADS)

Jin, Jing; Zhang, Hanhan; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej

2017-06-01

Objective. Brain-computer interfaces (BCIs) can help patients who have lost control over most muscles but are still conscious and able to communicate or interact with the environment. One of the most popular types of BCI is the P300-based BCI. With this BCI, users are asked to count the number of appearances of target stimuli in an experiment. To date, the majority of visual P300-based BCI systems developed have used the same character or picture as the target for every stimulus presentation, which can bore users. Consequently, users attention may decrease or be negatively affected by adjacent stimuli. Approach. In this study, a new stimulus is presented to increase user concentration. Honeycomb-shaped figures with 1-3 red dots were used as stimuli. The number and the positions of the red dots in the honeycomb-shaped figure were randomly changed during BCI control. The user was asked to count the number of the dots presented in each flash instead of the number of times they flashed. To assess the performance of this new stimulus, another honeycomb-shaped stimulus, without red dots, was used as a control condition. Main results. The results showed that the honeycomb-shaped stimuli with red dots obtained significantly higher classification accuracies and information transfer rates (p  <  0.05) compared to the honeycomb-shaped stimulus without red dots. Significance. The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.

An improved P300 pattern in BCI to catch user's attention.

PubMed

Jin, Jing; Zhang, Hanhan; Daly, Ian; Wang, Xingyu; Cichocki, Andrzej

2017-06-01

Brain-computer interfaces (BCIs) can help patients who have lost control over most muscles but are still conscious and able to communicate or interact with the environment. One of the most popular types of BCI is the P300-based BCI. With this BCI, users are asked to count the number of appearances of target stimuli in an experiment. To date, the majority of visual P300-based BCI systems developed have used the same character or picture as the target for every stimulus presentation, which can bore users. Consequently, users attention may decrease or be negatively affected by adjacent stimuli. In this study, a new stimulus is presented to increase user concentration. Honeycomb-shaped figures with 1-3 red dots were used as stimuli. The number and the positions of the red dots in the honeycomb-shaped figure were randomly changed during BCI control. The user was asked to count the number of the dots presented in each flash instead of the number of times they flashed. To assess the performance of this new stimulus, another honeycomb-shaped stimulus, without red dots, was used as a control condition. The results showed that the honeycomb-shaped stimuli with red dots obtained significantly higher classification accuracies and information transfer rates (p  <  0.05) compared to the honeycomb-shaped stimulus without red dots. The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.

Application of BCI systems in neurorehabilitation: a scoping review.

PubMed

Bamdad, Mahdi; Zarshenas, Homayoon; Auais, Mohammad A

2015-01-01

To review various types of electroencephalographic activities of the brain and present an overview of brain-computer interface (BCI) systems' history and their applications in rehabilitation. A scoping review of published English literature on BCI application in the field of rehabilitation was undertaken. IEEE Xplore, ScienceDirect, Google Scholar and Scopus databases were searched since inception up to August 2012. All experimental studies published in English and discussed complete cycle of the BCI process was included in the review. In total, 90 articles met the inclusion criteria and were reviewed. Various approaches that improve the accuracy and performance of BCI systems were discussed. Based on BCI's clinical application, reviewed articles were categorized into three groups: motion rehabilitation, speech rehabilitation and virtual reality control (VRC). Almost half of the reviewed papers (48%) concentrated on VRC. Speech rehabilitation and motion rehabilitation made up 33% and 19% of the reviewed papers, respectively. Among different types of electroencephalography signals, P300, steady state visual evoked potentials and motor imagery signals were the most common. This review discussed various applications of BCI in rehabilitation and showed how BCI can be used to improve the quality of life for people with neurological disabilities. It will develop and promote new models of communication and finally, will create an accurate, reliable, online communication between human brain and computer and reduces the negative effects of external stimuli on BCI performance. Implications for Rehabilitation The field of brain-computer interfaces (BCI) is rapidly advancing and it is expected to fulfill a critical role in rehabilitation of neurological disorders and in movement restoration in the forthcoming years. In the near future, BCI has notable potential to become a major tool used by people with disabilities to control locomotion and communicate with surrounding environment and, consequently, improve the quality of life for many affected persons. Electrical field recording at the scalp (i.e. electroencephalography) is the most likely method to be of practical value for clinical use as it is simple and non-invasive. However, some aspects need future improvements, such as the ability to separate close imagery signal (motion of extremities and phalanges that are close together).

A practical, intuitive brain-computer interface for communicating ‘yes’ or ‘no’ by listening

NASA Astrophysics Data System (ADS)

Hill, N. Jeremy; Ricci, Erin; Haider, Sameah; McCane, Lynn M.; Heckman, Susan; Wolpaw, Jonathan R.; Vaughan, Theresa M.

2014-06-01

Objective. Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words ‘yes’ and ‘no’) without loss of performance, and whether the system could be used by people in the locked-in state. Approach. We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions. Main results. The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects’ performance with word stimuli (mean 77% ± 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% ± 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. Significance. Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.

Movement imagery classification in EMOTIV cap based system by Naïve Bayes.

PubMed

Stock, Vinicius N; Balbinot, Alexandre

2016-08-01

Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters. The EPOC electrodes cap is used for EEG acquisition, in two test subjects, for two distinct trial formats. The channels picked are FC5, FC6, P7 and P8 of the 10-20 system, and a discussion about the differences of using C3, C4, P3 and P4 positions is proposed. Dataset 3 of the BCI Competition II is also analyzed using the implemented algorithms. The maximum classification results for the proposed experiment and for the BCI Competition dataset were, respectively, 79% and 85% The conclusion of this study is that the picked positions for electrodes may be applied for BCI systems with satisfactory classification rates.

Investigation of the effect of EEG-BCI on the simultaneous execution of flight simulation and attentional tasks.

PubMed

Vecchiato, Giovanni; Borghini, Gianluca; Aricò, Pietro; Graziani, Ilenia; Maglione, Anton Giulio; Cherubino, Patrizia; Babiloni, Fabio

2016-10-01

Brain-computer interfaces (BCIs) are widely used for clinical applications and exploited to design robotic and interactive systems for healthy people. We provide evidence to control a sensorimotor electroencephalographic (EEG) BCI system while piloting a flight simulator and attending a double attentional task simultaneously. Ten healthy subjects were trained to learn how to manage a flight simulator, use the BCI system, and answer to the attentional tasks independently. Afterward, the EEG activity was collected during a first flight where subjects were required to concurrently use the BCI, and a second flight where they were required to simultaneously use the BCI and answer to the attentional tasks. Results showed that the concurrent use of the BCI system during the flight simulation does not affect the flight performances. However, BCI performances decrease from the 83 to 63 % while attending additional alertness and vigilance tasks. This work shows that it is possible to successfully control a BCI system during the execution of multiple tasks such as piloting a flight simulator with an extra cognitive load induced by attentional tasks. Such framework aims to foster the knowledge on BCI systems embedded into vehicles and robotic devices to allow the simultaneous execution of secondary tasks.

P300 Chinese input system based on Bayesian LDA.

PubMed

Jin, Jing; Allison, Brendan Z; Brunner, Clemens; Wang, Bei; Wang, Xingyu; Zhang, Jianhua; Neuper, Christa; Pfurtscheller, Gert

2010-02-01

A brain-computer interface (BCI) is a new communication channel between humans and computers that translates brain activity into recognizable command and control signals. Attended events can evoke P300 potentials in the electroencephalogram. Hence, the P300 has been used in BCI systems to spell, control cursors or robotic devices, and other tasks. This paper introduces a novel P300 BCI to communicate Chinese characters. To improve classification accuracy, an optimization algorithm (particle swarm optimization, PSO) is used for channel selection (i.e., identifying the best electrode configuration). The effects of different electrode configurations on classification accuracy were tested by Bayesian linear discriminant analysis offline. The offline results from 11 subjects show that this new P300 BCI can effectively communicate Chinese characters and that the features extracted from the electrodes obtained by PSO yield good performance.

A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study.

PubMed

Amaral, Carlos P; Simões, Marco A; Mouga, Susana; Andrade, João; Castelo-Branco, Miguel

2017-10-01

We present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD). We tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems. The more suitable setup was tested online with 4 ASD subjects. Statistical accuracy was assessed based on the detection of P300, using spatial filtering and a Naïve-Bayes classifier. We compared: 1 - g.Mobilab+ (active dry-electrodes, wireless transmission); 2 - g.Nautilus (active electrodes, wireless transmission); 3 - V-Amp with actiCAP Xpress dry-electrodes. Significant statistical classification was achieved in all systems. g.Nautilus proved to be the best performing system in terms of accuracy in the detection of P300, preparation time, speed and reported comfort. Proof of concept tests in ASD participants proved that this setup is feasible for training joint attention skills in ASD. This work provides a unique combination of 'easy-to-use' BCI systems with new technologies such as VR to train joint-attention skills in autism. Our P300 BCI paradigm is feasible for future Phase I/II clinical trials to train joint-attention skills, with successful classification within few trials, online in ASD participants. The g.Nautilus system is the best performing one to use with the developed BCI setup. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved Accuracy Using Recursive Bayesian Estimation Based Language Model Fusion in ERP-Based BCI Typing Systems

PubMed Central

Orhan, U.; Erdogmus, D.; Roark, B.; Oken, B.; Purwar, S.; Hild, K. E.; Fowler, A.; Fried-Oken, M.

2013-01-01

RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive Bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve Bayesian fusion approach. The results indicate the superiority of the recursive Bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach. PMID:23366432

Brain-computer interface: changes in performance using virtual reality techniques.

PubMed

Ron-Angevin, Ricardo; Díaz-Estrella, Antonio

2009-01-09

The ability to control electroencephalographic (EEG) signals when different mental tasks are carried out would provide a method of communication for people with serious motor function problems. This system is known as a brain-computer interface (BCI). Due to the difficulty of controlling one's own EEG signals, a suitable training protocol is required to motivate subjects, as it is necessary to provide some type of visual feedback allowing subjects to see their progress. Conventional systems of feedback are based on simple visual presentations, such as a horizontal bar extension. However, virtual reality is a powerful tool with graphical possibilities to improve BCI-feedback presentation. The objective of the study is to explore the advantages of the use of feedback based on virtual reality techniques compared to conventional systems of feedback. Sixteen untrained subjects, divided into two groups, participated in the experiment. A group of subjects was trained using a BCI system, which uses conventional feedback (bar extension), and another group was trained using a BCI system, which submits subjects to a more familiar environment, such as controlling a car to avoid obstacles. The obtained results suggest that EEG behaviour can be modified via feedback presentation. Significant differences in classification error rates between both interfaces were obtained during the feedback period, confirming that an interface based on virtual reality techniques can improve the feedback control, specifically for untrained subjects.

The BCI competition. III: Validating alternative approaches to actual BCI problems.

PubMed

Blankertz, Benjamin; Müller, Klaus-Robert; Krusienski, Dean J; Schalk, Gerwin; Wolpaw, Jonathan R; Schlögl, Alois; Pfurtscheller, Gert; Millán, José del R; Schröder, Michael; Birbaumer, Niels

2006-06-01

A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

Research on steady-state visual evoked potentials in 3D displays

NASA Astrophysics Data System (ADS)

Chien, Yu-Yi; Lee, Chia-Ying; Lin, Fang-Cheng; Huang, Yi-Pai; Ko, Li-Wei; Shieh, Han-Ping D.

2015-05-01

Brain-computer interfaces (BCIs) are intuitive systems for users to communicate with outer electronic devices. Steady state visual evoked potential (SSVEP) is one of the common inputs for BCI systems due to its easy detection and high information transfer rates. An advanced interactive platform integrated with liquid crystal displays is leading a trend to provide an alternative option not only for the handicapped but also for the public to make our lives more convenient. Many SSVEP-based BCI systems have been studied in a 2D environment; however there is only little literature about SSVEP-based BCI systems using 3D stimuli. 3D displays have potentials in SSVEP-based BCI systems because they can offer vivid images, good quality in presentation, various stimuli and more entertainment. The purpose of this study was to investigate the effect of two important 3D factors (disparity and crosstalk) on SSVEPs. Twelve participants participated in the experiment with a patterned retarder 3D display. The results show that there is a significant difference (p-value<0.05) between large and small disparity angle, and the signal-to-noise ratios (SNRs) of small disparity angles is higher than those of large disparity angles. The 3D stimuli with smaller disparity and lower crosstalk are more suitable for applications based on the results of 3D perception and SSVEP responses (SNR). Furthermore, we can infer the 3D perception of users by SSVEP responses, and modify the proper disparity of 3D images automatically in the future.

A brain-computer interface controlled mail client.

PubMed

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Wang, Cong

2013-01-01

In this paper, we propose a brain-computer interface (BCI) based mail client. This system is controlled by hybrid features extracted from scalp-recorded electroencephalographic (EEG). We emulate the computer mouse by the motor imagery-based mu rhythm and the P300 potential. Furthermore, an adaptive P300 speller is included to provide text input function. With this BCI mail client, users can receive, read, write mails, as well as attach files in mail writing. The system has been tested on 3 subjects. Experimental results show that mail communication with this system is feasible.

Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends.

PubMed

Arico, Pietro; Borghini, Gianluca; Di Flumeri, Gianluca; Sciaraffa, Nicolina; Colosimo, Alfredo; Babiloni, Fabio

2017-07-01

This minireview aims to highlight recent important aspects to consider and evaluate when passive brain-computer interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications. Electroencephalography (EEG) based pBCI has become an important tool for real-time analysis of brain activity since it could potentially provide covertly-without distracting the user from the main task-and objectively-not affected by the subjective judgment of an observer or the user itself-information about the operator cognitive state. Different examples of pBCI applications in operational environments and new adaptive interface solutions have been presented and described. In addition, a general overview regarding the correct use of machine learning techniques (e.g., which algorithm to use, common pitfalls to avoid, etc.) in the pBCI field has been provided. Despite recent innovations on algorithms and neurotechnology, pBCI systems are not completely ready to enter the market yet, mainly due to limitations of the EEG electrodes technology, and algorithms reliability and capability in real settings. High complexity and safety critical systems (e.g., airplanes, ATM interfaces) should adapt their behaviors and functionality accordingly to the user' actual mental state. Thus, technologies (i.e., pBCIs) able to measure in real time the user's mental states would result very useful in such "high risk" environments to enhance human machine interaction, and so increase the overall safety.

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.

PubMed

Kumar, Shiu; Mamun, Kabir; Sharma, Alok

2017-12-01

Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier. The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings. The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid prototyping of an EEG-based brain-computer interface (BCI).

PubMed

Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

2001-03-01

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

PubMed Central

Zao, John K.; Gan, Tchin-Tze; You, Chun-Kai; Chung, Cheng-En; Wang, Yu-Te; Rodríguez Méndez, Sergio José; Mullen, Tim; Yu, Chieh; Kothe, Christian; Hsiao, Ching-Teng; Chu, San-Liang; Shieh, Ce-Kuen; Jung, Tzyy-Ping

2014-01-01

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system. PMID:24917804

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

PubMed

Zao, John K; Gan, Tchin-Tze; You, Chun-Kai; Chung, Cheng-En; Wang, Yu-Te; Rodríguez Méndez, Sergio José; Mullen, Tim; Yu, Chieh; Kothe, Christian; Hsiao, Ching-Teng; Chu, San-Liang; Shieh, Ce-Kuen; Jung, Tzyy-Ping

2014-01-01

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

Toward a hybrid brain-computer interface based on imagined movement and visual attention

NASA Astrophysics Data System (ADS)

Allison, B. Z.; Brunner, C.; Kaiser, V.; Müller-Putz, G. R.; Neuper, C.; Pfurtscheller, G.

2010-04-01

Brain-computer interface (BCI) systems do not work for all users. This article introduces a novel combination of tasks that could inspire BCI systems that are more accurate than conventional BCIs, especially for users who cannot attain accuracy adequate for effective communication. Subjects performed tasks typically used in two BCI approaches, namely event-related desynchronization (ERD) and steady state visual evoked potential (SSVEP), both individually and in a 'hybrid' condition that combines both tasks. Electroencephalographic (EEG) data were recorded across three conditions. Subjects imagined moving the left or right hand (ERD), focused on one of the two oscillating visual stimuli (SSVEP), and then simultaneously performed both tasks. Accuracy and subjective measures were assessed. Offline analyses suggested that half of the subjects did not produce brain patterns that could be accurately discriminated in response to at least one of the two tasks. If these subjects produced comparable EEG patterns when trying to use a BCI, these subjects would not be able to communicate effectively because the BCI would make too many errors. Results also showed that switching to a different task used in BCIs could improve accuracy in some of these users. Switching to a hybrid approach eliminated this problem completely, and subjects generally did not consider the hybrid condition more difficult. Results validate this hybrid approach and suggest that subjects who cannot use a BCI should consider switching to a different BCI approach, especially a hybrid BCI. Subjects proficient with both approaches might combine them to increase information throughput by improving accuracy, reducing selection time, and/or increasing the number of possible commands.

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

PubMed Central

Víctor Rodrigo, Mercado-García

2017-01-01

Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities. PMID:29317861

An online BCI game based on the decoding of users' attention to color stimulus.

PubMed

Yang, Lingling; Leung, Howard

2013-01-01

Studies have shown that statistically there are differences in theta, alpha and beta band powers when people look at blue and red colors. In this paper, a game has been developed to test whether these statistical differences are good enough for online Brain Computer Interface (BCI) application. We implemented a two-choice BCI game in which the subject makes the choice by looking at a color option and our system decodes the subject's intention by analyzing the EEG signal. In our system, band power features of the EEG data were used to train a support vector machine (SVM) classification model. An online mechanism was adopted to update the classification model during the training stage to account for individual differences. Our results showed that an accuracy of 70%-80% could be achieved and it provided evidence for the possibility in applying color stimuli to BCI applications.

[The P300-based brain-computer interface: presentation of the complex "flash + movement" stimuli].

PubMed

Ganin, I P; Kaplan, A Ia

2014-01-01

The P300 based brain-computer interface requires the detection of P300 wave of brain event-related potentials. Most of its users learn the BCI control in several minutes and after the short classifier training they can type a text on the computer screen or assemble an image of separate fragments in simple BCI-based video games. Nevertheless, insufficient attractiveness for users and conservative stimuli organization in this BCI may restrict its integration into real information processes control. At the same time initial movement of object (motion-onset stimuli) may be an independent factor that induces P300 wave. In current work we checked the hypothesis that complex "flash + movement" stimuli together with drastic and compact stimuli organization on the computer screen may be much more attractive for user while operating in P300 BCI. In 20 subjects research we showed the effectiveness of our interface. Both accuracy and P300 amplitude were higher for flashing stimuli and complex "flash + movement" stimuli compared to motion-onset stimuli. N200 amplitude was maximal for flashing stimuli, while for "flash + movement" stimuli and motion-onset stimuli it was only a half of it. Similar BCI with complex stimuli may be embedded into compact control systems requiring high level of user attention under impact of negative external effects obstructing the BCI control.

Brain oscillatory signatures of motor tasks

PubMed Central

Birbaumer, Niels

2015-01-01

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

Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications.

PubMed

Pahwa, Mrinal; Kusner, Matthew; Hacker, Carl D; Bundy, David T; Weinberger, Kilian Q; Leuthardt, Eric C

2015-01-01

Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.

High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery

PubMed Central

Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung Chan

2013-01-01

In most brain computer interface (BCI) systems, some target users have significant difficulty in using BCI systems. Such target users are called ‘BCI-illiterate’. This phenomenon has been poorly investigated, and a clear understanding of the BCI-illiteracy mechanism or a solution to this problem has not been reported to date. In this study, we sought to demonstrate the neurophysiological differences between two groups (literate, illiterate) with a total of 52 subjects. We investigated recordings under non-task related state (NTS) which is collected during subject is relaxed with eyes open. We found that high theta and low alpha waves were noticeable in the BCI-illiterate relative to the BCI-literate people. Furthermore, these high theta and low alpha wave patterns were preserved across different mental states, such as NTS, resting before motor imagery (MI), and MI states, even though the spatial distribution of both BCI-illiterate and BCI-literate groups did not differ. From these findings, an effective strategy for pre-screening subjects for BCI illiteracy has been determined, and a performance factor that reflects potential user performance has been proposed using a simple combination of band powers. Our proposed performance factor gave an r = 0.59 (r2 = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r2 = 0.50) when seven outliers were rejected during the evaluation of whole data (N = 61), including BCI competition datasets (N = 9). These findings may be directly applicable to online BCI systems. PMID:24278339

High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery.

PubMed

Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung Chan

2013-01-01

In most brain computer interface (BCI) systems, some target users have significant difficulty in using BCI systems. Such target users are called 'BCI-illiterate'. This phenomenon has been poorly investigated, and a clear understanding of the BCI-illiteracy mechanism or a solution to this problem has not been reported to date. In this study, we sought to demonstrate the neurophysiological differences between two groups (literate, illiterate) with a total of 52 subjects. We investigated recordings under non-task related state (NTS) which is collected during subject is relaxed with eyes open. We found that high theta and low alpha waves were noticeable in the BCI-illiterate relative to the BCI-literate people. Furthermore, these high theta and low alpha wave patterns were preserved across different mental states, such as NTS, resting before motor imagery (MI), and MI states, even though the spatial distribution of both BCI-illiterate and BCI-literate groups did not differ. From these findings, an effective strategy for pre-screening subjects for BCI illiteracy has been determined, and a performance factor that reflects potential user performance has been proposed using a simple combination of band powers. Our proposed performance factor gave an r = 0.59 (r(2) = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r(2) = 0.50) when seven outliers were rejected during the evaluation of whole data (N = 61), including BCI competition datasets (N = 9). These findings may be directly applicable to online BCI systems.

BCI Control of Heuristic Search Algorithms

PubMed Central

Cavazza, Marc; Aranyi, Gabor; Charles, Fred

2017-01-01

The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems. PMID:28197092

BCI Control of Heuristic Search Algorithms.

PubMed

Cavazza, Marc; Aranyi, Gabor; Charles, Fred

2017-01-01

The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users' mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems.

Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling.

PubMed

Cheng, Jiao; Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Bei; Wang, Xingyu; Cichocki, Andrzej

2018-02-13

Brain-computer interface (BCI) systems can allow their users to communicate with the external world by recognizing intention directly from their brain activity without the assistance of the peripheral motor nervous system. The P300-speller is one of the most widely used visual BCI applications. In previous studies, a flip stimulus (rotating the background area of the character) that was based on apparent motion, suffered from less refractory effects. However, its performance was not improved significantly. In addition, a presentation paradigm that used a "zooming" action (changing the size of the symbol) has been shown to evoke relatively higher P300 amplitudes and obtain a better BCI performance. To extend this method of stimuli presentation within a BCI and, consequently, to improve BCI performance, we present a new paradigm combining both the flip stimulus with a zooming action. This new presentation modality allowed BCI users to focus their attention more easily. We investigated whether such an action could combine the advantages of both types of stimuli presentation to bring a significant improvement in performance compared to the conventional flip stimulus. The experimental results showed that the proposed paradigm could obtain significantly higher classification accuracies and bit rates than the conventional flip paradigm (p<0.01).

Controlling an avatar by thought using real-time fMRI

NASA Astrophysics Data System (ADS)

Cohen, Ori; Koppel, Moshe; Malach, Rafael; Friedman, Doron

2014-06-01

Objective. We have developed a brain-computer interface (BCI) system based on real-time functional magnetic resonance imaging (fMRI) with virtual reality feedback. The advantage of fMRI is the relatively high spatial resolution and the coverage of the whole brain; thus we expect that it may be used to explore novel BCI strategies, based on new types of mental activities. However, fMRI suffers from a low temporal resolution and an inherent delay, since it is based on a hemodynamic response rather than electrical signals. Thus, our objective in this paper was to explore whether subjects could perform a BCI task in a virtual environment using our system, and how their performance was affected by the delay. Approach. The subjects controlled an avatar by left-hand, right-hand and leg motion or imagery. The BCI classification is based on locating the regions of interest (ROIs) related with each of the motor classes, and selecting the ROI with maximum average values online. The subjects performed a cue-based task and a free-choice task, and the analysis includes evaluation of the performance as well as subjective reports. Main results. Six subjects performed the task with high accuracy when allowed to move their fingers and toes, and three subjects achieved high accuracy using imagery alone. In the cue-based task the accuracy was highest 8-12 s after the trigger, whereas in the free-choice task the subjects performed best when the feedback was provided 6 s after the trigger. Significance. We show that subjects are able to perform a navigation task in a virtual environment using an fMRI-based BCI, despite the hemodynamic delay. The same approach can be extended to other mental tasks and other brain areas.

The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface.

PubMed

Xie, Jun; Xu, Guanghua; Luo, Ailing; Li, Min; Zhang, Sicong; Han, Chengcheng; Yan, Wenqiang

2017-08-14

As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α , θ , θ + α powers, θ / α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications.

Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms.

PubMed

Royer, Audrey S; He, Bin

2009-02-01

In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control, the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp-recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for seven out of eight subjects) and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.

Cognitive assessment in Amyotrophic Lateral Sclerosis by means of P300-Brain Computer Interface: a preliminary study.

PubMed

Poletti, Barbara; Carelli, Laura; Solca, Federica; Lafronza, Annalisa; Pedroli, Elisa; Faini, Andrea; Zago, Stefano; Ticozzi, Nicola; Meriggi, Paolo; Cipresso, Pietro; Lulé, Dorothée; Ludolph, Albert C; Riva, Giuseppe; Silani, Vincenzo

To investigate the use of P300-based Brain Computer Interface (BCI) technology for the administration of motor-verbal free cognitive tests in Amyotrophic Lateral Sclerosis (ALS). We recruited 15 ALS patients and 15 age- and education-matched healthy subjects. All participants underwent a BCI-based neuropsychological assessment, together with two standard cognitive screening tools (FAB, MoCA), two psychological questionnaires (BDI, STAI-Y) and a usability questionnaire. For patients, clinical and respiratory examinations were also performed, together with a behavioural assessment (FBI). Correlations were observed between standard cognitive and BCI-based neuropsychological assessment, mainly concerning execution times in the ALS group. Moreover, patients provided positive rates concerning the BCI perceived usability and subjective experience. Finally, execution times at the BCI-based neuropsychological assessment were useful to discriminate patients from controls, with patients achieving lower processing speed than controls regarding executive functions. The developed motor-verbal free neuropsychological battery represents an innovative approach, that could provide relevant information for clinical practice and ethical issues. Its use for cognitive evaluation throughout the course of ALS, currently not available by means of standard assessment, must be addressed in further longitudinal validation studies. Further work will be aimed at refining the developed system and enlarging the cognitive spectrum investigated.

Light on! Real world evaluation of a P300-based brain-computer interface (BCI) for environment control in a smart home.

PubMed

Carabalona, Roberta; Grossi, Ferdinando; Tessadri, Adam; Castiglioni, Paolo; Caracciolo, Antonio; de Munari, Ilaria

2012-01-01

Brain-computer interface (BCI) systems aim to enable interaction with other people and the environment without muscular activation by the exploitation of changes in brain signals due to the execution of cognitive tasks. In this context, the visual P300 potential appears suited to control smart homes through BCI spellers. The aim of this work is to evaluate whether the widely used character-speller is more sustainable than an icon-based one, designed to operate smart home environment or to communicate moods and needs. Nine subjects with neurodegenerative diseases and no BCI experience used both speller types in a real smart home environment. User experience during BCI tasks was evaluated recording concurrent physiological signals. Usability was assessed for each speller type immediately after use. Classification accuracy was lower for the icon-speller, which was also more attention demanding. However, in subjective evaluations, the effect of a real feedback partially counterbalanced the difficulty in BCI use. Since inclusive BCIs require to consider interface sustainability, we evaluated different ergonomic aspects of the interaction of disabled users with a character-speller (goal: word spelling) and an icon-speller (goal: operating a real smart home). We found the first one as more sustainable in terms of accuracy and cognitive effort.

An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

PubMed Central

Hill, N J; Schölkopf, B

2012-01-01

We report on the development and online testing of an EEG-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects’ modulation of N1 and P3 ERP components measured during single 5-second stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare “oddball” stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly-known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention-modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject’s attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology. PMID:22333135

An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

NASA Astrophysics Data System (ADS)

Hill, N. J.; Schölkopf, B.

2012-04-01

We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare ‘oddball’ stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

BCI-FES system for neuro-rehabilitation of stroke patients

NASA Astrophysics Data System (ADS)

Jure, Fabricio A.; Carrere, Lucía C.; Gentiletti, Gerardo G.; Tabernig, Carolina B.

2016-04-01

Nowadays, strokes are a growing cause of mortality and many people remain with motor sequelae and troubles in the daily activities. To treat this sequelae, alternative rehabilitation techniques are needed. In this article a Brain Computer Interface (BCI) system to control a Functional Electrical Stimulation (FES) system is presented. It can be used as a novel tool in easy setup clinical routines, to improve the rehabilitation process by mean of detecting patient´s motor intention, performing it by FES and finally receiving appropriate feedback The BCI-FES system presented here, consists of three blocks: the first one decodes the patient´s intention and it is composed by the patient, the acquisition hardware and the processing software (Emotiv EPOC®). The second block, based on Arduino’s technology, transforms the information into a valid command signal. The last one excites the patient´s neuromuscular system by means of a FES device. In order to evaluate the cerebral activity sensed by the device, topographic maps were obtained. The BCI-FES system was able to detect the patient´s motor intention and control the FES device. At the time of this publication, the system it’s being employing in a rehabilitation program with patients post stroke.

Local and Remote Cooperation With Virtual and Robotic Agents: A P300 BCI Study in Healthy and People Living With Spinal Cord Injury.

PubMed

Tidoni, Emmanuele; Abu-Alqumsan, Mohammad; Leonardis, Daniele; Kapeller, Christoph; Fusco, Gabriele; Guger, Cristoph; Hintermuller, Cristoph; Peer, Angelika; Frisoli, Antonio; Tecchia, Franco; Bergamasco, Massimo; Aglioti, Salvatore Maria

2017-09-01

The development of technological applications that allow people to control and embody external devices within social interaction settings represents a major goal for current and future brain-computer interface (BCI) systems. Prior research has suggested that embodied systems may ameliorate BCI end-user's experience and accuracy in controlling external devices. Along these lines, we developed an immersive P300-based BCI application with a head-mounted display for virtual-local and robotic-remote social interactions and explored in a group of healthy participants the role of proprioceptive feedback in the control of a virtual surrogate (Study 1). Moreover, we compared the performance of a small group of people with spinal cord injury (SCI) to a control group of healthy subjects during virtual and robotic social interactions (Study 2), where both groups received a proprioceptive stimulation. Our attempt to combine immersive environments, BCI technologies and neuroscience of body ownership suggests that providing realistic multisensory feedback still represents a challenge. Results have shown that healthy and people living with SCI used the BCI within the immersive scenarios with good levels of performance (as indexed by task accuracy, optimizations calls and Information Transfer Rate) and perceived control of the surrogates. Proprioceptive feedback did not contribute to alter performance measures and body ownership sensations. Further studies are necessary to test whether sensorimotor experience represents an opportunity to improve the use of future embodied BCI applications.

Integrating language models into classifiers for BCI communication: a review

NASA Astrophysics Data System (ADS)

Speier, W.; Arnold, C.; Pouratian, N.

2016-06-01

Objective. The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. Approach. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. Main results. Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. Significance. Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.

Integrating language models into classifiers for BCI communication: a review.

PubMed

Speier, W; Arnold, C; Pouratian, N

2016-06-01

The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.

Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery

NASA Astrophysics Data System (ADS)

Toppi, J.; Risetti, M.; Quitadamo, L. R.; Petti, M.; Bianchi, L.; Salinari, S.; Babiloni, F.; Cincotti, F.; Mattia, D.; Astolfi, L.

2014-06-01

Objective. It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. Approach. Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. Main results. Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. Significance. The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.

Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.

PubMed

Toppi, J; Risetti, M; Quitadamo, L R; Petti, M; Bianchi, L; Salinari, S; Babiloni, F; Cincotti, F; Mattia, D; Astolfi, L

2014-06-01

It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.

Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review.

PubMed

Monge-Pereira, Esther; Ibañez-Pereda, Jaime; Alguacil-Diego, Isabel M; Serrano, Jose I; Spottorno-Rubio, María P; Molina-Rueda, Francisco

2017-09-01

Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review. Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. II. Copyright © 2017 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

NASA Astrophysics Data System (ADS)

Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

2015-12-01

Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time. Significance. The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.

The portable P300 dialing system based on tablet and Emotiv Epoc headset.

PubMed

Tong Jijun; Zhang Peng; Xiao Ran; Ding Lei

2015-08-01

A Brain-computer interface (BCI) is a novel communication system that translates brain signals into a control signal. Now with the appearance of the commercial EEG headsets and mobile smart platforms (tablet, smartphone), it is possible to develop the mobile BCI system, which can greatly improve the life quality of patients suffering from motor disease, such as amyotrophic lateral scleroses (ALS), multiple sclerosis, cerebral palsy and head trauma. This study adopted a 14-channel Emotiv EPOC headset and Microsoft surface pro 3 to realize a dialing system, which was represented by 4×3 matrices of alphanumeric characters. The performance of the online portable dialing system based on P300 is satisfying. The average classification accuracy reaches 88.75±10.57% in lab and 73.75±16.94% in metro, while the information transfer rate (ITR) reaches 7.17±1.80 and 5.05±2.17 bits/min respectively. This means the commercial EEG headset and tablet has good prospect in developing real time BCI system in realistic environments.

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.

PubMed

Stawicki, Piotr; Gembler, Felix; Volosyak, Ivan

2016-01-01

Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system.

Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI

PubMed Central

2016-01-01

Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system. PMID:27528864

Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR)

PubMed Central

Hammer, Eva M.; Kaufmann, Tobias; Kleih, Sonja C.; Blankertz, Benjamin; Kübler, Andrea

2014-01-01

Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80–100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person’s visuo-motor control ability; and (2) subject’s “attentional impulsivity”. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors. PMID:25147518

Machine-Learning Based Co-adaptive Calibration: A Perspective to Fight BCI Illiteracy

NASA Astrophysics Data System (ADS)

Vidaurre, Carmen; Sannelli, Claudia; Müller, Klaus-Robert; Blankertz, Benjamin

"BCI illiteracy" is one of the biggest problems and challenges in BCI research. It means that BCI control cannot be achieved by a non-negligible number of subjects (estimated 20% to 25%). There are two main causes for BCI illiteracy in BCI users: either no SMR idle rhythm is observed over motor areas, or this idle rhythm is not attenuated during motor imagery, resulting in a classification performance lower than 70% (criterion level) already for offline calibration data. In a previous work of the same authors, the concept of machine learning based co-adaptive calibration was introduced. This new type of calibration provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adapting learning enables substantial BCI control for completely novice users and those who suffered from BCI illiteracy before.

Brain-Computer Interface Spellers: A Review.

PubMed

Rezeika, Aya; Benda, Mihaly; Stawicki, Piotr; Gembler, Felix; Saboor, Abdul; Volosyak, Ivan

2018-03-30

A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

The Role of Audio-Visual Feedback in a Thought-Based Control of a Humanoid Robot: A BCI Study in Healthy and Spinal Cord Injured People.

PubMed

Tidoni, Emmanuele; Gergondet, Pierre; Fusco, Gabriele; Kheddar, Abderrahmane; Aglioti, Salvatore M

2017-06-01

The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and-place task by means of a visual evoked potentials BCI system. Participants observed the remote environment from the robot's perspective through a head mounted display. Human-footsteps and computer-beep sounds were used as synchronous/asynchronous auditory feedback. Healthy participants achieved better placing accuracy when listening to human footstep sounds relative to a computer-generated sound. SCI people demonstrated more difficulty in steering the robot during asynchronous auditory feedback conditions. Importantly, subjective reports highlighted that the BCI mask overlaying the display did not limit the observation of the scenario and the feeling of being in control of the robot. Overall, the data seem to suggest that sensorimotor-related information may improve the control of external devices. Further studies are required to understand how the contribution of residual sensory channels could improve the reliability of BCI systems.

Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface

NASA Astrophysics Data System (ADS)

Widge, Alik S.; Moritz, Chet T.

2014-04-01

Objective. There is great interest in closed-loop neurostimulators that sense and respond to a patient's brain state. Such systems may have value for neurological and psychiatric illnesses where symptoms have high intraday variability. Animal models of closed-loop stimulators would aid preclinical testing. We therefore sought to demonstrate that rodents can directly control a closed-loop limbic neurostimulator via a brain-computer interface (BCI). Approach. We trained rats to use an auditory BCI controlled by single units in prefrontal cortex (PFC). The BCI controlled electrical stimulation in the medial forebrain bundle, a limbic structure involved in reward-seeking. Rigorous offline analyses were performed to confirm volitional control of the neurostimulator. Main results. All animals successfully learned to use the BCI and neurostimulator, with closed-loop control of this challenging task demonstrated at 80% of PFC recording locations. Analysis across sessions and animals confirmed statistically robust BCI control and specific, rapid modulation of PFC activity. Significance. Our results provide a preliminary demonstration of a method for emotion-regulating closed-loop neurostimulation. They further suggest that activity in PFC can be used to control a BCI without pre-training on a predicate task. This offers the potential for BCI-based treatments in refractory neurological and mental illness.

EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century

PubMed Central

Lazarou, Ioulietta; Nikolopoulos, Spiros; Petrantonakis, Panagiotis C.; Kompatsiaris, Ioannis; Tsolaki, Magda

2018-01-01

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain–computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future. PMID:29472849

Review of wireless and wearable electroencephalogram systems and brain-computer interfaces--a mini-review.

PubMed

Lin, Chin-Teng; Ko, Li-Wei; Chang, Meng-Hsiu; Duann, Jeng-Ren; Chen, Jing-Ying; Su, Tung-Ping; Jung, Tzyy-Ping

2010-01-01

Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, lightweight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems. Copyright 2009 S. Karger AG, Basel.

Neuroanatomical correlates of brain-computer interface performance.

PubMed

Kasahara, Kazumi; DaSalla, Charles Sayo; Honda, Manabu; Hanakawa, Takashi

2015-04-15

Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. Copyright © 2015 Elsevier Inc. All rights reserved.

Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis

NASA Astrophysics Data System (ADS)

Geronimo, A.; Simmons, Z.; Schiff, S. J.

2016-04-01

Objective. Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder. Approach. In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery. Results. The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age. Significance. These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.

Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits.

PubMed

Alonso-Valerdi, Luz Maria; Salido-Ruiz, Ricardo Antonio; Ramirez-Mendoza, Ricardo A

2015-12-01

When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system. Copyright © 2015 Elsevier Ltd. All rights reserved.

Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface Meeting.

PubMed

Peters, Betts; Bieker, Gregory; Heckman, Susan M; Huggins, Jane E; Wolf, Catherine; Zeitlin, Debra; Fried-Oken, Melanie

2015-03-01

More than 300 researchers gathered at the 2013 International Brain-Computer Interface (BCI) Meeting to discuss current practice and future goals for BCI research and development. The authors organized the Virtual Users' Forum at the meeting to provide the BCI community with feedback from users. We report on the Virtual Users' Forum, including initial results from ongoing research being conducted by 2 BCI groups. Online surveys and in-person interviews were used to solicit feedback from people with disabilities who are expert and novice BCI users. For the Virtual Users' Forum, their responses were organized into 4 major themes: current (non-BCI) communication methods, experiences with BCI research, challenges of current BCIs, and future BCI developments. Two authors with severe disabilities gave presentations during the Virtual Users' Forum, and their comments are integrated with the other results. While participants' hopes for BCIs of the future remain high, their comments about available systems mirror those made by consumers about conventional assistive technology. They reflect concerns about reliability (eg, typing accuracy/speed), utility (eg, applications and the desire for real-time interactions), ease of use (eg, portability and system setup), and support (eg, technical support and caregiver training). People with disabilities, as target users of BCI systems, can provide valuable feedback and input on the development of BCI as an assistive technology. To this end, participatory action research should be considered as a valuable methodology for future BCI research. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Diagnostic of students' misconceptions using the Biological Concepts Instrument (BCI): A method for conducting an educational needs assessment

PubMed Central

Champagne Queloz, Annie; Klymkowsky, Michael W.; Stern, Elsbeth; Hafen, Ernst; Köhler, Katja

2017-01-01

Concept inventories, constructed based on an analysis of students’ thinking and their explanations of scientific situations, serve as diagnostics for identifying misconceptions and logical inconsistencies and provide data that can help direct curricular reforms. In the current project, we distributed the Biological Concepts Instrument (BCI) to 17-18-year-old students attending the highest track of the Swiss school system (Gymnasium). Students’ performances on many questions related to evolution, genetics, molecular properties and functions were diverse. Important common misunderstandings were identified in the areas of evolutionary processes, molecular properties and an appreciation of stochastic processes in biological systems. Our observations provide further evidence that the BCI is efficient in identifying specific areas where targeted instruction is required. Based on these observations we have initiated changes at several levels to reconsider how biological systems are presented to university biology studies with the goal of improving student’s foundational understanding. PMID:28493960

Assisted closed-loop optimization of SSVEP-BCI efficiency

PubMed Central

Fernandez-Vargas, Jacobo; Pfaff, Hanns U.; Rodríguez, Francisco B.; Varona, Pablo

2012-01-01

We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research. PMID:23443214

Assisted closed-loop optimization of SSVEP-BCI efficiency.

PubMed

Fernandez-Vargas, Jacobo; Pfaff, Hanns U; Rodríguez, Francisco B; Varona, Pablo

2013-01-01

We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment

PubMed Central

Faller, Josef; Scherer, Reinhold; Friedrich, Elisabeth V. C.; Costa, Ursula; Opisso, Eloy; Medina, Josep; Müller-Putz, Gernot R.

2014-01-01

Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks (“SMR-AdBCI”) have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (“Auto-AdBCI”) could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%). PMID:25368546

A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

PubMed

Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K; Birch, Gary E

2007-06-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

TOPICAL REVIEW: A survey of signal processing algorithms in brain computer interfaces based on electrical brain signals

NASA Astrophysics Data System (ADS)

Bashashati, Ali; Fatourechi, Mehrdad; Ward, Rabab K.; Birch, Gary E.

2007-06-01

Brain computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?

Towards an architecture of a hybrid BCI based on SSVEP-BCI and passive-BCI.

PubMed

Cotrina, Anibal; Benevides, Alessandro; Ferreira, Andre; Bastos, Teodiano; Castillo, Javier; Menezes, Maria Luiza; Pereira, Carlos

2014-01-01

Recent decades have seen BCI applications as a novel and promising new channel of communication, control and entertainment for disabled and healthy people. However, BCI technology can be prone to errors due to the basic emotional state of the user: the performance of reactive and active BCIs decrease when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCI technology with cognitive monitoring, providing valuable information about the user's intentions, the situational interpretations and mainly the emotional state. In this work, an architecture composed by passive-BCI co-working with SSVEP-BCI is proposed, with the aim of improving the performance of the reactive-BCI. The possibility of adjusting recognition characteristics of SSVEP-BCIs using a passive-BCI output is evaluated. In this sense, two ways to recover the accuracy of SSVEP are presented in this paper: 1) Adjusting of Amplitude of the SSVEP and 2) Adjusting of Frequency of the SSVEP response. The results are promising, because accuracy of SSVEP-BCI can be recovered in the case that it was reduced by the BCI user's emotional state.

A Closed-loop Brain Computer Interface to a Virtual Reality Avatar: Gait Adaptation to Visual Kinematic Perturbations

PubMed Central

Luu, Trieu Phat; He, Yongtian; Brown, Samuel; Nakagome, Sho; Contreras-Vidal, Jose L.

2016-01-01

The control of human bipedal locomotion is of great interest to the field of lower-body brain computer interfaces (BCIs) for rehabilitation of gait. While the feasibility of a closed-loop BCI system for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a virtual reality (BCI-VR) environment has yet to be demonstrated. In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control the walking movements of a virtual avatar. Moreover, virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. These findings have implications for the development of BCI-VR systems for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI system. PMID:27713915

Brain–Computer Interface Spellers: A Review

PubMed Central

Gembler, Felix; Saboor, Abdul

2018-01-01

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers. PMID:29601538

Gamma band activity associated with BCI performance: simultaneous MEG/EEG study.

PubMed

Ahn, Minkyu; Ahn, Sangtae; Hong, Jun H; Cho, Hohyun; Kim, Kiwoong; Kim, Bong S; Chang, Jin W; Jun, Sung C

2013-01-01

While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called "BCI-Illiterates") cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.

Brain-computer interface control along instructed paths

NASA Astrophysics Data System (ADS)

Sadtler, P. T.; Ryu, S. I.; Tyler-Kabara, E. C.; Yu, B. M.; Batista, A. P.

2015-02-01

Objective. Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user’s intent, and we can increase the richness of the BCI movement repertoire. Approach. We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics. Main results. We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task. Significance. The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.

Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System

PubMed Central

Pan, Jiahui; Xie, Qiuyou; Huang, Haiyun; He, Yanbin; Sun, Yuping; Yu, Ronghao; Li, Yuanqing

2018-01-01

For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses. PMID:29867421

Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System.

PubMed

Pan, Jiahui; Xie, Qiuyou; Huang, Haiyun; He, Yanbin; Sun, Yuping; Yu, Ronghao; Li, Yuanqing

2018-01-01

For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.

A Brain-Based Communication and Orientation System

DTIC Science & Technology

2014-10-06

Review of the BCI Competition IV, Frontiers in Neuroscience, ( 2012): 0. doi: 10.3389/fnins.2012.00055 Eric C. Leuthardt, Xiao-Mei Pei, Jonathan...hardware and software for brain– computer interfaces ( BCIs ), Journal of Neural Engineering, (04 2011): 1. doi: 10.1088/1741-2560/8/2/025001...Cincotti, G. Schalk, Peter Brunner. Current Trends in Brain–Computer Interface ( BCI ) Research and Development, Journal of Neural Engineering, (3 2011

Describing different brain computer interface systems through a unique model: a UML implementation.

PubMed

Quitadamo, Lucia Rita; Marciani, Maria Grazia; Cardarilli, Gian Carlo; Bianchi, Luigi

2008-01-01

All the protocols currently implemented in brain computer interface (BCI) experiments are characterized by different structural and temporal entities. Moreover, due to the lack of a unique descriptive model for BCI systems, there is not a standard way to define the structure and the timing of a BCI experimental session among different research groups and there is also great discordance on the meaning of the most common terms dealing with BCI, such as trial, run and session. The aim of this paper is to provide a unified modeling language (UML) implementation of BCI systems through a unique dynamic model which is able to describe the main protocols defined in the literature (P300, mu-rhythms, SCP, SSVEP, fMRI) and demonstrates to be reasonable and adjustable according to different requirements. This model includes a set of definitions of the typical entities encountered in a BCI, diagrams which explain the structural correlations among them and a detailed description of the timing of a trial. This last represents an innovation with respect to the models already proposed in the literature. The UML documentation and the possibility of adapting this model to the different BCI systems built to date, make it a basis for the implementation of new systems and a mean for the unification and dissemination of resources. The model with all the diagrams and definitions reported in the paper are the core of the body language framework, a free set of routines and tools for the implementation, optimization and delivery of cross-platform BCI systems.

Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials

PubMed Central

2014-01-01

Background People with severe disabilities, e.g. due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g. recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system. Methods Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N = 15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm. Results Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed. Conclusion We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control. PMID:24428900

Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface.

PubMed

Emami, Zahra; Chau, Tom

2018-06-01

Brain-computer interfaces (BCIs) allow users to operate a device or application by means of cognitive activity. This technology will ultimately be used in real-world environments which include the presence of distractors. The purpose of the study was to determine the effect of visual distractors on BCI performance. Sixteen able-bodied participants underwent neurofeedback training to achieve motor imagery-guided BCI control in an online paradigm using electroencephalography (EEG) to measure neural signals. Participants then completed two sessions of the motor imagery EEG-BCI protocol in the presence of infrequent, small visual distractors. BCI performance was determined based on classification accuracy. The presence of distractors was found to affect motor imagery-specific patterns in mu and beta power. However, the distractors did not significantly affect the BCI classification accuracy; across participants, the mean classification accuracy was 81.5 ± 14% for non-distractor trials, and 78.3 ± 17% for distractor trials. This minimal consequence suggests that the BCI was robust to distractor effects, despite motor imagery-related brain activity being attenuated amid distractors. A BCI system that mitigates distraction-related effects may improve the ease of its use and ultimately facilitate the effective translation of the technology from the lab to the home. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

BCI Use and Its Relation to Adaptation in Cortical Networks.

PubMed

Casimo, Kaitlyn; Weaver, Kurt E; Wander, Jeremiah; Ojemann, Jeffrey G

2017-10-01

Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.

Reaching and Grasping a Glass of Water by Locked-In ALS Patients through a BCI-Controlled Humanoid Robot

PubMed Central

Spataro, Rossella; Chella, Antonio; Allison, Brendan; Giardina, Marcello; Sorbello, Rosario; Tramonte, Salvatore; Guger, Christoph; La Bella, Vincenzo

2017-01-01

Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling. In the present study, locked-in ALS patients used a BCI system to directly control the humanoid robot NAO (Aldebaran Robotics, France) with the aim of reaching and grasping a glass of water. Four ALS patients and four healthy controls were recruited and trained to operate this humanoid robot through a P300-based BCI. A few minutes training was sufficient to efficiently operate the system in different environments. Three out of the four ALS patients and all controls successfully performed the task with a high level of accuracy. These results suggest that BCI-operated robots can be used by locked-in ALS patients as an artificial alter-ego, the machine being able to move, speak and act in his/her place. PMID:28298888

Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP

NASA Astrophysics Data System (ADS)

Yi, Weibo; Qiu, Shuang; Wang, Kun; Qi, Hongzhi; Zhao, Xin; He, Feng; Zhou, Peng; Yang, Jiajia; Ming, Dong

2017-04-01

Objective. We proposed a novel simultaneous hybrid brain-computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated. Approach. 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously. Main results. The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature. Significance. The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.

A general method for assessing brain-computer interface performance and its limitations

NASA Astrophysics Data System (ADS)

Hill, N. Jeremy; Häuser, Ann-Katrin; Schalk, Gerwin

2014-04-01

Objective. When researchers evaluate brain-computer interface (BCI) systems, we want quantitative answers to questions such as: How good is the system’s performance? How good does it need to be? and: Is it capable of reaching the desired level in future? In response to the current lack of objective, quantitative, study-independent approaches, we introduce methods that help to address such questions. We identified three challenges: (I) the need for efficient measurement techniques that adapt rapidly and reliably to capture a wide range of performance levels; (II) the need to express results in a way that allows comparison between similar but non-identical tasks; (III) the need to measure the extent to which certain components of a BCI system (e.g. the signal processing pipeline) not only support BCI performance, but also potentially restrict the maximum level it can reach. Approach. For challenge (I), we developed an automatic staircase method that adjusted task difficulty adaptively along a single abstract axis. For challenge (II), we used the rate of information gain between two Bernoulli distributions: one reflecting the observed success rate, the other reflecting chance performance estimated by a matched random-walk method. This measure includes Wolpaw’s information transfer rate as a special case, but addresses the latter’s limitations including its restriction to item-selection tasks. To validate our approach and address challenge (III), we compared four healthy subjects’ performance using an EEG-based BCI, a ‘Direct Controller’ (a high-performance hardware input device), and a ‘Pseudo-BCI Controller’ (the same input device, but with control signals processed by the BCI signal processing pipeline). Main results. Our results confirm the repeatability and validity of our measures, and indicate that our BCI signal processing pipeline reduced attainable performance by about 33% (21 bits min-1). Significance. Our approach provides a flexible basis for evaluating BCI performance and its limitations, across a wide range of tasks and task difficulties.

Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis

PubMed Central

Riccio, Angela; Simione, Luca; Schettini, Francesca; Pizzimenti, Alessia; Inghilleri, Maurizio; Belardinelli, Marta Olivetti; Mattia, Donatella; Cincotti, Febo

2013-01-01

The purpose of this study was to investigate the support of attentional and memory processes in controlling a P300-based brain-computer interface (BCI) in people with amyotrophic lateral sclerosis (ALS). Eight people with ALS performed two behavioral tasks: (i) a rapid serial visual presentation (RSVP) task, screening the temporal filtering capacity and the speed of the update of the attentive filter, and (ii) a change detection task, screening the memory capacity and the spatial filtering capacity. The participants were also asked to perform a P300-based BCI spelling task. By using correlation and regression analyses, we found that only the temporal filtering capacity in the RSVP task was a predictor of both the P300-based BCI accuracy and of the amplitude of the P300 elicited performing the BCI task. We concluded that the ability to keep the attentional filter active during the selection of a target influences performance in BCI control. PMID:24282396

Quantification of Emphysema: A Bullae Distribution Based Approach

NASA Astrophysics Data System (ADS)

Tan, Kok Liang; Tanaka, Toshiyuki; Nakamura, Hidetoshi; Shirahata, Toru; Sugiura, Hiroaki

Computed tomography (CT)-based quantifications of emphysema encompass, and are not limited to, the ratio of the low-attenuation area, the bullae size, and the distribution of bullae in the lung. The standard CT-based emphysema describing indices include the mean lung density, the percentage of area of low attenuation [the pixel index (PI)] and the bullae index (BI). These standard emphysema describing indices are not expressive for describing the distribution of bullae in the lung. Consequently, the goal of this paper is to present a new emphysema describing index, the bullae congregation index (BCI), that describes whether bullae gather in a specific area of the lung and form a nearly single mass, and if so, how dense the mass of bullae is in the lung. BCI ranges from zero to ten corresponding to sparsely distributed bullae to densely distributed bullae. BCI is calculated based on the relative distance between every pair of bullae in the lung. The bullae pair distances are sorted into 200 distance classes. A smaller distance class corresponds to a closer proximity between the bullae. BCI is derived by calculating the percentage of the area of bullae in the lung that are separated by a certain distance class. Four bullae congregation classes are defined based on BCI. We evaluate BCI using 114 CT images that are hand-annotated by a radiologist into four bullae congregation classes. The average four-class classification accuracy of BCI is 88.21%. BCI correlates better than PI, BI and other standard statistical dispersion based methods with the radiological consensus-classified bullae congregation class.While BCI is not a specific index for indicating emphysema severity, it complements the existing set of emphysema describing indices to facilitate a more thorough knowledge about the emphysematous conditions in the lung. BCI is especially useful when it comes to comparing the distribution of bullae for cases with approximately the same PI, BI or PI and BI. BCI is easy to interpret and potentitally helpful for the purpose of comparative study and progressive monitoring of emphysema.

Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.

PubMed

Cruz, Aniana; Pires, Gabriel; Nunes, Urbano J

2018-01-01

Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.

Progress in EEG-Based Brain Robot Interaction Systems

PubMed Central

Li, Mengfan; Niu, Linwei; Xian, Bin; Zeng, Ming; Chen, Genshe

2017-01-01

The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques. PMID:28484488

The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

PubMed Central

Powers, J. Clark; Bieliaieva, Kateryna; Wu, Shuohao; Nam, Chang S.

2015-01-01

Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs) show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use—i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE) of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1) alternative signal evocation methods within the oddball paradigm; (2) environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3) measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications. PMID:26266424

Self-paced brain-computer interface control of ambulation in a virtual reality environment.

PubMed

Wang, Po T; King, Christine E; Chui, Luis A; Do, An H; Nenadic, Zoran

2012-10-01

Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogram (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over five sessions, in which they utilized the KMI to control the linear ambulation of an avatar and make ten sequential stops at designated points within the VRE. The average offline training performance across subjects was 77.2 ± 11.0%, ranging from 64.3% (p = 0.001 76) to 94.5% (p = 6.26 × 10(-23)), with chance performance being 50%. The average online performance was 8.5 ± 1.1 (out of 10) successful stops and 303 ± 53 s completion time (perfect = 211 s). All subjects achieved performances significantly different than those of random walk (p < 0.05) in 44 of the 45 online sessions. By using a data-driven machine learning approach to decode users' KMI, this BCI-VRE system enabled intuitive and purposeful self-paced control of ambulation after only 10 minutes training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface

NASA Astrophysics Data System (ADS)

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-08-01

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s-1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface.

PubMed

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-08-01

At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s(-1). Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study.

PubMed

Jeunet, Camille; Jahanpour, Emilie; Lotte, Fabien

2016-06-01

While promising, electroencephaloraphy based brain-computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users' motor imagery based BCI (MI-BCI) control performance. We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users' spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced with difficult pre-training, subjects seemed to explore more strategies and therefore learn better.

A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.

PubMed

Jiang, Jun; Zhou, Zongtan; Yin, Erwei; Yu, Yang; Liu, Yadong; Hu, Dewen

2015-11-01

Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting BCI subject performance using probabilistic spatio-temporal filters.

PubMed

Suk, Heung-Il; Fazli, Siamac; Mehnert, Jan; Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms-a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject 'prototypes' (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

On the Relationship Between Attention Processing and P300-Based Brain Computer Interface Control in Amyotrophic Lateral Sclerosis

PubMed Central

Riccio, Angela; Schettini, Francesca; Simione, Luca; Pizzimenti, Alessia; Inghilleri, Maurizio; Olivetti-Belardinelli, Marta; Mattia, Donatella; Cincotti, Febo

2018-01-01

Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition. Thirteen ALS patients and 13 healthy volunteers matched for age and years of education underwent a P3-speller BCI task and a rapid serial visual presentation (RSVP) task. The RSVP task was performed by participants in order to screen their temporal pattern of attentional resource allocation, namely: (i) the temporal attentional filtering capacity (scored as T1%); and (ii) the capability to adequately update the attentive filter in the temporal dynamics of the attentional selection (scored as T2%). For the P3-speller BCI task, the online accuracy and information transfer rate (ITR) were obtained. Centroid Latency and Mean Amplitude of N200 and P300 were also obtained. No significant differences emerged between ALS patients and Controls with regards to online accuracy (p = 0.13). Differently, the performance in controlling the P3-speller expressed as ITR values (calculated offline) were compromised in ALS patients (p < 0.05), with a delay in the latency of P3 when processing BCI stimuli as compared with Control group (p < 0.01). Furthermore, the temporal aspect of attentional filtering which was related to BCI control (r = 0.51; p < 0.05) and to the P3 wave amplitude (r = 0.63; p < 0.05) was also altered in ALS patients (p = 0.01). These findings ground the knowledge required to develop sensible classes of BCI specifically designed by taking into account the influence of the cognitive characteristics of the possible candidates in need of a BCI system for communication. PMID:29892218

Percutaneous versus transcutaneous bone conduction implant system: a feasibility study on a cadaver head.

PubMed

Håkansson, Bo; Eeg-Olofsson, Måns; Reinfeldt, Sabine; Stenfelt, Stefan; Granström, Gösta

2008-12-01

Percutaneous bone-anchored hearing aid (BAHA) is an important rehabilitation alternative for patients who have conductive or mixed hearing loss. However, these devices use a percutaneous and bone-anchored implant that has some drawbacks reported. A transcutaneous bone conduction implant system (BCI) is proposed as an alternative to the percutaneous system because it leaves the skin intact. The BCI transmits the signal to a permanently implanted transducer with an induction loop system through the intact skin. The aim of this study was to compare the electroacoustic performance of the BAHA Classic-300 with a full-scale BCI on a cadaver head in a sound field. The BCI comprised the audio processor of the vibrant sound bridge connected to a balanced vibration transducer (balanced electromagnetic separation transducer). Implants with snap abutments were placed in the parietal bone (Classic-300) and 15-mm deep in the temporal bone (BCI). The vibration responses at the ipsilateral and contralateral cochlear promontories were measured with a laser Doppler vibrometer, with the beam aimed through the ear canal. Results show that the BCI produces approximately 5 dB higher maximum output level and has a slightly lower distortion than the Classic-300 at the ipsilateral promontorium at speech frequencies. At the contralateral promontorium, the maximum output level was considerably lower for the BCI than for the Classic-300 except in the 1-2 kHz range, where it was similar. Present results support the proposal that a BCI system can be a realistic alternative to a BAHA.

A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities

NASA Astrophysics Data System (ADS)

Leeb, Robert; Sagha, Hesam; Chavarriaga, Ricardo; Millán, José del R.

2011-04-01

Hybrid brain-computer interfaces (BCIs) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework we want to explore the parallel usage of electroencephalographic (EEG) and electromyographic (EMG) activity, whereby the control abilities of both channels are fused. Results showed that the participants could achieve a good control of their hybrid BCI independently of their level of muscular fatigue. Thereby the multimodal fusion approach of muscular and brain activity yielded better and more stable performance compared to the single conditions. Even in the case of an increasing muscular fatigue a good control (moderate and graceful degradation of the performance compared to the non-fatigued case) and a smooth handover could be achieved. Therefore, such systems allow the users a very reliable hybrid BCI control although they are getting more and more exhausted or fatigued during the day.

A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme

PubMed Central

Abou Zeid, Elias; Rezazadeh Sereshkeh, Alborz; Schultz, Benjamin; Chau, Tom

2017-01-01

In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have mainly attempted binary single-trial classification of RP. An RP-based BCI with three or more states would expand the options for functional control. Here, we propose a ternary BCI based on single-trial RPs. This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement. A pipeline of spatio-temporal filtering with per participant parameter optimization was used for feature extraction. The ternary classification was decomposed into binary classifications using a decision-directed acyclic graph (DDAG). For each class pair in the DDAG structure, an ordered diversified classifier system (ODCS-DDAG) was used to select the best among various classification algorithms or to combine the results of different classification algorithms. Using EEG data from 14 participants performing self-initiated left or right key presses, punctuated with rest periods, we compared the performance of ODCS-DDAG to a ternary classifier and four popular multiclass decomposition methods using only a single classification algorithm. ODCS-DDAG had the highest performance (0.769 Cohen's Kappa score) and was significantly better than the ternary classifier and two of the four multiclass decomposition methods. Our work supports further study of RP-based BCI for intuitive asynchronous environmental control or augmentative communication. PMID:28596725

Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.

PubMed

Jeunet, Camille; N'Kaoua, Bernard; Subramanian, Sriram; Hachet, Martin; Lotte, Fabien

2015-01-01

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.

Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns

PubMed Central

Jeunet, Camille; N’Kaoua, Bernard; Subramanian, Sriram; Hachet, Martin; Lotte, Fabien

2015-01-01

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy—EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user. PMID:26625261

Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification

PubMed Central

Zhao, Yuwei; Han, Jiuqi; Chen, Yushu; Sun, Hongji; Chen, Jiayun; Ke, Ang; Han, Yao; Zhang, Peng; Zhang, Yi; Zhou, Jin; Wang, Changyong

2018-01-01

Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems. PMID:29867307

Affective SSVEP BCI to effectively control 3D objects by using a prism array-based display

NASA Astrophysics Data System (ADS)

Mun, Sungchul; Park, Min-Chul

2014-06-01

3D objects with depth information can provide many benefits to users in education, surgery, and interactions. In particular, many studies have been done to enhance sense of reality in 3D interaction. Viewing and controlling stereoscopic 3D objects with crossed or uncrossed disparities, however, can cause visual fatigue due to the vergenceaccommodation conflict generally accepted in 3D research fields. In order to avoid the vergence-accommodation mismatch and provide a strong sense of presence to users, we apply a prism array-based display to presenting 3D objects. Emotional pictures were used as visual stimuli in control panels to increase information transfer rate and reduce false positives in controlling 3D objects. Involuntarily motivated selective attention by affective mechanism can enhance steady-state visually evoked potential (SSVEP) amplitude and lead to increased interaction efficiency. More attentional resources are allocated to affective pictures with high valence and arousal levels than to normal visual stimuli such as white-and-black oscillating squares and checkerboards. Among representative BCI control components (i.e., eventrelated potentials (ERP), event-related (de)synchronization (ERD/ERS), and SSVEP), SSVEP-based BCI was chosen in the following reasons. It shows high information transfer rates and takes a few minutes for users to control BCI system while few electrodes are required for obtaining reliable brainwave signals enough to capture users' intention. The proposed BCI methods are expected to enhance sense of reality in 3D space without causing critical visual fatigue to occur. In addition, people who are very susceptible to (auto) stereoscopic 3D may be able to use the affective BCI.

Control-display mapping in brain-computer interfaces.

PubMed

Thurlings, Marieke E; van Erp, Jan B F; Brouwer, Anne-Marie; Blankertz, Benjamin; Werkhoven, Peter

2012-01-01

Event-related potential (ERP) based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. When using a tactile ERP-BCI for navigation, mapping is required between navigation directions on a visual display and unambiguously corresponding tactile stimuli (tactors) from a tactile control device: control-display mapping (CDM). We investigated the effect of congruent (both display and control horizontal or both vertical) and incongruent (vertical display, horizontal control) CDMs on task performance, the ERP and potential BCI performance. Ten participants attended to a target (determined via CDM), in a stream of sequentially vibrating tactors. We show that congruent CDM yields best task performance, enhanced the P300 and results in increased estimated BCI performance. This suggests a reduced availability of attentional resources when operating an ERP-BCI with incongruent CDM. Additionally, we found an enhanced N2 for incongruent CDM, which indicates a conflict between visual display and tactile control orientations. Incongruency in control-display mapping reduces task performance. In this study, brain responses, task and system performance are related to (in)congruent mapping of command options and the corresponding stimuli in a brain-computer interface (BCI). Directional congruency reduces task errors, increases available attentional resources, improves BCI performance and thus facilitates human-computer interaction.

A Discussion of Possibility of Reinforcement Learning Using Event-Related Potential in BCI

NASA Astrophysics Data System (ADS)

Yamagishi, Yuya; Tsubone, Tadashi; Wada, Yasuhiro

Recently, Brain computer interface (BCI) which is a direct connecting pathway an external device such as a computer or a robot and a human brain have gotten a lot of attention. Since BCI can control the machines as robots by using the brain activity without using the voluntary muscle, the BCI may become a useful communication tool for handicapped persons, for instance, amyotrophic lateral sclerosis patients. However, in order to realize the BCI system which can perform precise tasks on various environments, it is necessary to design the control rules to adapt to the dynamic environments. Reinforcement learning is one approach of the design of the control rule. If this reinforcement leaning can be performed by the brain activity, it leads to the attainment of BCI that has general versatility. In this research, we paid attention to P300 of event-related potential as an alternative signal of the reward of reinforcement learning. We discriminated between the success and the failure trials from P300 of the EEG of the single trial by using the proposed discrimination algorithm based on Support vector machine. The possibility of reinforcement learning was examined from the viewpoint of the number of discriminated trials. It was shown that there was a possibility to be able to learn in most subjects.

Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI

PubMed Central

Müller-Putz, Gernot R.; Breitwieser, Christian; Cincotti, Febo; Leeb, Robert; Schreuder, Martijn; Leotta, Francesco; Tavella, Michele; Bianchi, Luigi; Kreilinger, Alex; Ramsay, Andrew; Rohm, Martin; Sagebaum, Max; Tonin, Luca; Neuper, Christa; Millán, José del. R.

2011-01-01

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated. PMID:22131973

Learning-induced Dependence of Neuronal Activity in Primary Motor Cortex on Motor Task Condition.

PubMed

Cai, X; Shimansky, Y; He, Jiping

2005-01-01

A brain-computer interface (BCI) system such as a cortically controlled robotic arm must have a capacity of adjusting its function to a specific environmental condition. We studied this capacity in non-human primates based on chronic multi-electrode recording from the primary motor cortex of a monkey during the animal's performance of a center-out 3D reaching task and adaptation to external force perturbations. The main condition-related feature of motor cortical activity observed before the onset of force perturbation was a phasic raise of activity immediately before the perturbation onset. This feature was observed during a series of perturbation trials, but were absent under no perturbations. After adaptation has been completed, it usually was taking the subject only one trial to recognize a change in the condition to switch the neuronal activity accordingly. These condition-dependent features of neuronal activity can be used by a BCI for recognizing a change in the environmental condition and making corresponding adjustments, which requires that the BCI-based control system possess such advanced properties of the neural motor control system as capacity to learn and adapt.

Feasibility of a Hybrid Brain-Computer Interface for Advanced Functional Electrical Therapy

PubMed Central

Savić, Andrej M.; Malešević, Nebojša M.; Popović, Mirjana B.

2014-01-01

We present a feasibility study of a novel hybrid brain-computer interface (BCI) system for advanced functional electrical therapy (FET) of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP) and event-related desynchronization (ERD). The sequence of the two stages, SSVEP-BCI and ERD-BCI, runs in a closed-loop architecture. The first stage, SSVEP-BCI, acts as a selector of electrical stimulation pattern that corresponds to one of the three basic types of grasp: palmar, lateral, or precision. In the second stage, ERD-BCI operates as a brain switch which activates the stimulation pattern selected in the previous stage. The system was tested in 6 healthy subjects who were all able to control the device with accuracy in a range of 0.64–0.96. The results provided the reference data needed for the planned clinical study. This novel BCI may promote further restoration of the impaired motor function by closing the loop between the “will to move” and contingent temporally synchronized sensory feedback. PMID:24616644

Feasibility of an ultra-low power digital signal processor platform as a basis for a fully implantable brain-computer interface system.

PubMed

Wang, Po T; Gandasetiawan, Keulanna; McCrimmon, Colin M; Karimi-Bidhendi, Alireza; Liu, Charles Y; Heydari, Payam; Nenadic, Zoran; Do, An H

2016-08-01

A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP. The system's signal acquisition fidelity was tested and characterized by acquiring harmonic signals from a function generator. In addition, the BCI decoding performance was tested, first with signals from a function generator, and subsequently using human electroencephalogram (EEG) during eyes opening and closing task. On average, the system spent 322 ms to process and analyze 2 s of data. Crosstalk (<;-65 dB) and harmonic distortion (~1%) were minimal. Timing jitter averaged 49 μs per 1000 ms. The online BCI decoding accuracies were 100% for both function generator and EEG data. These results show that a complex BCI algorithm can be executed on an ULP DSP without compromising performance. This suggests that the proposed hardware platform may be used as a basis for future, fully implantable BCI systems.

Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

NASA Astrophysics Data System (ADS)

Perdikis, S.; Leeb, R.; Williamson, J.; Ramsay, A.; Tavella, M.; Desideri, L.; Hoogerwerf, E.-J.; Al-Khodairy, A.; Murray-Smith, R.; Millán, J. d. R.

2014-06-01

Objective. While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. Approach. This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. Main results. We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. Significance. This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller.

PubMed

Perdikis, S; Leeb, R; Williamson, J; Ramsay, A; Tavella, M; Desideri, L; Hoogerwerf, E-J; Al-Khodairy, A; Murray-Smith, R; Millán, J D R

2014-06-01

While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources.

PubMed

Liu, Yu-Ting; Pal, Nikhil R; Marathe, Amar R; Wang, Yu-Kai; Lin, Chin-Teng

2017-01-01

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.

Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources

PubMed Central

Liu, Yu-Ting; Pal, Nikhil R.; Marathe, Amar R.; Wang, Yu-Kai; Lin, Chin-Teng

2017-01-01

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems. PMID:28676734

Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.

PubMed

Dimitriadis, Stavros I; Marimpis, Avraam D

2018-01-01

A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class ( N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class ( N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class ( N = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5-4 Hz), θ: (4-8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 324 bits/min with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of 10-25 bits/min . In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.

A Steady-State Visual Evoked Potential Brain-Computer Interface System Evaluation as an In-Vehicle Warning Device

NASA Astrophysics Data System (ADS)

Riyahi, Pouria

This thesis is part of current research at Center for Intelligence Systems Research (CISR) at The George Washington University for developing new in-vehicle warning systems via Brain-Computer Interfaces (BCIs). The purpose of conducting this research is to contribute to the current gap between BCI and in-vehicle safety studies. It is based on the premise that accurate and timely monitoring of human (driver) brain's signal to external stimuli could significantly aide in detection of driver's intentions and development of effective warning systems. The thesis starts with introducing the concept of BCI and its development history while it provides a literature review on the nature of brain signals. The current advancement and increasing demand for commercial and non-medical BCI products are described. In addition, the recent research attempts in transportation safety to study drivers' behavior or responses through brain signals are reviewed. The safety studies, which are focused on employing a reliable and practical BCI system as an in-vehicle assistive device, are also introduced. A major focus of this thesis research has been on the evaluation and development of the signal processing algorithms which can effectively filter and process brain signals when the human subject is subjected to Visual LED (Light Emitting Diodes) stimuli at different frequencies. The stimulated brain generates a voltage potential, referred to as Steady-State Visual Evoked Potential (SSVEP). Therefore, a newly modified analysis algorithm for detecting the brain visual signals is proposed. These algorithms are designed to reach a satisfactory accuracy rate without preliminary trainings, hence focusing on eliminating the need for lengthy training of human subjects. Another important concern is the ability of the algorithms to find correlation of brain signals with external visual stimuli in real-time. The developed analysis models are based on algorithms which are capable of generating results for real-time processing of BCI devices. All of these methods are evaluated through two sets of recorded brain signals which were recorded by g.TEC CO. as an external source and recorded brain signals during our car driving simulator experiments. The final discussion is about how the presence of an SSVEP based warning system could affect drivers' performances which is defined by their reaction distance and Time to Collision (TTC). Three different scenarios with and without warning LEDs were planned to measure the subjects' normal driving behavior and their performance while they use a warning system during their driving task. Finally, warning scenarios are divided into short and long warning periods without and with informing the subjects, respectively. The long warning period scenario attempts to determine the level of drivers' distraction or vigilance during driving. The good outcome of warning scenarios can bridge between vehicle safety studies and online BCI system design research. The preliminary results show some promise of the developed methods for in-vehicle safety systems. However, for any decisive conclusion that considers using a BCI system as a helpful in-vehicle assistive device requires far deeper scrutinizing.

Brain-computer interface after nervous system injury.

PubMed

Burns, Alexis; Adeli, Hojjat; Buford, John A

2014-12-01

Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. © The Author(s) 2014.

Circadian course of the P300 ERP in patients with amyotrophic lateral sclerosis - implications for brain-computer interfaces (BCI).

PubMed

Erlbeck, Helena; Mochty, Ursula; Kübler, Andrea; Real, Ruben G L

2017-01-07

Accidents or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) can lead to progressing, extensive, and complete paralysis leaving patients aware but unable to communicate (locked-in state). Brain-computer interfaces (BCI) based on electroencephalography represent an important approach to establish communication with these patients. The most common BCI for communication rely on the P300, a positive deflection arising in response to rare events. To foster broader application of BCIs for restoring lost function, also for end-users with impaired vision, we explored whether there were specific time windows during the day in which a P300 driven BCI should be preferably applied. The present study investigated the influence of time of the day and modality (visual vs. auditory) on P300 amplitude and latency. A sample of 14 patients (end-users) with ALS and 14 healthy age matched volunteers participated in the study and P300 event-related potentials (ERP) were recorded at four different times (10, 12 am, 2, & 4 pm) during the day. Results indicated no differences in P300 amplitudes or latencies between groups (ALS patients v. healthy participants) or time of measurement. In the auditory condition, latencies were shorter and amplitudes smaller as compared to the visual condition. Our findings suggest applicability of EEG/BCI sessions in patients with ALS throughout normal waking hours. Future studies using actual BCI systems are needed to generalize these findings with regard to BCI effectiveness/efficiency and other times of day.

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study

PubMed Central

Kapeller, Christoph; Kamada, Kyousuke; Ogawa, Hiroshi; Prueckl, Robert; Scharinger, Josef; Guger, Christoph

2014-01-01

A brain-computer-interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on visual stimuli with constant stimulation cycles that elicit steady-state visual evoked potentials (SSVEP) in the electroencephalogram (EEG). This EEG response can be generated with a LED or a computer screen flashing at a constant frequency, and similar EEG activity can be elicited with pseudo-random stimulation sequences on a screen (code-based BCI). Using electrocorticography (ECoG) instead of EEG promises higher spatial and temporal resolution and leads to more dominant evoked potentials due to visual stimulation. This work is focused on BCIs based on visual evoked potentials (VEP) and its capability as a continuous control interface for augmentation of video applications. One 35 year old female subject with implanted subdural grids participated in the study. The task was to select one out of four visual targets, while each was flickering with a code sequence. After a calibration run including 200 code sequences, a linear classifier was used during an evaluation run to identify the selected visual target based on the generated code-based VEPs over 20 trials. Multiple ECoG buffer lengths were tested and the subject reached a mean online classification accuracy of 99.21% for a window length of 3.15 s. Finally, the subject performed an unsupervised free run in combination with visual feedback of the current selection. Additionally, an algorithm was implemented that allowed to suppress false positive selections and this allowed the subject to start and stop the BCI at any time. The code-based BCI system attained very high online accuracy, which makes this approach very promising for control applications where a continuous control signal is needed. PMID:25147509

Eyes-closed hybrid brain-computer interface employing frontal brain activation.

PubMed

Shin, Jaeyoung; Müller, Klaus-Robert; Hwang, Han-Jeong

2018-01-01

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroencephalography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA- from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 ± 10.3%) was shown to be significantly higher than those of unimodal EEG-based (77.3 ± 15.9%) and NIRS-based BCI (75.9 ± 6.3%). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.

Effects of brain-computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results

PubMed Central

Chung, EunJung; Park, Sang-In; Jang, Yun-Yung; Lee, Byoung-Hee

2015-01-01

[Purpose] The purpose of this study was to determine the effects of brain-computer interface (BCI)-based functional electrical stimulation (FES) on balance and gait function in patients with stroke. [Subjects] Subjects were randomly allocated to a BCI-FES group (n=5) and a FES group (n=5). [Methods] The BCI-FES group received ankle dorsiflexion training with FES according to a BCI-based program for 30 minutes per day for 5 days. The FES group received ankle dorsiflexion training with FES for the same duration. [Results] Following the intervention, the BCI-FES group showed significant differences in Timed Up and Go test value, cadence, and step length on the affected side. The FES group showed no significant differences after the intervention. However, there were no significant differences between the 2 groups after the intervention. [Conclusion] The results of this study suggest that BCI-based FES training is a more effective exercise for balance and gait function than FES training alone in patients with stroke. PMID:25729205

Effects of brain-computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results.

PubMed

Chung, EunJung; Park, Sang-In; Jang, Yun-Yung; Lee, Byoung-Hee

2015-02-01

[Purpose] The purpose of this study was to determine the effects of brain-computer interface (BCI)-based functional electrical stimulation (FES) on balance and gait function in patients with stroke. [Subjects] Subjects were randomly allocated to a BCI-FES group (n=5) and a FES group (n=5). [Methods] The BCI-FES group received ankle dorsiflexion training with FES according to a BCI-based program for 30 minutes per day for 5 days. The FES group received ankle dorsiflexion training with FES for the same duration. [Results] Following the intervention, the BCI-FES group showed significant differences in Timed Up and Go test value, cadence, and step length on the affected side. The FES group showed no significant differences after the intervention. However, there were no significant differences between the 2 groups after the intervention. [Conclusion] The results of this study suggest that BCI-based FES training is a more effective exercise for balance and gait function than FES training alone in patients with stroke.

Co-Adaptive Aiding and Automation Enhance Operator Performance

DTIC Science & Technology

2013-03-01

activation system. There is a close relation between physiologically activated adaptive aiding and brain- computer interfaces ( BCI ). BCI here refers...classification of EEG signals (Farwell & Donchin, 1988). Physiologically activated adaptive aiding is, in a sense, a special case of BCI wherein the...as passive BCI , e.g. Zander, Kothe, Jatzev, & 3 Distribution A: Approved for public release; distribution unlimited. 88 ABW Cleared 05/13/2013

Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

NASA Astrophysics Data System (ADS)

Jeunet, Camille; Jahanpour, Emilie; Lotte, Fabien

2016-06-01

Objective. While promising, electroencephaloraphy based brain-computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users’ motor imagery based BCI (MI-BCI) control performance. Approach. We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users’ spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main results. Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance. These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced with difficult pre-training, subjects seemed to explore more strategies and therefore learn better.

A Collaborative Brain-Computer Interface for Improving Human Performance

PubMed Central

Wang, Yijun; Jung, Tzyy-Ping

2011-01-01

Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100–250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior. PMID:21655253

On the Use of Electrooculogram for Efficient Human Computer Interfaces

PubMed Central

Usakli, A. B.; Gurkan, S.; Aloise, F.; Vecchiato, G.; Babiloni, F.

2010-01-01

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as “clean-up” could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes. PMID:19841687

Write, read and answer emails with a dry 'n' wireless brain-computer interface system.

PubMed

Pinegger, Andreas; Deckert, Lisa; Halder, Sebastian; Barry, Norbert; Faller, Josef; Käthner, Ivo; Hintermüller, Christoph; Wriessnegger, Selina C; Kübler, Andrea; Müller-Putz, Gernot R

2014-01-01

Brain-computer interface (BCI) users can control very complex applications such as multimedia players or even web browsers. Therefore, different biosignal acquisition systems are available to noninvasively measure the electrical activity of the brain, the electroencephalogram (EEG). To make BCIs more practical, hardware and software are nowadays designed more user centered and user friendly. In this paper we evaluated one of the latest innovations in the area of BCI: A wireless EEG amplifier with dry electrode technology combined with a web browser which enables BCI users to use standard webmail. With this system ten volunteers performed a daily life task: Write, read and answer an email. Experimental results of this study demonstrate the power of the introduced BCI system.

Feasibility of an EEG-based brain-computer interface in the intensive care unit.

PubMed

Chatelle, Camille; Spencer, Camille A; Cash, Sydney S; Hochberg, Leigh R; Edlow, Brian L

2018-05-09

We tested the feasibility of deploying a commercially available EEG-based brain-computer interface (BCI) in the intensive care unit (ICU) to detect consciousness in patients with acute disorders of consciousness (DoC) or locked-in syndrome (LIS). Ten patients (9 DoC, 1 LIS) and 10 healthy subjects (HS) were enrolled. The BCI utilized oddball auditory evoked potentials, vibrotactile evoked potentials (VTP) and motor imagery (MoI) to assess consciousness. We recorded the assessment completion rate and the time required for assessment, and we calculated the sensitivity and specificity of each paradigm for detecting behavioral signs of consciousness. All 10 patients completed the assessment, 9 of whom required less than 1 h. The LIS patient reported fatigue before the end of the session. The HS and LIS patient showed more consistent BCI responses than DoC patients, but overall there was no association between BCI responses and behavioral signs of consciousness. The system is feasible to deploy in the ICU and may confirm consciousness in acute LIS, but it was unreliable in acute DoC. The accuracy of the paradigms for detecting consciousness must be improved and the duration of the protocol should be shortened before this commercially available BCI is ready for clinical implementation in the ICU in patients with acute DoC. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children

NASA Astrophysics Data System (ADS)

Kinney-Lang, E.; Auyeung, B.; Escudero, J.

2016-12-01

Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. • BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. • A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. • Indirect studies discovered from the systematic literature search, i.e. neurorehabilitation in children via BCI for autism spectrum disorder, provide insight into translating motor rehabilitation BCI applications to children. • Translating BCI applications to children is a relevant, important area of research which is relatively barren.

Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children.

PubMed

Kinney-Lang, E; Auyeung, B; Escudero, J

2016-12-01

Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. •  BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. •  A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. •  Indirect studies discovered from the systematic literature search, i.e. neurorehabilitation in children via BCI for autism spectrum disorder, provide insight into translating motor rehabilitation BCI applications to children. •  Translating BCI applications to children is a relevant, important area of research which is relatively barren.

A Procedure for Measuring Latencies in Brain-Computer Interfaces

PubMed Central

Wilson, J. Adam; Mellinger, Jürgen; Schalk, Gerwin; Williams, Justin

2011-01-01

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware, software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration. PMID:20403781

Functional recovery in upper limb function in stroke survivors by using brain-computer interface A single case A-B-A-B design.

PubMed

Ono, Takashi; Mukaino, Masahiko; Ushiba, Junichi

2013-01-01

Resent studies suggest that brain-computer interface (BCI) training for chronic stroke patient is useful to improve their motor function of paretic hand. However, these studies does not show the extent of the contribution of the BCI clearly because they prescribed BCI with other rehabilitation systems, e.g. an orthosis itself, a robotic intervention, or electrical stimulation. We therefore compared neurological effects between interventions with neuromuscular electrical stimulation (NMES) with motor imagery and BCI-driven NMES, employing an ABAB experimental design. In epoch A, the subject received NMES on paretic extensor digitorum communis (EDC). The subject was asked to attempt finger extension simultaneously. In epoch B, the subject received NMES when BCI system detected motor-related electroencephalogram change while attempting motor imagery. Both epochs were carried out for 60 min per day, 5 days per week. As a result, EMG activity of EDC was enhanced by BCI-driven NMES and significant cortico-muscular coherence was observed at the final evaluation. These results indicate that the training by BCI-driven NMES is effective even compared to motor imagery combined with NMES, suggesting the superiority of closed-loop training with BCI-driven NMES to open-loop NMES for chronic stroke patients.

A UML model for the description of different brain-computer interface systems.

PubMed

Quitadamo, Lucia Rita; Abbafati, Manuel; Saggio, Giovanni; Marciani, Maria Grazia; Cardarilli, Gian Carlo; Bianchi, Luigi

2008-01-01

BCI research lacks a universal descriptive language among labs and a unique standard model for the description of BCI systems. This results in a serious problem in comparing performances of different BCI processes and in unifying tools and resources. In such a view we implemented a Unified Modeling Language (UML) model for the description virtually of any BCI protocol and we demonstrated that it can be successfully applied to the most common ones such as P300, mu-rhythms, SCP, SSVEP, fMRI. Finally we illustrated the advantages in utilizing a standard terminology for BCIs and how the same basic structure can be successfully adopted for the implementation of new systems.

Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.

PubMed

Shu, Xiaokang; Chen, Shugeng; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Jia, Jie; Zhu, Xiangyang

2018-01-01

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732, p < 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance ( r = 0.641, p < 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.

Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

PubMed Central

Shu, Xiaokang; Chen, Shugeng; Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Jia, Jie; Zhu, Xiangyang

2018-01-01

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10–50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = −0.732, p < 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r = 0.641, p < 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients. PMID:29515363

From Innovative Programs to Systemic Education Reform: Lesson from Five Communities. The Final Report of the Benchmark Communities Initiative.

ERIC Educational Resources Information Center

DeSalvatore, Larry; Goldberger, Susan; Steinberg, Adria

This document presents the lessons of Jobs for the Future's Benchmark Communities Initiative (BCI), a 5-year systemic educational reform initiative launched in 1994 in five communities. Before joining the BCI, the five Benchmark communities had each begun a school-to-career effort. Five key findings from the BCI are outlined: (1) students engaged…

Ark and Archive: Making a Place for Long-Term Research on Barro Colorado Island, Panama.

PubMed

Raby, Megan

2015-12-01

Barro Colorado Island (BCI), Panama, may be the most studied tropical forest in the world. A 1,560-hectare island created by the flooding of the Panama Canal, BCI became a nature reserve and biological research station in 1923. Contemporaries saw the island as an "ark" preserving a sample of primeval tropical nature for scientific study. BCI was not simply "set aside," however. The project of making it a place for science significantly reshaped the island through the twentieth century. This essay demonstrates that BCI was constructed specifically to allow long-term observation of tropical organisms--their complex behaviors, life histories, population dynamics, and changing species composition. An evolving system of monitoring and information technology transformed the island into a living scientific "archive," in which the landscape became both an object and a repository of scientific knowledge. As a research site, BCI enabled a long-term, place-based form of collective empiricism, focused on the study of the ecology of a single tropical island. This essay articulates tropical ecology as a "science of the archive" in order to examine the origins of practices of environmental surveillance that have become central to debates about global change and conservation.

Multiresolution analysis over graphs for a motor imagery based online BCI game.

PubMed

Asensio-Cubero, Javier; Gan, John Q; Palaniappan, Ramaswamy

2016-01-01

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A novel brain-computer interface based on the rapid serial visual presentation paradigm.

PubMed

Acqualagna, Laura; Treder, Matthias Sebastian; Schreuder, Martijn; Blankertz, Benjamin

2010-01-01

Most present-day visual brain computer interfaces (BCIs) suffer from the fact that they rely on eye movements, are slow-paced, or feature a small vocabulary. As a potential remedy, we explored a novel BCI paradigm consisting of a central rapid serial visual presentation (RSVP) of the stimuli. It has a large vocabulary and realizes a BCI system based on covert non-spatial selective visual attention. In an offline study, eight participants were presented sequences of rapid bursts of symbols. Two different speeds and two different color conditions were investigated. Robust early visual and P300 components were elicited time-locked to the presentation of the target. Offline classification revealed a mean accuracy of up to 90% for selecting the correct symbol out of 30 possibilities. The results suggest that RSVP-BCI is a promising new paradigm, also for patients with oculomotor impairments.

Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups.

PubMed

Hsu, Hao-Teng; Lee, I-Hui; Tsai, Han-Ting; Chang, Hsiang-Chih; Shyu, Kuo-Kai; Hsu, Chuan-Chih; Chang, Hsiao-Huang; Yeh, Ting-Kuang; Chang, Chun-Yen; Lee, Po-Lei

2016-05-01

This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

PubMed

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

PubMed

Combaz, Adrien; Van Hulle, Marc M

2015-01-01

We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI. We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline. Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.

A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.

PubMed

Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Mrachacz-Kersting, Natalie; Zhu, Xiangyang; Farina, Dario

2017-09-01

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain-computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems. Thirty two subjects were randomly divided into a Control-Group and a Hybrid-Group. In the Control-Group, the subjects performed left and right hand motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the subjects performed the four mental tasks (i.e., L-MI, R-MI, L-SAO, and R-SAO). The results indicate that combining two of the tasks in a hybrid manner (such as L-SAO and R-MI) resulted in a significantly greater classification accuracy than when using two MI tasks. The hybrid modality reached 86.1% classification accuracy on average, with a 7.70% increase with respect to MI ( ), and 7.21% to SAO ( ) alone. Moreover, all 16 subjects in the hybrid modality reached at least 70% accuracy, which is considered the threshold for BCI illiteracy. In addition to the two-class results, the classification accuracy was 68.1% and 54.1% for the three-class and four-class hybrid BCI. Combining the induced brain signals from motor and somatosensory cortex, the proposed stimulus-independent hybrid BCI has shown improved performance with respect to individual modalities, reducing the portion of BCI-illiterate subjects, and provided novel types of multi-class BCIs.

Biased feedback in brain-computer interfaces.

PubMed

Barbero, Alvaro; Grosse-Wentrup, Moritz

2010-07-27

Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level.

A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform.

PubMed

Elnady, Ahmed Mohamed; Zhang, Xin; Xiao, Zhen Gang; Yong, Xinyi; Randhawa, Bubblepreet Kaur; Boyd, Lara; Menon, Carlo

2015-01-01

Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain-computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants' ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke.

A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform

PubMed Central

Elnady, Ahmed Mohamed; Zhang, Xin; Xiao, Zhen Gang; Yong, Xinyi; Randhawa, Bubblepreet Kaur; Boyd, Lara; Menon, Carlo

2015-01-01

Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain–computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants’ ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke. PMID:25870554

Robotic and Virtual Reality BCIs Using Spatial Tactile and Auditory Oddball Paradigms.

PubMed

Rutkowski, Tomasz M

2016-01-01

The paper reviews nine robotic and virtual reality (VR) brain-computer interface (BCI) projects developed by the author, in collaboration with his graduate students, within the BCI-lab research group during its association with University of Tsukuba, Japan. The nine novel approaches are discussed in applications to direct brain-robot and brain-virtual-reality-agent control interfaces using tactile and auditory BCI technologies. The BCI user intentions are decoded from the brainwaves in realtime using a non-invasive electroencephalography (EEG) and they are translated to a symbiotic robot or virtual reality agent thought-based only control. A communication protocol between the BCI output and the robot or the virtual environment is realized in a symbiotic communication scenario using an user datagram protocol (UDP), which constitutes an internet of things (IoT) control scenario. Results obtained from healthy users reproducing simple brain-robot and brain-virtual-agent control tasks in online experiments support the research goal of a possibility to interact with robotic devices and virtual reality agents using symbiotic thought-based BCI technologies. An offline BCI classification accuracy boosting method, using a previously proposed information geometry derived approach, is also discussed in order to further support the reviewed robotic and virtual reality thought-based control paradigms.

A reductionist approach to the analysis of learning in brain-computer interfaces.

PubMed

Danziger, Zachary

2014-04-01

The complexity and scale of brain-computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.

Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Power, Sarah D.; Falk, Tiago H.; Chau, Tom

2010-04-01

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% ± 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.

EEG controlled neuromuscular electrical stimulation of the upper limb for stroke patients

NASA Astrophysics Data System (ADS)

Tan, Hock Guan; Shee, Cheng Yap; Kong, Keng He; Guan, Cuntai; Ang, Wei Tech

2011-03-01

This paper describes the Brain Computer Interface (BCI) system and the experiments to allow post-acute (<3 months) stroke patients to use electroencephalogram (EEG) to trigger neuromuscular electrical stimulation (NMES)-assisted extension of the wrist/fingers, which are essential pre-requisites for useful hand function. EEG was recorded while subjects performed motor imagery of their paretic limb, and then analyzed to determine the optimal frequency range within the mu-rhythm, with the greatest attenuation. Aided by visual feedback, subjects then trained to regulate their mu-rhythm EEG to operate the BCI to trigger NMES of the wrist/finger. 6 post-acute stroke patients successfully completed the training, with 4 able to learn to control and use the BCI to initiate NMES. This result is consistent with the reported BCI literacy rate of healthy subjects. Thereafter, without the loss of generality, the controller of the NMES is developed and is based on a model of the upper limb muscle (biceps/triceps) groups to determine the intensity of NMES required to flex or extend the forearm by a specific angle. The muscle model is based on a phenomenological approach, with parameters that are easily measured and conveniently implemented.

Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users.

PubMed

Rutkowski, Tomasz M; Mori, Hiromu

2015-04-15

The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm. In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI. Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies. We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques. The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research. Copyright © 2014 Elsevier B.V. All rights reserved.

Psychological Predictors of Visual and Auditory P300 Brain-Computer Interface Performance

PubMed Central

Hammer, Eva M.; Halder, Sebastian; Kleih, Sonja C.; Kübler, Andrea

2018-01-01

Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor “emotional stability” was negatively correlated (Spearman's rho = −0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. “Emotional stability” was also negatively related to auditory P300-BCI performance (Spearman's rho = −0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. “Emotional stability” was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors. PMID:29867319

Effect of biased feedback on motor imagery learning in BCI-teleoperation system.

PubMed

Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

2014-01-01

Feedback design is an important issue in motor imagery BCI systems. Regardless, to date it has not been reported how feedback presentation can optimize co-adaptation between a human brain and such systems. This paper assesses the effect of realistic visual feedback on users' BCI performance and motor imagery skills. We previously developed a tele-operation system for a pair of humanlike robotic hands and showed that BCI control of such hands along with first-person perspective visual feedback of movements can arouse a sense of embodiment in the operators. In the first stage of this study, we found that the intensity of this ownership illusion was associated with feedback presentation and subjects' performance during BCI motion control. In the second stage, we probed the effect of positive and negative feedback bias on subjects' BCI performance and motor imagery skills. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects' online performance, evaluation of brain activity patterns revealed that subjects' self-regulation of motor imagery features improved due to a positive bias of feedback and a possible occurrence of ownership illusion. Our findings suggest that in general training protocols for BCIs, manipulation of feedback can play an important role in the optimization of subjects' motor imagery skills.

Decoding of intended saccade direction in an oculomotor brain-computer interface

NASA Astrophysics Data System (ADS)

Jia, Nan; Brincat, Scott L.; Salazar-Gómez, Andrés F.; Panko, Mikhail; Guenther, Frank H.; Miller, Earl K.

2017-08-01

Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from the hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for ‘point and click’ computer operation as used in most current AAC systems.

An independent brain-computer interface using covert non-spatial visual selective attention

NASA Astrophysics Data System (ADS)

Zhang, Dan; Maye, Alexander; Gao, Xiaorong; Hong, Bo; Engel, Andreas K.; Gao, Shangkai

2010-02-01

In this paper, a novel independent brain-computer interface (BCI) system based on covert non-spatial visual selective attention of two superimposed illusory surfaces is described. Perception of two superimposed surfaces was induced by two sets of dots with different colors rotating in opposite directions. The surfaces flickered at different frequencies and elicited distinguishable steady-state visual evoked potentials (SSVEPs) over parietal and occipital areas of the brain. By selectively attending to one of the two surfaces, the SSVEP amplitude at the corresponding frequency was enhanced. An online BCI system utilizing the attentional modulation of SSVEP was implemented and a 3-day online training program with healthy subjects was carried out. The study was conducted with Chinese subjects at Tsinghua University, and German subjects at University Medical Center Hamburg-Eppendorf (UKE) using identical stimulation software and equivalent technical setup. A general improvement of control accuracy with training was observed in 8 out of 18 subjects. An averaged online classification accuracy of 72.6 ± 16.1% was achieved on the last training day. The system renders SSVEP-based BCI paradigms possible for paralyzed patients with substantial head or ocular motor impairments by employing covert attention shifts instead of changing gaze direction.

An independent brain-computer interface using covert non-spatial visual selective attention.

PubMed

Zhang, Dan; Maye, Alexander; Gao, Xiaorong; Hong, Bo; Engel, Andreas K; Gao, Shangkai

2010-02-01

In this paper, a novel independent brain-computer interface (BCI) system based on covert non-spatial visual selective attention of two superimposed illusory surfaces is described. Perception of two superimposed surfaces was induced by two sets of dots with different colors rotating in opposite directions. The surfaces flickered at different frequencies and elicited distinguishable steady-state visual evoked potentials (SSVEPs) over parietal and occipital areas of the brain. By selectively attending to one of the two surfaces, the SSVEP amplitude at the corresponding frequency was enhanced. An online BCI system utilizing the attentional modulation of SSVEP was implemented and a 3-day online training program with healthy subjects was carried out. The study was conducted with Chinese subjects at Tsinghua University, and German subjects at University Medical Center Hamburg-Eppendorf (UKE) using identical stimulation software and equivalent technical setup. A general improvement of control accuracy with training was observed in 8 out of 18 subjects. An averaged online classification accuracy of 72.6 +/- 16.1% was achieved on the last training day. The system renders SSVEP-based BCI paradigms possible for paralyzed patients with substantial head or ocular motor impairments by employing covert attention shifts instead of changing gaze direction.

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

PubMed

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

PubMed

Siuly; Li, Yan; Paul Wen, Peng

2014-03-01

Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Probabilistic co-adaptive brain-computer interfacing

NASA Astrophysics Data System (ADS)

Bryan, Matthew J.; Martin, Stefan A.; Cheung, Willy; Rao, Rajesh P. N.

2013-12-01

Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

PubMed

Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

2018-01-01

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

Concept of software interface for BCI systems

NASA Astrophysics Data System (ADS)

Svejda, Jaromir; Zak, Roman; Jasek, Roman

2016-06-01

Brain Computer Interface (BCI) technology is intended to control external system by brain activity. One of main part of such system is software interface, which carries about clear communication between brain and either computer or additional devices connected to computer. This paper is organized as follows. Firstly, current knowledge about human brain is briefly summarized to points out its complexity. Secondly, there is described a concept of BCI system, which is then used to build an architecture of proposed software interface. Finally, there are mentioned disadvantages of sensing technology discovered during sensing part of our research.

Asynchronous steady-state visual evoked potential based BCI control of a 2-DoF artificial upper limb.

PubMed

Horki, Petar; Neuper, Christa; Pfurtscheller, Gert; Müller-Putz, Gernot

2010-12-01

A brain-computer interface (BCI) provides a direct connection between the human brain and a computer. One type of BCI can be realized using steady-state visual evoked potentials (SSVEPs), resulting from repetitive stimulation. The aim of this study was the realization of an asynchronous SSVEP-BCI, based on canonical correlation analysis, suitable for the control of a 2-degrees of freedom (DoF) hand and elbow neuroprosthesis. To determine whether this BCI is suitable for the control of 2-DoF neuroprosthetic devices, online experiments with a virtual and a robotic limb feedback were conducted with eight healthy subjects and one tetraplegic patient. All participants were able to control the artificial limbs with the BCI. In the online experiments, the positive predictive value (PPV) varied between 69% and 83% and the false negative rate (FNR) varied between 1% and 17%. The spinal cord injured patient achieved PPV and FNR values within one standard deviation of the mean for all healthy subjects.

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

PubMed Central

Käthner, Ivo; Halder, Sebastian; Hintermüller, Christoph; Espinosa, Arnau; Guger, Christoph; Miralles, Felip; Vargiu, Eloisa; Dauwalder, Stefan; Rafael-Palou, Xavier; Solà, Marc; Daly, Jean M.; Armstrong, Elaine; Martin, Suzanne; Kübler, Andrea

2017-01-01

Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis. PMID:28588442

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes.

PubMed

Käthner, Ivo; Halder, Sebastian; Hintermüller, Christoph; Espinosa, Arnau; Guger, Christoph; Miralles, Felip; Vargiu, Eloisa; Dauwalder, Stefan; Rafael-Palou, Xavier; Solà, Marc; Daly, Jean M; Armstrong, Elaine; Martin, Suzanne; Kübler, Andrea

2017-01-01

Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes ( N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.

Asynchronous P300-based brain-computer interface to control a virtual environment: initial tests on end users.

PubMed

Aloise, Fabio; Schettini, Francesca; Aricò, Pietro; Salinari, Serenella; Guger, Christoph; Rinsma, Johanna; Aiello, Marco; Mattia, Donatella; Cincotti, Febo

2011-10-01

Motor disability and/or ageing can prevent individuals from fully enjoying home facilities, thus worsening their quality of life. Advances in the field of accessible user interfaces for domotic appliances can represent a valuable way to improve the independence of these persons. An asynchronous P300-based Brain-Computer Interface (BCI) system was recently validated with the participation of healthy young volunteers for environmental control. In this study, the asynchronous P300-based BCI for the interaction with a virtual home environment was tested with the participation of potential end-users (clients of a Frisian home care organization) with limited autonomy due to ageing and/or motor disabilities. System testing revealed that the minimum number of stimulation sequences needed to achieve correct classification had a higher intra-subject variability in potential end-users with respect to what was previously observed in young controls. Here we show that the asynchronous modality performed significantly better as compared to the synchronous mode in continuously adapting its speed to the users' state. Furthermore, the asynchronous system modality confirmed its reliability in avoiding misclassifications and false positives, as previously shown in young healthy subjects. The asynchronous modality may contribute to filling the usability gap between BCI systems and traditional input devices, representing an important step towards their use in the activities of daily living.

A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm

NASA Astrophysics Data System (ADS)

Yin, Erwei; Zhou, Zongtan; Jiang, Jun; Chen, Fanglin; Liu, Yadong; Hu, Dewen

2013-04-01

Objective. Although extensive studies have shown improvement in spelling accuracy, the conventional P300 speller often exhibits errors, which occur in almost the same row or column relative to the target. To address this issue, we propose a novel hybrid brain-computer interface (BCI) approach by incorporating the steady-state visual evoked potential (SSVEP) into the conventional P300 paradigm. Approach. We designed a periodic stimuli mechanism and superimposed it onto the P300 stimuli to increase the difference between the symbols in the same row or column. Furthermore, we integrated the random flashings and periodic flickers to simultaneously evoke the P300 and SSVEP, respectively. Finally, we developed a hybrid detection mechanism based on the P300 and SSVEP in which the target symbols are detected by the fusion of three-dimensional, time-frequency features. Main results. The results obtained from 12 healthy subjects show that an online classification accuracy of 93.85% and information transfer rate of 56.44 bit/min were achieved using the proposed BCI speller in only a single trial. Specifically, 5 of the 12 subjects exhibited an information transfer rate of 63.56 bit/min with an accuracy of 100%. Significance. The pilot studies suggested that the proposed BCI speller could achieve a better and more stable system performance compared with the conventional P300 speller, and it is promising for achieving quick spelling in stimulus-driven BCI applications.

Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices

NASA Astrophysics Data System (ADS)

Klein, Eran; Ojemann, Jeffrey

2016-08-01

Objective. Implantable brain-computer interface (BCI) research promises improvements in human health and enhancements in quality of life. Informed consent of subjects is a central tenet of this research. Rapid advances in neuroscience, and the intimate connection between functioning of the brain and conceptions of the self, make informed consent particularly challenging in BCI research. Identification of safety and research-related risks associated with BCI devices is an important step in ensuring meaningful informed consent. Approach. This paper highlights a number of BCI research risks, including safety concerns, cognitive and communicative impairments, inappropriate subject expectations, group vulnerabilities, privacy and security, and disruptions of identity. Main results. Based on identified BCI research risks, best practices are needed for understanding and incorporating BCI-related risks into informed consent protocols. Significance. Development of best practices should be guided by processes that are: multidisciplinary, systematic and transparent, iterative, relational and exploratory.

Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices.

PubMed

Klein, Eran; Ojemann, Jeffrey

2016-08-01

Implantable brain-computer interface (BCI) research promises improvements in human health and enhancements in quality of life. Informed consent of subjects is a central tenet of this research. Rapid advances in neuroscience, and the intimate connection between functioning of the brain and conceptions of the self, make informed consent particularly challenging in BCI research. Identification of safety and research-related risks associated with BCI devices is an important step in ensuring meaningful informed consent. This paper highlights a number of BCI research risks, including safety concerns, cognitive and communicative impairments, inappropriate subject expectations, group vulnerabilities, privacy and security, and disruptions of identity. Based on identified BCI research risks, best practices are needed for understanding and incorporating BCI-related risks into informed consent protocols. Development of best practices should be guided by processes that are: multidisciplinary, systematic and transparent, iterative, relational and exploratory.

Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification.

PubMed

Dehzangi, Omid; Farooq, Muhamed

2018-01-01

A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.

A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

PubMed Central

Ahn, Minkyu; Lee, Mijin; Choi, Jinyoung; Jun, Sung Chan

2014-01-01

In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to “the easiness of playing” and the “development platform” as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration. PMID:25116904

A review of brain-computer interface games and an opinion survey from researchers, developers and users.

PubMed

Ahn, Minkyu; Lee, Mijin; Choi, Jinyoung; Jun, Sung Chan

2014-08-11

In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to "the easiness of playing" and the "development platform" as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.

Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

PubMed

Mahmoudi, Babak; Erfanian, Abbas

2006-11-01

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

PubMed

Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

2017-01-01

Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

Orientation-modulated attention effect on visual evoked potential: Application for PIN system using brain-computer interface.

PubMed

Wilaiprasitporn, Theerawit; Yagi, Tohru

2015-01-01

This research demonstrates the orientation-modulated attention effect on visual evoked potential. We combined this finding with our previous findings about the motion-modulated attention effect and used the result to develop novel visual stimuli for a personal identification number (PIN) application based on a brain-computer interface (BCI) framework. An electroencephalography amplifier with a single electrode channel was sufficient for our application. A computationally inexpensive algorithm and small datasets were used in processing. Seven healthy volunteers participated in experiments to measure offline performance. Mean accuracy was 83.3% at 13.9 bits/min. Encouraged by these results, we plan to continue developing the BCI-based personal identification application toward real-time systems.

An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

PubMed Central

Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup. PMID:25384045

An efficient ERP-based brain-computer interface using random set presentation and face familiarity.

PubMed

Yeom, Seul-Ki; Fazli, Siamac; Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

PubMed Central

Shin, Jaeyoung; Müller, Klaus-R; Hwang, Han-Jeong

2016-01-01

We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently. PMID:27824089

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic.

PubMed

Shin, Jaeyoung; Müller, Klaus-R; Hwang, Han-Jeong

2016-11-08

We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently.

Student teaching and research laboratory focusing on brain-computer interface paradigms--A creative environment for computer science students.

PubMed

Rutkowski, Tomasz M

2015-08-01

This paper presents an applied concept of a brain-computer interface (BCI) student research laboratory (BCI-LAB) at the Life Science Center of TARA, University of Tsukuba, Japan. Several successful case studies of the student projects are reviewed together with the BCI Research Award 2014 winner case. The BCI-LAB design and project-based teaching philosophy is also explained. Future teaching and research directions summarize the review.

A novel BCI based on ERP components sensitive to configural processing of human faces

NASA Astrophysics Data System (ADS)

Zhang, Yu; Zhao, Qibin; Jing, Jin; Wang, Xingyu; Cichocki, Andrzej

2012-04-01

This study introduces a novel brain-computer interface (BCI) based on an oddball paradigm using stimuli of facial images with loss of configural face information (e.g., inversion of face). To the best of our knowledge, till now the configural processing of human faces has not been applied to BCI but widely studied in cognitive neuroscience research. Our experiments confirm that the face-sensitive event-related potential (ERP) components N170 and vertex positive potential (VPP) have reflected early structural encoding of faces and can be modulated by the configural processing of faces. With the proposed novel paradigm, we investigate the effects of ERP components N170, VPP and P300 on target detection for BCI. An eight-class BCI platform is developed to analyze ERPs and evaluate the target detection performance using linear discriminant analysis without complicated feature extraction processing. The online classification accuracy of 88.7% and information transfer rate of 38.7 bits min-1 using stimuli of inverted faces with only single trial suggest that the proposed paradigm based on the configural processing of faces is very promising for visual stimuli-driven BCI applications.

A novel BCI based on ERP components sensitive to configural processing of human faces.

PubMed

Zhang, Yu; Zhao, Qibin; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2012-04-01

This study introduces a novel brain-computer interface (BCI) based on an oddball paradigm using stimuli of facial images with loss of configural face information (e.g., inversion of face). To the best of our knowledge, till now the configural processing of human faces has not been applied to BCI but widely studied in cognitive neuroscience research. Our experiments confirm that the face-sensitive event-related potential (ERP) components N170 and vertex positive potential (VPP) have reflected early structural encoding of faces and can be modulated by the configural processing of faces. With the proposed novel paradigm, we investigate the effects of ERP components N170, VPP and P300 on target detection for BCI. An eight-class BCI platform is developed to analyze ERPs and evaluate the target detection performance using linear discriminant analysis without complicated feature extraction processing. The online classification accuracy of 88.7% and information transfer rate of 38.7 bits min(-1) using stimuli of inverted faces with only single trial suggest that the proposed paradigm based on the configural processing of faces is very promising for visual stimuli-driven BCI applications.

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

Should the parameters of a BCI translation algorithm be continually adapted?

PubMed

McFarland, Dennis J; Sarnacki, William A; Wolpaw, Jonathan R

2011-07-15

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures. Copyright © 2011 Elsevier B.V. All rights reserved.

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

PubMed Central

2016-01-01

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.

PubMed

Li, Guangye; Zhang, Dingguo

2016-01-01

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.

Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.

PubMed

Zhang, Tao; Liu, Tiejun; Li, Fali; Li, Mengchen; Liu, Dongbo; Zhang, Rui; He, Hui; Li, Peiyang; Gong, Jinnan; Luo, Cheng; Yao, Dezhong; Xu, Peng

2016-07-01

Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance. Copyright © 2016 Elsevier Inc. All rights reserved.

How stimulation speed affects Event-Related Potentials and BCI performance.

PubMed

Höhne, Johannes; Tangermann, Michael

2012-01-01

In most paradigms for Brain-Computer Interfaces (BCIs) that are based on Event-Related Potentials (ERPs), stimuli are presented with a pre-defined and constant speed. In order to boost BCI performance by optimizing the parameters of stimulation, this offline study investigates the impact of the stimulus onset asynchrony (SOA) on ERPs and the resulting classification accuracy. The SOA is defined as the time between the onsets of two consecutive stimuli, which represents a measure for stimulation speed. A simple auditory oddball paradigm was tested in 14 SOA conditions with a SOA between 50 ms and 1000 ms. Based on an offline ERP analysis, the BCI performance (quantified by the Information Transfer Rate, ITR in bits/min) was simulated. A great variability in the simulated BCI performance was observed within subjects (N=11). This indicates a potential increase in BCI performance (≥ 1.6 bits/min) for ERP-based paradigms, if the stimulation speed is specified for each user individually.

Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2015-12-01

Objective. The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach. Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results. VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance. These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.

An online semi-supervised brain-computer interface.

PubMed

Gu, Zhenghui; Yu, Zhuliang; Shen, Zhifang; Li, Yuanqing

2013-09-01

Practical brain-computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85 % or above within an average online semi-supervised learning time of around 3 min.

A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

PubMed Central

Geng, Tao; Gan, John Q.; Dyson, Matthew; Tsui, Chun SL; Sepulveda, Francisco

2008-01-01

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms. PMID:18584040

Current trends in hardware and software for brain-computer interfaces (BCIs)

NASA Astrophysics Data System (ADS)

Brunner, P.; Bianchi, L.; Guger, C.; Cincotti, F.; Schalk, G.

2011-04-01

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation.

PubMed

Carelli, Laura; Solca, Federica; Faini, Andrea; Meriggi, Paolo; Sangalli, Davide; Cipresso, Pietro; Riva, Giuseppe; Ticozzi, Nicola; Ciammola, Andrea; Silani, Vincenzo; Poletti, Barbara

2017-01-01

Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects. We discuss some advantages of BCI for both assessment and training purposes, the former concerning the possibility of longitudinally and reliably evaluating cognitive functions in patients with severe motor disabilities, the latter regarding the possibility of enhancing patients' motivation and engagement for improving neural plasticity. Finally, we discuss some present and future challenges in the BCI use for the described purposes.

The advantages of the surface Laplacian in brain-computer interface research.

PubMed

McFarland, Dennis J

2015-09-01

Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user's intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control. Copyright © 2014 Elsevier B.V. All rights reserved.

Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

PubMed

Marathe, Amar R; Lawhern, Vernon J; Wu, Dongrui; Slayback, David; Lance, Brent J

2016-03-01

The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibrated to account for changes in the neural signals due to a variety of factors including changes in human state, the surrounding environment, and task conditions. Novel approaches to reduce calibration time or effort will dramatically improve the usability of BCI systems. Active Learning (AL) is an iterative semi-supervised learning technique for learning in situations in which data may be abundant, but labels for the data are difficult or expensive to obtain. In this paper, we apply AL to a simulated BCI system for target identification using data from a rapid serial visual presentation (RSVP) paradigm to minimize the amount of training samples needed to initially calibrate a neural classifier. Our results show AL can produce similar overall classification accuracy with significantly less labeled data (in some cases less than 20%) when compared to alternative calibration approaches. In fact, AL classification performance matches performance of 10-fold cross-validation (CV) in over 70% of subjects when training with less than 50% of the data. To our knowledge, this is the first work to demonstrate the use of AL for offline electroencephalography (EEG) calibration in a simulated BCI paradigm. While AL itself is not often amenable for use in real-time systems, this work opens the door to alternative AL-like systems that are more amenable for BCI applications and thus enables future efforts for developing highly adaptive BCI systems.

Continuous EEG signal analysis for asynchronous BCI application.

PubMed

Hsu, Wei-Yen

2011-08-01

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

Leveraging anatomical information to improve transfer learning in brain-computer interfaces

NASA Astrophysics Data System (ADS)

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian K. C.

2015-08-01

Objective. Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main results. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates

PubMed Central

2017-01-01

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users. PMID:28665058

Leveraging anatomical information to improve transfer learning in brain-computer interfaces.

PubMed

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian K C

2015-08-01

Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.

Leveraging anatomical information to improve transfer learning in brain-computer interfaces

PubMed Central

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian KC

2015-01-01

Objective Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main Results Based on simulated and measured EEG activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time. PMID:26169961

Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human.

PubMed

Friedenberg, David A; Schwemmer, Michael A; Landgraf, Andrew J; Annetta, Nicholas V; Bockbrader, Marcia A; Bouton, Chad E; Zhang, Mingming; Rezai, Ali R; Mysiw, W Jerry; Bresler, Herbert S; Sharma, Gaurav

2017-08-21

Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.

The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.

PubMed

Brumberg, Jonathan S; Lorenz, Sean D; Galbraith, Byron V; Guenther, Frank H

2012-01-01

In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software "app" development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50-60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

PubMed Central

Yuan, Han; He, Bin

2014-01-01

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e. the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g. electroencephalography (EEG), and have demonstrated the capability of multi-dimensional prosthesis control. This article reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications are reviewed. Lastly, limitations of SMR-BCIs and future outlooks are also discussed. PMID:24759276

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

The effect of multimodal and enriched feedback on SMR-BCI performance.

PubMed

Sollfrank, T; Ramsay, A; Perdikis, S; Williamson, J; Murray-Smith, R; Leeb, R; Millán, J D R; Kübler, A

2016-01-01

This study investigated the effect of multimodal (visual and auditory) continuous feedback with information about the uncertainty of the input signal on motor imagery based BCI performance. A liquid floating through a visualization of a funnel (funnel feedback) provided enriched visual or enriched multimodal feedback. In a between subject design 30 healthy SMR-BCI naive participants were provided with either conventional bar feedback (CB), or visual funnel feedback (UF), or multimodal (visual and auditory) funnel feedback (MF). Subjects were required to imagine left and right hand movement and were trained to control the SMR based BCI for five sessions on separate days. Feedback accuracy varied largely between participants. The MF feedback lead to a significantly better performance in session 1 as compared to the CB feedback and could significantly enhance motivation and minimize frustration in BCI use across the five training sessions. The present study demonstrates that the BCI funnel feedback allows participants to modulate sensorimotor EEG rhythms. Participants were able to control the BCI with the funnel feedback with better performance during the initial session and less frustration compared to the CB feedback. The multimodal funnel feedback provides an alternative to the conventional cursorbar feedback for training subjects to modulate their sensorimotor rhythms. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Effects of user mental state on EEG-BCI performance.

PubMed

Myrden, Andrew; Chau, Tom

2015-01-01

Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states-fatigue, frustration, and attention-on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 7% higher than average when self-reported frustration was moderate. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.

Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface

PubMed Central

Metzen, Jan H.

2013-01-01

A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays. PMID:23844021

A P300-based Brain-Computer Interface with Stimuli on Moving Objects: Four-Session Single-Trial and Triple-Trial Tests with a Game-Like Task Design

PubMed Central

Ganin, Ilya P.; Shishkin, Sergei L.; Kaplan, Alexander Y.

2013-01-01

Brain-computer interfaces (BCIs) are tools for controlling computers and other devices without using muscular activity, employing user-controlled variations in signals recorded from the user’s brain. One of the most efficient noninvasive BCIs is based on the P300 wave of the brain’s response to stimuli and is therefore referred to as the P300 BCI. Many modifications of this BCI have been proposed to further improve the BCI’s characteristics or to better adapt the BCI to various applications. However, in the original P300 BCI and in all of its modifications, the spatial positions of stimuli were fixed relative to each other, which can impose constraints on designing applications controlled by this BCI. We designed and tested a P300 BCI with stimuli presented on objects that were freely moving on a screen at a speed of 5.4°/s. Healthy participants practiced a game-like task with this BCI in either single-trial or triple-trial mode within four sessions. At each step, the participants were required to select one of nine moving objects. The mean online accuracy of BCI-based selection was 81% in the triple-trial mode and 65% in the single-trial mode. A relatively high P300 amplitude was observed in response to targets in most participants. Self-rated interest in the task was high and stable over the four sessions (the medians in the 1st/4th sessions were 79/84% and 76/71% in the groups practicing in the single-trial and triple-trial modes, respectively). We conclude that the movement of stimulus positions relative to each other may not prevent the efficient use of the P300 BCI by people controlling their gaze, e.g., in robotic devices and in video games. PMID:24302977

Simple communication using a SSVEP-based BCI

NASA Astrophysics Data System (ADS)

Sanchez, Guillermo; Diez, Pablo F.; Avila, Enrique; Laciar Leber, Eric

2011-12-01

Majority of Brain-Computer Interface (BCI) for communication purposes are speller, i.e., the user has to select letter by letter. In this work, is proposed a different approach where the user can select words from a word set designed in order to answer a wide range of questions. The word selection process is commanded by a Steady-state visual evoked potential (SSVEP) based-BCI that allows selecting a word in an average time of 26 s with accuracies of 92% on average. This BCI is focus in the first stages on rehabilitation or even in first moments of some diseases (such as stroke), when the person is eager to communicate with family and doctors.

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.

PubMed

Perdikis, Serafeim; Tonin, Luca; Saeedi, Sareh; Schneider, Christoph; Millán, José Del R

2018-05-01

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI-user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface-application, BCI output, and electroencephalography (EEG) neuroimaging-with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions.

Control of an electrical prosthesis with an SSVEP-based BCI.

PubMed

Müller-Putz, Gernot R; Pfurtscheller, Gert

2008-01-01

Brain-computer interfaces (BCIs) are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. They are used in a broad field of applications nowadays. One important issue is the control of neuroprosthetic devices for the restoration of the grasp function in spinal-cord-injured people. In this communication, an asynchronous (self-paced) four-class BCI based on steady-state visual evoked potentials (SSVEPs) was used to control a two-axes electrical hand prosthesis. During training, four healthy participants reached an online classification accuracy between 44% and 88%. Controlling the prosthetic hand asynchronously, the participants reached a performance of 75.5 to 217.5 s to copy a series of movements, whereas the fastest possible duration determined by the setup was 64 s. The number of false negative (FN) decisions varied from 0 to 10 (the maximal possible decisions were 34). It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.

A P300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people.

PubMed

Corralejo, Rebeca; Nicolás-Alonso, Luis F; Alvarez, Daniel; Hornero, Roberto

2014-10-01

The present study aims at developing and assessing an assistive tool for operating electronic devices at home by means of a P300-based brain-computer interface (BCI). Fifteen severely impaired subjects participated in the study. The developed tool allows users to interact with their usual environment fulfilling their main needs. It allows for navigation through ten menus and to manage up to 113 control commands from eight electronic devices. Ten out of the fifteen subjects were able to operate the proposed tool with accuracy above 77 %. Eight out of them reached accuracies higher than 95 %. Moreover, bitrates up to 20.1 bit/min were achieved. The novelty of this study lies in the use of an environment control application in a real scenario: real devices managed by potential BCI end-users. Although impaired users might not be able to set up this system without aid of others, this study takes a significant step to evaluate the degree to which such populations could eventually operate a stand-alone system. Our results suggest that neither the type nor the degree of disability is a relevant issue to suitably operate a P300-based BCI. Hence, it could be useful to assist disabled people at home improving their personal autonomy.

Independence of amplitude-frequency and phase calibrations in an SSVEP-based BCI using stepping delay flickering sequences.

PubMed

Chang, Hsiang-Chih; Lee, Po-Lei; Lo, Men-Tzung; Lee, I-Hui; Yeh, Ting-Kuang; Chang, Chun-Yen

2012-05-01

This study proposes a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) independent of amplitude-frequency and phase calibrations. Six stepping delay flickering sequences (SDFSs) at 32-Hz flickering frequency were used to implement a six-command BCI system. EEG signals recorded from Oz position were first filtered within 29-35 Hz, segmented based on trigger events of SDFSs to obtain SDFS epochs, and then stored separately in epoch registers. An epoch-average process suppressed the inter-SDFS interference. For each detection point, the latest six SDFS epochs in each epoch register were averaged and the normalized power of averaged responses was calculated. The visual target that induced the maximum normalized power was identified as the visual target. Eight subjects were recruited in this study. All subjects were requested to produce the "563241" command sequence four times. The averaged accuracy, command transfer interval, and information transfer rate (mean ± std.) values for all eight subjects were 97.38 ± 5.97%, 3.56 ± 0.68 s, and 42.46 ± 11.17 bits/min, respectively. The proposed system requires no calibration in either the amplitude-frequency characteristic or the reference phase of SSVEP which may provide an efficient and reliable channel for the neuromuscular disabled to communicate with external environments.

Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss.

PubMed

Nakanishi, Masaki; Wang, Yu-Te; Jung, Tzyy-Ping; Zao, John K; Chien, Yu-Yi; Diniz-Filho, Alberto; Daga, Fabio B; Lin, Yuan-Pin; Wang, Yijun; Medeiros, Felipe A

2017-06-01

The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters. To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss. This case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants. Glaucoma was diagnosed based on a masked grading of optic disc stereophotographs. All participants underwent testing with a BCI device and standard automated perimetry (SAP) within 3 months. The BCI device integrates wearable, wireless, dry electroencephalogram and electrooculogram systems and a cellphone-based head-mounted display to enable the detection of multifocal steady state visual-evoked potentials associated with visual field stimulation. The performances of global and sectoral multifocal steady state visual-evoked potentials metrics to discriminate glaucomatous from healthy eyes were compared with global and sectoral SAP parameters. The repeatability of the BCI device measurements was assessed by collecting results of repeated testing in 20 eyes of 10 participants with glaucoma for 3 sessions of measurements separated by weekly intervals. Receiver operating characteristic curves summarizing diagnostic accuracy. Intraclass correlation coefficients and coefficients of variation for assessing repeatability. Among the 33 participants with glaucoma, 19 (58%) were white, 12 (36%) were black, and 2 (6%) were Asian, while among the 17 participants with healthy eyes, 9 (53%) were white, 8 (47%) were black, and none were Asian. The receiver operating characteristic curve area for the global BCI multifocal steady state visual-evoked potentials parameter was 0.92 (95% CI, 0.86-0.96), which was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P = .03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P = .01). No statistically significant differences were seen for the sectoral measurements between the BCI and SAP. Intraclass coefficients for global and sectoral parameters ranged from 0.74 to 0.92, and mean coefficients of variation ranged from 3.03% to 7.45%. The BCI device may be useful for assessing the electrical brain responses associated with visual field stimulation. The device discriminated eyes with glaucomatous neuropathy from healthy eyes in a clinically based setting. Further studies should investigate the feasibility of the BCI device for home-based testing as well as for detecting visual function loss over time.

Robotic and Virtual Reality BCIs Using Spatial Tactile and Auditory Oddball Paradigms

PubMed Central

Rutkowski, Tomasz M.

2016-01-01

The paper reviews nine robotic and virtual reality (VR) brain–computer interface (BCI) projects developed by the author, in collaboration with his graduate students, within the BCI–lab research group during its association with University of Tsukuba, Japan. The nine novel approaches are discussed in applications to direct brain-robot and brain-virtual-reality-agent control interfaces using tactile and auditory BCI technologies. The BCI user intentions are decoded from the brainwaves in realtime using a non-invasive electroencephalography (EEG) and they are translated to a symbiotic robot or virtual reality agent thought-based only control. A communication protocol between the BCI output and the robot or the virtual environment is realized in a symbiotic communication scenario using an user datagram protocol (UDP), which constitutes an internet of things (IoT) control scenario. Results obtained from healthy users reproducing simple brain-robot and brain-virtual-agent control tasks in online experiments support the research goal of a possibility to interact with robotic devices and virtual reality agents using symbiotic thought-based BCI technologies. An offline BCI classification accuracy boosting method, using a previously proposed information geometry derived approach, is also discussed in order to further support the reviewed robotic and virtual reality thought-based control paradigms. PMID:27999538

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

PubMed

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

2017-01-01

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

Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

PubMed Central

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-01-01

Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

A Multi-purpose Brain-Computer Interface Output Device

PubMed Central

Thompson, David E; Huggins, Jane E

2012-01-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as standalone communication and control systems, rather than as interfaces to existing systems built for these purposes. While an individual communication and control system may be powerful or flexible, no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCIs could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e. without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems. PMID:22208120

A multi-purpose brain-computer interface output device.

PubMed

Thompson, David E; Huggins, Jane E

2011-10-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.

Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness

NASA Astrophysics Data System (ADS)

Pichiorri, F.; De Vico Fallani, F.; Cincotti, F.; Babiloni, F.; Molinari, M.; Kleih, S. C.; Neuper, C.; Kübler, A.; Mattia, D.

2011-04-01

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

True Zero-Training Brain-Computer Interfacing – An Online Study

PubMed Central

Kindermans, Pieter-Jan; Schreuder, Martijn; Schrauwen, Benjamin; Müller, Klaus-Robert; Tangermann, Michael

2014-01-01

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model. PMID:25068464

Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

PubMed

Oken, Barry S; Orhan, Umut; Roark, Brian; Erdogmus, Deniz; Fowler, Andrew; Mooney, Aimee; Peters, Betts; Miller, Meghan; Fried-Oken, Melanie B

2014-05-01

Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

Removal of proprioception by BCI raises a stronger body ownership illusion in control of a humanlike robot.

PubMed

Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

2016-09-22

Body ownership illusions provide evidence that our sense of self is not coherent and can be extended to non-body objects. Studying about these illusions gives us practical tools to understand the brain mechanisms that underlie body recognition and the experience of self. We previously introduced an illusion of body ownership transfer (BOT) for operators of a very humanlike robot. This sensation of owning the robot's body was confirmed when operators controlled the robot either by performing the desired motion with their body (motion-control) or by employing a brain-computer interface (BCI) that translated motor imagery commands to robot movement (BCI-control). The interesting observation during BCI-control was that the illusion could be induced even with a noticeable delay in the BCI system. Temporal discrepancy has always shown critical weakening effects on body ownership illusions. However the delay-robustness of BOT during BCI-control raised a question about the interaction between the proprioceptive inputs and delayed visual feedback in agency-driven illusions. In this work, we compared the intensity of BOT illusion for operators in two conditions; motion-control and BCI-control. Our results revealed a significantly stronger BOT illusion for the case of BCI-control. This finding highlights BCI's potential in inducing stronger agency-driven illusions by building a direct communication between the brain and controlled body, and therefore removing awareness from the subject's own body.

Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial.

PubMed

Chung, EunJung; Kim, Jung-Hee; Park, Dae-Sung; Lee, Byoung-Hee

2015-03-01

[Purpose] This study sought to determine the effects of brain-computer interface-based functional electrical stimulation (BCI-FES) on brain activation in patients with stroke. [Subjects] The subjects were randomized to in a BCI-FES group (n=5) and a functional electrical stimulation (FES) group (n=5). [Methods] Patients in the BCI-FES group received ankle dorsiflexion training with FES for 30 minutes per day, 5 times under the brain-computer interface-based program. The FES group received ankle dorsiflexion training with FES for the same amount of time. [Results] The BCI-FES group demonstrated significant differences in the frontopolar regions 1 and 2 attention indexes, and frontopolar 1 activation index. The FES group demonstrated no significant differences. There were significant differences in the frontopolar 1 region activation index between the two groups after the interventions. [Conclusion] The results of this study suggest that BCI-FES training may be more effective in stimulating brain activation than only FES training in patients recovering from stroke.

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

PubMed Central

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

2016-01-01

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

High Frequency SSVEP-BCI With Hardware Stimuli Control and Phase-Synchronized Comb Filter.

PubMed

Chabuda, Anna; Durka, Piotr; Zygierewicz, Jaroslaw

2018-02-01

We present an efficient implementation of brain-computer interface (BCI) based on high-frequency steady state visually evoked potentials (SSVEP). Individual shape of the SSVEP response is extracted by means of a feedforward comb filter, which adds delayed versions of the signal to itself. Rendering of the stimuli is controlled by specialized hardware (BCI Appliance). Out of 15 participants of the study, nine were able to produce stable response in at least eight out of ten frequencies from the 30-39 Hz range. They achieved on average 96±4% accuracy and 47±5 bit/min information transfer rate (ITR) for an optimized simple seven-letter speller, while generic full-alphabet speller allowed in this group for 89±9% accuracy and 36±9 bit/min ITR. These values exceed the performances of high-frequency SSVEP-BCI systems reported to date. Classical approach to SSVEP parameterization by relative spectral power in the frequencies of stimulation, implemented on the same data, resulted in significantly lower performance. This suggests that specific shape of the response is an important feature in classification. Finally, we discuss the differences in SSVEP responses of the participants who were able or unable to use the interface, as well as the statistically significant influence of the layout of the speller on the speed of BCI operation.

Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

PubMed

Edelman, Bradley J; Meng, Jianjun; Gulachek, Nicholas; Cline, Christopher C; He, Bin

2018-05-01

EEG-based brain-computer interface (BCI) technology creates non-biological pathways for conveying a user's mental intent solely through noninvasively measured neural signals. While optimizing the performance of a single task has long been the focus of BCI research, in order to translate this technology into everyday life, realistic situations, in which multiple tasks are performed simultaneously, must be investigated. In this paper, we explore the concept of cognitive flexibility, or multitasking, within the BCI framework by utilizing a 2-D cursor control task, using sensorimotor rhythms (SMRs), and a four-target visual attention task, using steady-state visual evoked potentials (SSVEPs), both individually and simultaneously. We found no significant difference between the accuracy of the tasks when executing them alone (SMR-57.9% ± 15.4% and SSVEP-59.0% ± 14.2%) and simultaneously (SMR-54.9% ± 17.2% and SSVEP-57.5% ± 15.4%). These modest decreases in performance were supported by similar, non-significant changes in the electrophysiology of the SSVEP and SMR signals. In this sense, we report that multiple BCI tasks can be performed simultaneously without a significant deterioration in performance; this finding will help drive these systems toward realistic daily use in which a user's cognition will need to be involved in multiple tasks at once.

Live animal measurements, carcass composition and plasma hormone and metabolite concentrations in male progeny of sires differing in genetic merit for beef production.

PubMed

Clarke, A M; Drennan, M J; McGee, M; Kenny, D A; Evans, R D; Berry, D P

2009-07-01

In genetic improvement programmes for beef cattle, the effect of selecting for a given trait or index on other economically important traits, or their predictors, must be quantified to ensure no deleterious consequential effects go unnoticed. The objective was to compare live animal measurements, carcass composition and plasma hormone and metabolite concentrations of male progeny of sires selected on an economic index in Ireland. This beef carcass index (BCI) is expressed in euros and based on weaning weight, feed intake, carcass weight and carcass conformation and fat scores. The index is used to aid in the genetic comparison of animals for the expected profitability of their progeny at slaughter. A total of 107 progeny from beef sires of high (n = 11) or low (n = 11) genetic merit for the BCI were compared in either a bull (slaughtered at 16 months of age) or steer (slaughtered at 24 months of age) production system, following purchase after weaning (8 months of age) from commercial beef herds. Data were analysed as a 2 × 2 factorial design (two levels of genetic merit by two production systems). Progeny of high BCI sires had heavier carcasses, greater (P < 0.01) muscularity scores after weaning, greater (P < 0.05) skeletal scores and scanned muscle depth pre-slaughter, higher (P < 0.05) plasma insulin concentrations and greater (P < 0.01) animal value (obtained by multiplying carcass weight by carcass value, which was based on the weight of meat in each cut by its commercial value) than progeny of low BCI sires. Regression of progeny performance on sire genetic merit was also undertaken across the entire data set. In steers, the effect of BCI on carcass meat proportion, calculated carcass value (c/kg) and animal value was positive (P < 0.01), while a negative association was observed for scanned fat depth pre-slaughter and carcass fat proportion (P < 0.01), but there was no effect in bulls. The effect of sire expected progeny difference (EPD) for carcass weight followed the same trends as BCI. Muscularity scores, carcass meat proportion and calculated carcass value increased, whereas scanned fat depth, carcass fat and bone proportions decreased with increasing sire EPD for conformation score. The opposite association was observed for sire EPD for fat score. Results from this study show that selection using the BCI had positive effects on live animal muscularity, carcass meat proportion, proportions of high-value cuts and carcass value in steer progeny, which are desirable traits in beef production.

Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

PubMed Central

Lotte, Fabien; Larrue, Florian; Mühl, Christian

2013-01-01

While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently. PMID:24062669

Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface.

PubMed

Xu, Ren; Jiang, Ning; Dosen, Strahinja; Lin, Chuang; Mrachacz-Kersting, Natalie; Dremstrup, Kim; Farina, Dario

2016-08-01

In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of  ∼ 80% and  ∼ 70%, and an information transfer rate of  ∼ 7 bits/min and  ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.

Towards psychologically adaptive brain-computer interfaces

NASA Astrophysics Data System (ADS)

Myrden, A.; Chau, T.

2016-12-01

Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

Clinical usefulness of brain-computer interface-controlled functional electrical stimulation for improving brain activity in children with spastic cerebral palsy: a pilot randomized controlled trial.

PubMed

Kim, Tae-Woo; Lee, Byoung-Hee

2016-09-01

[Purpose] Evaluating the effect of brain-computer interface (BCI)-based functional electrical stimulation (FES) training on brain activity in children with spastic cerebral palsy (CP) was the aim of this study. [Subjects and Methods] Subjects were randomized into a BCI-FES group (n=9) and a functional electrical stimulation (FES) control group (n=9). Subjects in the BCI-FES group received wrist and hand extension training with FES for 30 minutes per day, 5 times per week for 6 weeks under the BCI-based program. The FES group received wrist and hand extension training with FES for the same amount of time. Sensorimotor rhythms (SMR) and middle beta waves (M-beta) were measured in frontopolar regions 1 and 2 (Fp1, Fp2) to determine the effects of BCI-FES training. [Results] Significant improvements in the SMR and M-beta of Fp1 and Fp2 were seen in the BCI-FES group. In contrast, significant improvement was only seen in the SMR and M-beta of Fp2 in the control group. [Conclusion] The results of the present study suggest that BCI-controlled FES training may be helpful in improving brain activity in patients with cerebral palsy and may be applied as effectively as traditional FES training.

EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis

NASA Astrophysics Data System (ADS)

Mak, Joseph N.; McFarland, Dennis J.; Vaughan, Theresa M.; McCane, Lynn M.; Tsui, Phillippa Z.; Zeitlin, Debra J.; Sellers, Eric W.; Wolpaw, Jonathan R.

2012-04-01

The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.

Non-invasive brain-computer interface system: towards its application as assistive technology.

PubMed

Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

2008-04-15

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

Non invasive Brain-Computer Interface system: towards its application as assistive technology

PubMed Central

Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

2010-01-01

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain Computer Interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI. PMID:18394526

A novel channel selection method for optimal classification in different motor imagery BCI paradigms.

PubMed

Shan, Haijun; Xu, Haojie; Zhu, Shanan; He, Bin

2015-10-21

For sensorimotor rhythms based brain-computer interface (BCI) systems, classification of different motor imageries (MIs) remains a crucial problem. An important aspect is how many scalp electrodes (channels) should be used in order to reach optimal performance classifying motor imaginations. While the previous researches on channel selection mainly focus on MI tasks paradigms without feedback, the present work aims to investigate the optimal channel selection in MI tasks paradigms with real-time feedback (two-class control and four-class control paradigms). In the present study, three datasets respectively recorded from MI tasks experiment, two-class control and four-class control experiments were analyzed offline. Multiple frequency-spatial synthesized features were comprehensively extracted from every channel, and a new enhanced method IterRelCen was proposed to perform channel selection. IterRelCen was constructed based on Relief algorithm, but was enhanced from two aspects: change of target sample selection strategy and adoption of the idea of iterative computation, and thus performed more robust in feature selection. Finally, a multiclass support vector machine was applied as the classifier. The least number of channels that yield the best classification accuracy were considered as the optimal channels. One-way ANOVA was employed to test the significance of performance improvement among using optimal channels, all the channels and three typical MI channels (C3, C4, Cz). The results show that the proposed method outperformed other channel selection methods by achieving average classification accuracies of 85.2, 94.1, and 83.2 % for the three datasets, respectively. Moreover, the channel selection results reveal that the average numbers of optimal channels were significantly different among the three MI paradigms. It is demonstrated that IterRelCen has a strong ability for feature selection. In addition, the results have shown that the numbers of optimal channels in the three different motor imagery BCI paradigms are distinct. From a MI task paradigm, to a two-class control paradigm, and to a four-class control paradigm, the number of required channels for optimizing the classification accuracy increased. These findings may provide useful information to optimize EEG based BCI systems, and further improve the performance of noninvasive BCI.

Differentiating closed-loop cortical intention from rest: building an asynchronous electrocorticographic BCI.

PubMed

Williams, Jordan J; Rouse, Adam G; Thongpang, Sanitta; Williams, Justin C; Moran, Daniel W

2013-08-01

Recent experiments have shown that electrocorticography (ECoG) can provide robust control signals for a brain-computer interface (BCI). Strategies that attempt to adapt a BCI control algorithm by learning from past trials often assume that the subject is attending to each training trial. Likewise, automatic disabling of movement control would be desirable during resting periods when random brain fluctuations might cause unintended movements of a device. To this end, our goal was to identify ECoG differences that arise between periods of active BCI use and rest. We examined spectral differences in multi-channel, epidural micro-ECoG signals recorded from non-human primates when rest periods were interleaved between blocks of an active BCI control task. Post-hoc analyses demonstrated that these states can be decoded accurately on both a trial-by-trial and real-time basis, and this discriminability remains robust over a period of weeks. In addition, high gamma frequencies showed greater modulation with desired movement direction, while lower frequency components demonstrated greater amplitude differences between task and rest periods, suggesting possible specialized BCI roles for these frequencies. The results presented here provide valuable insight into the neurophysiology of BCI control as well as important considerations toward the design of an asynchronous BCI system.

Differentiating closed-loop cortical intention from rest: building an asynchronous electrocorticographic BCI

NASA Astrophysics Data System (ADS)

Williams, Jordan J.; Rouse, Adam G.; Thongpang, Sanitta; Williams, Justin C.; Moran, Daniel W.

2013-08-01

Objective. Recent experiments have shown that electrocorticography (ECoG) can provide robust control signals for a brain-computer interface (BCI). Strategies that attempt to adapt a BCI control algorithm by learning from past trials often assume that the subject is attending to each training trial. Likewise, automatic disabling of movement control would be desirable during resting periods when random brain fluctuations might cause unintended movements of a device. To this end, our goal was to identify ECoG differences that arise between periods of active BCI use and rest. Approach. We examined spectral differences in multi-channel, epidural micro-ECoG signals recorded from non-human primates when rest periods were interleaved between blocks of an active BCI control task. Main Results. Post-hoc analyses demonstrated that these states can be decoded accurately on both a trial-by-trial and real-time basis, and this discriminability remains robust over a period of weeks. In addition, high gamma frequencies showed greater modulation with desired movement direction, while lower frequency components demonstrated greater amplitude differences between task and rest periods, suggesting possible specialized BCI roles for these frequencies. Significance. The results presented here provide valuable insight into the neurophysiology of BCI control as well as important considerations toward the design of an asynchronous BCI system.

Assessment of the prognostic and predictive utility of the Breast Cancer Index (BCI): an NCIC CTG MA.14 study.

PubMed

Sgroi, Dennis C; Chapman, Judy-Anne W; Badovinac-Crnjevic, T; Zarella, Elizabeth; Binns, Shemeica; Zhang, Yi; Schnabel, Catherine A; Erlander, Mark G; Pritchard, Kathleen I; Han, Lei; Shepherd, Lois E; Goss, Paul E; Pollak, Michael

2016-01-04

Biomarkers that can be used to accurately assess the residual risk of disease recurrence in women with hormone receptor-positive breast cancer are clinically valuable. We evaluated the prognostic value of the Breast Cancer Index (BCI), a continuous risk index based on a combination of HOXB13:IL17BR and molecular grade index, in women with early breast cancer treated with either tamoxifen alone or tamoxifen plus octreotide in the NCIC MA.14 phase III clinical trial (ClinicalTrials.gov Identifier NCT00002864; registered 1 November 1999). Gene expression analysis of BCI by real-time polymerase chain reaction was performed blinded to outcome on RNA extracted from archived formalin-fixed, paraffin-embedded tumor samples of 299 patients with both lymph node-negative (LN-) and lymph node-positive (LN+) disease enrolled in the MA.14 trial. Our primary objective was to determine the prognostic performance of BCI based on relapse-free survival (RFS). MA.14 patients experienced similar RFS on both treatment arms. Association of gene expression data with RFS was evaluated in univariate analysis with a stratified log-rank test statistic, depicted with a Kaplan-Meier plot and an adjusted Cox survivor plot. In the multivariate assessment, we used stratified Cox regression. The prognostic performance of an emerging, optimized linear BCI model was also assessed in a post hoc analysis. Of 299 samples, 292 were assessed successfully for BCI for 146 patients accrued in each MA.14 treatment arm. BCI risk groups had a significant univariate association with RFS (stratified log-rank p = 0.005, unstratified log-rank p = 0.007). Adjusted 10-year RFS in BCI low-, intermediate-, and high-risk groups was 87.5 %, 83.9 %, and 74.7 %, respectively. BCI had a significant prognostic effect [hazard ratio (HR) 2.34, 95 % confidence interval (CI) 1.33-4.11; p = 0.004], although not a predictive effect, on RFS in stratified multivariate analysis, adjusted for pathological tumor stage (HR 2.22, 95 % CI 1.22-4.07; p = 0.01). In the post hoc multivariate analysis, higher linear BCI was associated with shorter RFS (p = 0.002). BCI had a strong prognostic effect on RFS in patients with early-stage breast cancer treated with tamoxifen alone or with tamoxifen and octreotide. BCI was prognostic in both LN- and LN+ patients. This retrospective study is an independent validation of the prognostic performance of BCI in a prospective trial.

Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.

PubMed

McCane, Lynn M; Sellers, Eric W; McFarland, Dennis J; Mak, Joseph N; Carmack, C Steve; Zeitlin, Debra; Wolpaw, Jonathan R; Vaughan, Theresa M

2014-06-01

Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.

[Analysis and research of brain-computer interface experiments for imaging left-right hands movement].

PubMed

Wu, Yazhou; He, Qinghua; Huang, Hua; Zhang, Ling; Zhuo, Yu; Xie, Qi; Wu, Baoming

2008-10-01

This is a research carried out to explore a pragmatic way of BCI based imaging movement, i. e. to extract the feature of EEG for reflecting different thinking by searching suitable methods of signal extraction and recognition algorithm processing, to boost the recognition rate of communication for BCI system, and finally to establish a substantial theory and experimental support for BCI application. In this paper, different mental tasks for imaging left-right hands movement from 6 subjects were studied in three different time sections (hint keying at 2s, 1s and 0s after appearance of arrow). Then we used wavelet analysis and Feed-forward Back-propagation Neural Network (BP-NN) method for processing and analyzing the experimental data of off-line. Delay time delta t2, delta t1 and delta t0 for all subjects in the three different time sections were analyzed. There was significant difference between delta to and delta t2 or delta t1 (P<0.05), but no significant difference was noted between delta t2 and delta t1 (P>0.05). The average results of recognition rate were 65%, 86.67% and 72%, respectively. There were obviously different features for imaging left-right hands movement about 0.5-1s before actual movement; these features displayed significant difference. We got higher recognition rate of communication under the hint keying at about 1s after the appearance of arrow. These showed the feasibility of using the feature signals extracted from the project as the external control signals for BCI system, and demon strated that the project provided new ideas and methods for feature extraction and classification of mental tasks for BCI.

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.

PubMed

Lin, Chin-Teng; Chen, Yu-Chieh; Huang, Teng-Yi; Chiu, Tien-Ting; Ko, Li-Wei; Liang, Sheng-Fu; Hsieh, Hung-Yi; Hsu, Shang-Hwa; Duann, Jeng-Ren

2008-05-01

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

[A wireless smart home system based on brain-computer interface of steady state visual evoked potential].

PubMed

Zhao, Li; Xing, Xiao; Guo, Xuhong; Liu, Zehua; He, Yang

2014-10-01

Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.

Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study.

PubMed

Holz, Elisa Mira; Botrel, Loic; Kaufmann, Tobias; Kübler, Andrea

2015-03-01

Despite intense brain-computer interface (BCI) research for >2 decades, BCIs have hardly been established at patients' homes. The current study aimed at demonstrating expert independent BCI home use by a patient in the locked-in state and the effect it has on quality of life. In this case study, the P300 BCI-controlled application Brain Painting was facilitated and installed at the patient's home. Family and caregivers were trained in setting up the BCI system. After every BCI session, the end user indicated subjective level of control, loss of control, level of exhaustion, satisfaction, frustration, and enjoyment. To monitor BCI home use, evaluation data of every session were automatically sent and stored on a remote server. Satisfaction with the BCI as an assistive device and subjective workload was indicated by the patient. In accordance with the user-centered design, usability of the BCI was evaluated in terms of its effectiveness, efficiency, and satisfaction. The influence of the BCI on quality of life of the end user was assessed. At the patient's home. A 73-year-old patient with amyotrophic lateral sclerosis in the locked-in state. Not applicable. The BCI has been used by the patient independent of experts for >14 months. The patient painted in about 200 BCI sessions (1-3 times per week) with a mean painting duration of 81.86 minutes (SD=52.15, maximum: 230.41). BCI improved quality of life of the patient. In most of the BCI sessions the end user's satisfaction was high (mean=7.4, SD=3.24; range, 0-10). Dissatisfaction occurred mostly because of technical problems at the beginning of the study or varying BCI control. The subjective workload was moderate (mean=40.61; range, 0-100). The end user was highy satisfied with all components of the BCI (mean 4.42-5.0; range, 1-5). A perfect match between the user and the BCI technology was achieved (mean: 4.8; range, 1-5). Brain Painting had a positive impact on the patient's life on all three dimensions: competence (1.5), adaptability (2.17) and self-esteem (1.5); (range: -3 = maximum negative impact; 3 maximum positive impact). The patient had her first public art exhibition in July 2013; future exhibitions are in preparation. Independent BCI home use is possible with high satisfaction for the end user. The BCI indeed positively influenced quality of life of the patient and supports social inclusion. Results demonstrate that visual P300 BCIs can be valuable for patients in the locked-in state even if other means of communication are still available (eye tracker). Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

EEG-based classification of imaginary left and right foot movements using beta rebound.

PubMed

Hashimoto, Yasunari; Ushiba, Junichi

2013-11-01

The purpose of this study was to investigate cortical lateralization of event-related (de)synchronization during left and right foot motor imagery tasks and to determine classification accuracy of the two imaginary movements in a brain-computer interface (BCI) paradigm. We recorded 31-channel scalp electroencephalograms (EEGs) from nine healthy subjects during brisk imagery tasks of left and right foot movements. EEG was analyzed with time-frequency maps and topographies, and the accuracy rate of classification between left and right foot movements was calculated. Beta rebound at the end of imagination (increase of EEG beta rhythm amplitude) was identified from the two EEGs derived from the right-shift and left-shift bipolar pairs at the vertex. This process enabled discrimination between right or left foot imagery at a high accuracy rate (maximum 81.6% in single trial analysis). These data suggest that foot motor imagery has potential to elicit left-right differences in EEG, while BCI using the unilateral foot imagery can achieve high classification accuracy, similar to ordinary BCI, based on hand motor imagery. By combining conventional discrimination techniques, the left-right discrimination of unilateral foot motor imagery provides a novel BCI system that could control a foot neuroprosthesis or a robotic foot. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates.

PubMed

Lee, Jeyeon; Choi, Hoseok; Lee, Seho; Cho, Baek Hwan; Ahn, Kyoung Ha; Kim, In Young; Lee, Kyoung Min; Jang, Dong Pyo

2017-08-01

A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users. © 2017 The Korean Academy of Medical Sciences.

Brain-controlled applications using dynamic P300 speller matrices.

PubMed

Halder, Sebastian; Pinegger, Andreas; Käthner, Ivo; Wriessnegger, Selina C; Faller, Josef; Pires Antunes, João B; Müller-Putz, Gernot R; Kübler, Andrea

2015-01-01

Access to the world wide web and multimedia content is an important aspect of life. We present a web browser and a multimedia user interface adapted for control with a brain-computer interface (BCI) which can be used by severely motor impaired persons. The web browser dynamically determines the most efficient P300 BCI matrix size to select the links on the current website. This enables control of the web browser with fewer commands and smaller matrices. The multimedia player was based on an existing software. Both applications were evaluated with a sample of ten healthy participants and three end-users. All participants used a visual P300 BCI with face-stimuli for control. The healthy participants completed the multimedia player task with 90% accuracy and the web browsing task with 85% accuracy. The end-users completed the tasks with 62% and 58% accuracy. All healthy participants and two out of three end-users reported that they felt to be in control of the system. In this study we presented a multimedia application and an efficient web browser implemented for control with a BCI. Both applications provide access to important areas of modern information retrieval and entertainment. Copyright © 2014 Elsevier B.V. All rights reserved.

Effects of Action Observational Training Plus Brain-Computer Interface-Based Functional Electrical Stimulation on Paretic Arm Motor Recovery in Patient with Stroke: A Randomized Controlled Trial.

PubMed

Kim, TaeHoon; Kim, SeongSik; Lee, ByoungHee

2016-03-01

The purpose of this study was to investigate whether action observational training (AOT) plus brain-computer interface-based functional electrical stimulation (BCI-FES) has a positive influence on motor recovery of paretic upper extremity in patients with stroke. This was a hospital-based, randomized controlled trial with a blinded assessor. Thirty patients with a first-time stroke were randomly allocated to one of two groups: the BCI-FES group (n = 15) and the control group (n = 15). The BCI-FES group administered to AOT plus BCI-FES on the paretic upper extremity five times per week during 4 weeks while both groups received conventional therapy. The primary outcomes were the Fugl-Meyer Assessment of the Upper Extremity, Motor Activity Log (MAL), Modified Barthel Index and range of motion of paretic arm. A blinded assessor evaluated the outcomes at baseline and 4 weeks. All baseline outcomes did not differ significantly between the two groups. After 4 weeks, the Fugl-Meyer Assessment of the Upper Extremity sub-items (total, shoulder and wrist), MAL (MAL-Activity of Use and Quality of Movement), Modified Barthel Index and wrist flexion range of motion were significantly higher in the BCI-FES group (p < 0.05). AOT plus BCI-based FES is effective in paretic arm rehabilitation by improving the upper extremity performance. The motor improvements suggest that AOT plus BCI-based FES can be used as a therapeutic tool for stroke rehabilitation. The limitations of the study are that subjects had a certain limited level of upper arm function, and the sample size was comparatively small; hence, it is recommended that future large-scale trials should consider stratified and lager populations according to upper arm function. Copyright © 2015 John Wiley & Sons, Ltd.

[Neural engineering and neural prostheses].

PubMed

Gao, Shang-Kai; Zhang, Zhi-Guang; Gao, Xiao-Rong; Hong, Bo; Yang, Fu-Sheng

2006-03-01

The motivation of the brain-computer interface (BCI) research and its potential applications are introduced in this paper. Some of the problems in BCI-based medical device developments are also discussed.

Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface.

PubMed

Valeriani, Davide; Cinel, Caterina; Poli, Riccardo

2017-08-10

Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions. We compare the performance of hBCI-assisted groups with the performance of non-BCI groups using standard majority voting, and non-BCI groups using weighted voting based on reported decision confidence. We also investigate the impact on group performance of a computer-mediated form of communication between members. Results across three experiments suggest that the hBCI provides significant advantages over non-BCI decision methods in all cases. We also found that our form of communication increases individual error rates by almost 50% compared to non-communicating observers, which also results in worse group performance. Communication also makes reported confidence uncorrelated with the decision correctness, thereby nullifying its value in weighing votes. In summary, best decisions are achieved by hBCI-assisted, non-communicating groups.

Alpha neurofeedback training improves SSVEP-based BCI performance.

PubMed

Wan, Feng; da Cruz, Janir Nuno; Nan, Wenya; Wong, Chi Man; Vai, Mang I; Rosa, Agostinho

2016-06-01

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user's SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs. An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with 'low' performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison. The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group. These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects' performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.

Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution.

PubMed

Höhne, Johannes; Holz, Elisa; Staiger-Sälzer, Pit; Müller, Klaus-Robert; Kübler, Andrea; Tangermann, Michael

2014-01-01

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

PubMed Central

Höhne, Johannes; Holz, Elisa; Staiger-Sälzer, Pit; Müller, Klaus-Robert; Kübler, Andrea; Tangermann, Michael

2014-01-01

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT. PMID:25162231

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

PubMed Central

Saeedi, Sareh; Schneider, Christoph; Millán, José del R.

2018-01-01

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI–user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface—application, BCI output, and electroencephalography (EEG) neuroimaging—with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions. PMID:29746465

A Hybrid Brain-Computer Interface Based on the Fusion of P300 and SSVEP Scores.

PubMed

Yin, Erwei; Zeyl, Timothy; Saab, Rami; Chau, Tom; Hu, Dewen; Zhou, Zongtan

2015-07-01

The present study proposes a hybrid brain-computer interface (BCI) with 64 selectable items based on the fusion of P300 and steady-state visually evoked potential (SSVEP) brain signals. With this approach, row/column (RC) P300 and two-step SSVEP paradigms were integrated to create two hybrid paradigms, which we denote as the double RC (DRC) and 4-D spellers. In each hybrid paradigm, the target is simultaneously detected based on both P300 and SSVEP potentials as measured by the electroencephalogram. We further proposed a maximum-probability estimation (MPE) fusion approach to combine the P300 and SSVEP on a score level and compared this approach to other approaches based on linear discriminant analysis, a naïve Bayes classifier, and support vector machines. The experimental results obtained from thirteen participants indicated that the 4-D hybrid paradigm outperformed the DRC paradigm and that the MPE fusion achieved higher accuracy compared with the other approaches. Importantly, 12 of the 13 participants, using the 4-D paradigm achieved an accuracy of over 90% and the average accuracy was 95.18%. These promising results suggest that the proposed hybrid BCI system could be used in the design of a high-performance BCI-based keyboard.

Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention.

PubMed

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian Kc

2016-10-01

Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research. In this study, we use source imaging-a neuroimaging technique that projects EEG signals onto the surface of the brain-in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention. Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques-a major design choice in most BCIs. Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.

A brain-computer interface with vibrotactile biofeedback for haptic information.

PubMed

Chatterjee, Aniruddha; Aggarwal, Vikram; Ramos, Ander; Acharya, Soumyadipta; Thakor, Nitish V

2007-10-17

It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy. A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance. Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.

Effect of higher frequency on the classification of steady-state visual evoked potentials

NASA Astrophysics Data System (ADS)

Won, Dong-Ok; Hwang, Han-Jeong; Dähne, Sven; Müller, Klaus-Robert; Lee, Seong-Whan

2016-02-01

Objective. Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. Approach. We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. Main results. The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. Significance. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

Effect of higher frequency on the classification of steady-state visual evoked potentials.

PubMed

Won, Dong-Ok; Hwang, Han-Jeong; Dähne, Sven; Müller, Klaus-Robert; Lee, Seong-Whan

2016-02-01

Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

[The P300 based brain-computer interface: effect of stimulus position in a stimulus train].

PubMed

Ganin, I P; Shishkin, S L; Kochetova, A G; Kaplan, A Ia

2012-01-01

The P300 brain-computer interface (BCI) is currently the most efficient BCI. This interface is based on detection of the P300 wave of the brain potentials evoked when a symbol related to the intended input is highlighted. To increase operation speed of the P300 BCI, reduction of the number of stimuli repetitions is needed. This reduction leads to increase of the relative contribution to the input symbol detection from the reaction to the first target stimulus. It is known that the event-related potentials (ERP) to the first stimulus presentations can be different from the ERP to stimuli presented latter. In particular, the amplitude of responses to the first stimulus presentations is often increased, which is beneficial for their recognition by the BCI. However, this effect was not studied within the BCI framework. The current study examined the ERP obtained from healthy participants (n = 14) in the standard P300 BCI paradigm using 10 trials, as well as in the modified P300 BCI with stimuli presented on moving objects in triple-trial (n = 6) and single-trial (n = 6) stimulation modes. Increased ERP amplitude was observed in response to the first target stimuli in both conditions, as well as in the single-trial mode comparing to triple-trial. We discuss the prospects of using the specific features of the ERP to first stimuli and the single-trial ERP for optimizing the high-speed modes in the P300 BCIs.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

A binary motor imagery tasks based brain-computer interface for two-dimensional movement control

NASA Astrophysics Data System (ADS)

Xia, Bin; Cao, Lei; Maysam, Oladazimi; Li, Jie; Xie, Hong; Su, Caixia; Birbaumer, Niels

2017-12-01

Objective. Two-dimensional movement control is a popular issue in brain-computer interface (BCI) research and has many applications in the real world. In this paper, we introduce a combined control strategy to a binary class-based BCI system that allows the user to move a cursor in a two-dimensional (2D) plane. Users focus on a single moving vector to control 2D movement instead of controlling vertical and horizontal movement separately. Approach. Five participants took part in a fixed-target experiment and random-target experiment to verify the effectiveness of the combination control strategy under the fixed and random routine conditions. Both experiments were performed in a virtual 2D dimensional environment and visual feedback was provided on the screen. Main results. The five participants achieved an average hit rate of 98.9% and 99.4% for the fixed-target experiment and the random-target experiment, respectively. Significance. The results demonstrate that participants could move the cursor in the 2D plane effectively. The proposed control strategy is based only on a basic two-motor imagery BCI, which enables more people to use it in real-life applications.

Defining and quantifying users' mental Imagery-based BCI skills: a first step.

PubMed

Lotte, Fabien; Jeunet, Camille

2018-05-17

While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, Classification Accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for Mental Imagery (MI) BCIs, independently of any classification algorithm. Approach: We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier. Main results: By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new metrics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG. Significance: Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source. © 2018 IOP Publishing Ltd.

Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.

PubMed

Amanpour, Behzad; Erfanian, Abbas

2013-01-01

An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface.

PubMed

Ng, Kian B; Bradley, Andrew P; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface

NASA Astrophysics Data System (ADS)

Ng, Kian B.; Bradley, Andrew P.; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Software platform for rapid prototyping of NIRS brain computer interfacing techniques.

PubMed

Matthews, Fiachra; Soraghan, Christopher; Ward, Tomas E; Markham, Charles; Pearlmutter, Barak A

2008-01-01

This paper describes the control system of a next-generation optical brain-computer interface (BCI). Using functional near-infrared spectroscopy (fNIRS) as a BCI modality is a relatively new concept, and research has only begun to explore approaches for its implementation. It is necessary to have a system by which it is possible to investigate the signal processing and classification techniques available in the BCI community. Most importantly, these techniques must be easily testable in real-time applications. The system we describe was built using LABVIEW, a graphical programming language designed for interaction with National Instruments hardware. This platform allows complete configurability from hardware control and regulation, testing and filtering in a graphical interface environment.

[Design and implementation of controlling smart car systems using P300 brain-computer interface].

PubMed

Wang, Jinjia; Yang, Chengjie; Hu, Bei

2013-04-01

Using human electroencephalogram (EEG) to control external devices in order to achieve a variety of functions has been focus of the field of brain-computer interface (BCI) research. P300 is experiments which stimulate the eye to produce EEG by using letters flashing, and then identify the corresponding letters. In this paper, some improvements based on the P300 experiments were made??. Firstly, the matrix of flashing letters were modified into words which represent a certain sense. Secondly, the BCI2000 procedures were added with the corresponding source code. Thirdly, the smart car systems were designed using the radiofrequency signal. Finally it was realized that the evoked potentials were used to control the state of the smart car.

Estimating the mutual information of an EEG-based Brain-Computer Interface.

PubMed

Schlögl, A; Neuper, C; Pfurtscheller, G

2002-01-01

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.

Geometry aware Stationary Subspace Analysis

DTIC Science & Technology

2016-11-22

approach to handling non-stationarity is to remove or minimize it before attempting to analyze the data. In the context of brain computer interface ( BCI ...context of brain computer interface ( BCI ) data analysis, two such note-worthy methods are stationary subspace analysis (SSA) (von Bünau et al., 2009a... BCI systems, is sCSP. Its goal is to project the data onto a subspace in which the various data classes are more separable. The sCSP method directs

Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.

PubMed

Mundahl, John; Jianjun Meng; He, Jeffrey; Bin He

2016-08-01

Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.

A brain-computer interface based cognitive training system for healthy elderly: a randomized control pilot study for usability and preliminary efficacy.

PubMed

Lee, Tih-Shih; Goh, Siau Juinn Alexa; Quek, Shin Yi; Phillips, Rachel; Guan, Cuntai; Cheung, Yin Bun; Feng, Lei; Teng, Stephanie Sze Wei; Wang, Chuan Chu; Chin, Zheng Yang; Zhang, Haihong; Ng, Tze Pin; Lee, Jimmy; Keefe, Richard; Krishnan, K Ranga Rama

2013-01-01

Cognitive decline in aging is a pressing issue associated with significant healthcare costs and deterioration in quality of life. Previously, we reported the successful use of a novel brain-computer interface (BCI) training system in improving symptoms of attention deficit hyperactivity disorder. Here, we examine the feasibility of the BCI system with a new game that incorporates memory training in improving memory and attention in a pilot sample of healthy elderly. This study investigates the safety, usability and acceptability of our BCI system to elderly, and obtains an efficacy estimate to warrant a phase III trial. Thirty-one healthy elderly were randomized into intervention (n = 15) and waitlist control arms (n = 16). Intervention consisted of an 8-week training comprising 24 half-hour sessions. A usability and acceptability questionnaire was administered at the end of training. Safety was investigated by querying users about adverse events after every session. Efficacy of the system was measured by the change of total score from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) before and after training. Feedback on the usability and acceptability questionnaire was positive. No adverse events were reported for all participants across all sessions. Though the median difference in the RBANS change scores between arms was not statistically significant, an effect size of 0.6SD was obtained, which reflects potential clinical utility according to Simon's randomized phase II trial design. Pooled data from both arms also showed that the median change in total scores pre and post-training was statistically significant (Mdn = 4.0; p<0.001). Specifically, there were significant improvements in immediate memory (p = 0.038), visuospatial/constructional (p = 0.014), attention (p = 0.039), and delayed memory (p<0.001) scores. Our BCI-based system shows promise in improving memory and attention in healthy elderly, and appears to be safe, user-friendly and acceptable to senior users. Given the efficacy signal, a phase III trial is warranted. ClinicalTrials.gov NCT01661894.

An online hybrid BCI system based on SSVEP and EMG

NASA Astrophysics Data System (ADS)

Lin, Ke; Cinetto, Andrea; Wang, Yijun; Chen, Xiaogang; Gao, Shangkai; Gao, Xiaorong

2016-04-01

Objective. A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance. Approach. A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct. Main results. The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min-1 respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min-1, SSVEP: 60.2 bits min-1). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was enhanced to 85.8% and 90.9 bit min-1. Significance. These results suggest that the hybrid system proposed here is suitable for practical use.

A mechanical mounting system for functional near-infrared spectroscopy brain imaging studies

NASA Astrophysics Data System (ADS)

Coyle, Shirley; Markham, Charles; Lanigan, William; Ward, Tomas

2005-06-01

In this work a mechanical optode mounting system for functional brain imaging with light is presented. The particular application here is a non-invasive optical brain computer interface (BCI) working in the near-infrared range. A BCI is a device that allows a user to interact with their environment through thought processes alone. Their most common use is as a communication aid for the severely disabled. We have recently pioneered the use of optical techniques for such BCI systems rather than the usual electrical modality. Our optical BCI detects characteristic changes in the cerebral haemodynamic responses that occur during motor imagery tasks. On detection of features of the optical response, resulting from localised haemodynamic changes, the BCI translates such responses and provides visual feedback to the user. While signal processing has a large part to play in terms of optimising performance we have found that it is the mechanical mounting of the optical sources and detectors (optodes) that has the greatest bearing on the performance of the system and indeed presents many interesting and novel challenges with regard to sensor placement, depth of penetration, signal intensity, artifact reduction and robustness of measurement. Here a solution is presented that accommodates the range of experimental parameters required for the application as well as meeting many of the challenges outlined above. This is the first time that a concerted study on optode mounting systems for optical BCIs has been attempted and it is hoped this paper may stimulate further research in this area.

Electrochemical and catalytic properties of Ni/BaCe0.75Y0.25O3-δ anode for direct ammonia-fueled solid oxide fuel cells.

PubMed

Yang, Jun; Molouk, Ahmed Fathi Salem; Okanishi, Takeou; Muroyama, Hiroki; Matsui, Toshiaki; Eguchi, Koichi

2015-04-08

In this study, Ni/BaCe0.75Y0.25O3-δ (Ni/BCY25) was investigated as an anode for direct ammonia-fueled solid oxide fuel cells. The catalytic activity of Ni/BCY25 for ammonia decomposition was found to be remarkably higher than Ni/8 mol % Y2O3-ZrO2 and Ni/Ce0.90Gd0.10O1.95. The poisoning effect of water and hydrogen on ammonia decomposition reaction over Ni/BCY25 was evaluated. In addition, an electrolyte-supported SOFC employing BaCe0.90Y0.10O3-δ (BCY10) electrolyte and Ni/BCY25 anode was fabricated, and its electrochemical performance was investigated at 550-650 °C with supply of ammonia and hydrogen fuel gases. The effect of water content in anode gas on the cell performance was also studied. Based on these results, it was concluded that Ni/BCY25 was a promising anode for direct ammonia-fueled SOFCs. An anode-supported single cell denoted as Ni/BCY25|BCY10|Sm0.5Sr0.5CoO3-δ was also fabricated, and maximum powder density of 216 and 165 mW cm(-2) was achieved at 650 and 600 °C, for ammonia fuel, respectively.

Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

PubMed Central

Millán, J. d. R.; Rupp, R.; Müller-Putz, G. R.; Murray-Smith, R.; Giugliemma, C.; Tangermann, M.; Vidaurre, C.; Cincotti, F.; Kübler, A.; Leeb, R.; Neuper, C.; Müller, K.-R.; Mattia, D.

2010-01-01

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices. PMID:20877434

A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Wang, Yijun; Zhang, Shangen; Gao, Shangkai; Hu, Yong; Gao, Xiaorong

2017-04-01

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted. Approach. This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based ‘on/off’ stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis. Main results. SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance. Significance. These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.

Functional source separation and hand cortical representation for a brain–computer interface feature extraction

PubMed Central

Tecchio, Franca; Porcaro, Camillo; Barbati, Giulia; Zappasodi, Filippo

2007-01-01

A brain–computer interface (BCI) can be defined as any system that can track the person's intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non-differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features – spatial and time-frequency properties – are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients. PMID:17331989

An audiovisual BCI system for assisting clinical communication assessment in patients with disorders of consciousness: a case study.

PubMed

Fei Wang; Yanbin He; Jun Qu; Qiuyou Xie; Qing Lin; Xiaoxiao Ni; Yan Chen; Ronghao Yu; Chin-Teng Lin; Yuanqing Li

2016-08-01

The JFK Coma Recovery Scale-Revised (JFK CRS-R), a behavioral scale, is often used for clinical assessments of patients with disorders of consciousness (DOC), such as patients in a vegetative state. However, there has been a high rate of clinical misdiagnosis with the JFK CRS-R because patients with severe brain injures cannot provide sufficient behavioral responses. It is particularly difficult to evaluate the communication function in DOC patients using the JFK CRS-R because a higher level of behavioral responses is needed for communication assessments than for many other assessments, such as an auditory startle assessment. Brain-computer interfaces (BCIs), which provide control and communication by detecting changes in brain signals, can be used to evaluate patients with DOC without the need of behavioral expressions. In this paper, we proposed an audiovisual BCI system to supplement the JFK CRS-R in assessing the communication ability of patients with DOC. In the graphic user interface of the BCI system, two word buttons ("Yes" and "No" in Chinese) were randomly displayed in the left and right sides and flashed in an alternating manner. When a word button flashed, its corresponding spoken word was broadcast from an ipsilateral headphone. The use of semantically congruent audiovisual stimuli improves the detection performance of the BCI system. Similar to the JFK CRS-R, several situation-orientation questions were presented one by one to patients with DOC. For each question, the patient was required to provide his/her answer by selectively focusing on an audiovisual stimulus (audiovisual "Yes" or "No"). As a case study, we applied our BCI system in a patient with DOC who was clinically diagnosed as being in a minimally conscious state (MCS). According to the JFK CRS-R assessment, this patient was unable to communicate consistently. However, he achieved a high accuracy of 86.5% in our BCI experiment. This result indicates his reliable communication ability and demonstrates the effectiveness of our system.

Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.

PubMed

Riechmann, Hannes; Finke, Andrea; Ritter, Helge

2016-06-01

Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use.

Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients.

PubMed

Bai, Ou; Lin, Peter; Huang, Dandan; Fei, Ding-Yu; Floeter, Mary Kay

2010-08-01

Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load. Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10min and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5-2h in a single visit. Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1+/-8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4+/-3.4% with motor execution and 83.3+/-8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery. Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior. The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients. Copyright 2010 International Federation of Clinical Neurophysiology. All rights reserved.

[Neurophysiological Foundations and Practical Realizations of the Brain-Machine Interfaces the Technology in Neurological Rehabilitation].

PubMed

Kaplan, A Ya

2016-01-01

Technology brain-computer interface (BCI) based on the registration and interpretation of EEG has recently become one of the most popular developments in neuroscience and psychophysiology. This is due not only to the intended future use of these technologies in many areas of practical human activity, but also to the fact that IMC--is a completely new paradigm in psychophysiology, allowing test hypotheses about the possibilities of the human brain to the development of skills of interaction with the outside world without the mediation of the motor system, i.e. only with the help of voluntary modulation of EEG generators. This paper examines the theoretical and experimental basis, the current state and prospects of development of training, communicational and assisting complexes based on BCI to control them without muscular effort on the basis of mental commands detected in the EEG of patients with severely impaired speech and motor system.

Assistive device with conventional, alternative, and brain-computer interface inputs to enhance interaction with the environment for people with amyotrophic lateral sclerosis: a feasibility and usability study.

PubMed

Schettini, Francesca; Riccio, Angela; Simione, Luca; Liberati, Giulia; Caruso, Mario; Frasca, Vittorio; Calabrese, Barbara; Mecella, Massimo; Pizzimenti, Alessia; Inghilleri, Maurizio; Mattia, Donatella; Cincotti, Febo

2015-03-01

To evaluate the feasibility and usability of an assistive technology (AT) prototype designed to be operated with conventional/alternative input channels and a P300-based brain-computer interface (BCI) in order to provide users who have different degrees of muscular impairment resulting from amyotrophic lateral sclerosis (ALS) with communication and environmental control applications. Proof-of-principle study with a convenience sample. An apartment-like space designed to be fully accessible by people with motor disabilities for occupational therapy, placed in a neurologic rehabilitation hospital. End-users with ALS (N=8; 5 men, 3 women; mean age ± SD, 60 ± 12 y) recruited by a clinical team from an ALS center. Three experimental conditions based on (1) a widely validated P300-based BCI alone; (2) the AT prototype operated by a conventional/alternative input device tailored to the specific end-user's residual motor abilities; and (3) the AT prototype accessed by a P300-based BCI. These 3 conditions were presented to all participants in 3 different sessions. System usability was evaluated in terms of effectiveness (accuracy), efficiency (written symbol rate, time for correct selection, workload), and end-user satisfaction (overall satisfaction) domains. A comparison of the data collected in the 3 conditions was performed. Effectiveness and end-user satisfaction did not significantly differ among the 3 experimental conditions. Condition III was less efficient than condition II as expressed by the longer time for correct selection. A BCI can be used as an input channel to access an AT by persons with ALS, with no significant reduction of usability. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.

PubMed

Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

2015-06-30

Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC.

A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection

PubMed Central

Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

2015-01-01

Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC. PMID:26123281

An optical brain computer interface for environmental control.

PubMed

Ayaz, Hasan; Shewokis, Patricia A; Bunce, Scott; Onaral, Banu

2011-01-01

A brain computer interface (BCI) is a system that translates neurophysiological signals detected from the brain to supply input to a computer or to control a device. Volitional control of neural activity and its real-time detection through neuroimaging modalities are key constituents of BCI systems. The purpose of this study was to develop and test a new BCI design that utilizes intention-related cognitive activity within the dorsolateral prefrontal cortex using functional near infrared (fNIR) spectroscopy. fNIR is a noninvasive, safe, portable and affordable optical technique with which to monitor hemodynamic changes, in the brain's cerebral cortex. Because of its portability and ease of use, fNIR is amenable to deployment in ecologically valid natural working environments. We integrated a control paradigm in a computerized 3D virtual environment to augment interactivity. Ten healthy participants volunteered for a two day study in which they navigated a virtual environment with keyboard inputs, but were required to use the fNIR-BCI for interaction with virtual objects. Results showed that participants consistently utilized the fNIR-BCI with an overall success rate of 84% and volitionally increased their cerebral oxygenation level to trigger actions within the virtual environment.

Removal of proprioception by BCI raises a stronger body ownership illusion in control of a humanlike robot

PubMed Central

Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

2016-01-01

Body ownership illusions provide evidence that our sense of self is not coherent and can be extended to non-body objects. Studying about these illusions gives us practical tools to understand the brain mechanisms that underlie body recognition and the experience of self. We previously introduced an illusion of body ownership transfer (BOT) for operators of a very humanlike robot. This sensation of owning the robot’s body was confirmed when operators controlled the robot either by performing the desired motion with their body (motion-control) or by employing a brain-computer interface (BCI) that translated motor imagery commands to robot movement (BCI-control). The interesting observation during BCI-control was that the illusion could be induced even with a noticeable delay in the BCI system. Temporal discrepancy has always shown critical weakening effects on body ownership illusions. However the delay-robustness of BOT during BCI-control raised a question about the interaction between the proprioceptive inputs and delayed visual feedback in agency-driven illusions. In this work, we compared the intensity of BOT illusion for operators in two conditions; motion-control and BCI-control. Our results revealed a significantly stronger BOT illusion for the case of BCI-control. This finding highlights BCI’s potential in inducing stronger agency-driven illusions by building a direct communication between the brain and controlled body, and therefore removing awareness from the subject’s own body. PMID:27654174

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Beyond maximum speed—a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI)

NASA Astrophysics Data System (ADS)

Kaufmann, Tobias; Kübler, Andrea

2014-10-01

Objective. The speed of brain-computer interfaces (BCI), based on event-related potentials (ERP), is inherently limited by the commonly used one-stimulus paradigm. In this paper, we introduce a novel paradigm that can increase the spelling speed by a factor of 2, thereby extending the one-stimulus paradigm to a two-stimulus paradigm. Two different stimuli (a face and a symbol) are presented at the same time, superimposed on different characters and ERPs are classified using a multi-class classifier. Here, we present the proof-of-principle that is achieved with healthy participants. Approach. Eight participants were confronted with the novel two-stimulus paradigm and, for comparison, with two one-stimulus paradigms that used either one of the stimuli. Classification accuracies (percentage of correctly predicted letters) and elicited ERPs from the three paradigms were compared in a comprehensive offline analysis. Main results. The accuracies slightly decreased with the novel system compared to the established one-stimulus face paradigm. However, the use of two stimuli allowed for spelling at twice the maximum speed of the one-stimulus paradigms, and participants still achieved an average accuracy of 81.25%. This study introduced an alternative way of increasing the spelling speed in ERP-BCIs and illustrated that ERP-BCIs may not yet have reached their speed limit. Future research is needed in order to improve the reliability of the novel approach, as some participants displayed reduced accuracies. Furthermore, a comparison to the most recent BCI systems with individually adjusted, rapid stimulus timing is needed to draw conclusions about the practical relevance of the proposed paradigm. Significance. We introduced a novel two-stimulus paradigm that might be of high value for users who have reached the speed limit with the current one-stimulus ERP-BCI systems.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface

PubMed Central

Kim, Youngmoo E.

2017-01-01

Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emerging and has shown potential as a supplement or replacement for electroencephalography. However, studies often use only two or three motor-imagery tasks, limiting the number of available commands. In this work, we present the results of the first four-class motor-imagery-based online fNIRS-BCI for robot control. Thirteen participants utilized upper- and lower-limb motor-imagery tasks (left hand, right hand, left foot, and right foot) that were mapped to four high-level commands (turn left, turn right, move forward, and move backward) to control the navigation of a simulated or real robot. A significant improvement in classification accuracy was found between the virtual-robot-based BCI (control of a virtual robot) and the physical-robot BCI (control of the DARwIn-OP humanoid robot). Differences were also found in the oxygenated hemoglobin activation patterns of the four tasks between the first and second BCI. These results corroborate previous findings that motor imagery can be improved with feedback and imply that a four-class motor-imagery-based fNIRS-BCI could be feasible with sufficient subject training. PMID:28804712

Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface.

PubMed

Batula, Alyssa M; Kim, Youngmoo E; Ayaz, Hasan

2017-01-01

Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emerging and has shown potential as a supplement or replacement for electroencephalography. However, studies often use only two or three motor-imagery tasks, limiting the number of available commands. In this work, we present the results of the first four-class motor-imagery-based online fNIRS-BCI for robot control. Thirteen participants utilized upper- and lower-limb motor-imagery tasks (left hand, right hand, left foot, and right foot) that were mapped to four high-level commands (turn left, turn right, move forward, and move backward) to control the navigation of a simulated or real robot. A significant improvement in classification accuracy was found between the virtual-robot-based BCI (control of a virtual robot) and the physical-robot BCI (control of the DARwIn-OP humanoid robot). Differences were also found in the oxygenated hemoglobin activation patterns of the four tasks between the first and second BCI. These results corroborate previous findings that motor imagery can be improved with feedback and imply that a four-class motor-imagery-based fNIRS-BCI could be feasible with sufficient subject training.

A pilot randomized controlled trial using EEG-based brain-computer interface training for a Chinese-speaking group of healthy elderly.

PubMed

Lee, Tih-Shih; Quek, Shin Yi; Goh, Siau Juinn Alexa; Phillips, Rachel; Guan, Cuntai; Cheung, Yin Bun; Feng, Lei; Wang, Chuan Chu; Chin, Zheng Yang; Zhang, Haihong; Lee, Jimmy; Ng, Tze Pin; Krishnan, K Ranga Rama

2015-01-01

There is growing evidence that cognitive training (CT) can improve the cognitive functioning of the elderly. CT may be influenced by cultural and linguistic factors, but research examining CT programs has mostly been conducted on Western populations. We have developed an innovative electroencephalography (EEG)-based brain-computer interface (BCI) CT program that has shown preliminary efficacy in improving cognition in 32 healthy English-speaking elderly adults in Singapore. In this second pilot trial, we examine the acceptability, safety, and preliminary efficacy of our BCI CT program in healthy Chinese-speaking Singaporean elderly. Thirty-nine elderly participants were randomized into intervention (n=21) and wait-list control (n=18) arms. Intervention consisted of 24 half-hour sessions with our BCI-based CT training system to be completed in 8 weeks; the control arm received the same intervention after an initial 8-week waiting period. At the end of the training, a usability and acceptability questionnaire was administered. Efficacy was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), which was translated and culturally adapted for the Chinese-speaking local population. Users were asked about any adverse events experienced after each session as a safety measure. The training was deemed easily usable and acceptable by senior users. The median difference in the change scores pre- and post-training of the modified RBANS total score was 8.0 (95% confidence interval [CI]: 0.0-16.0, P=0.042) higher in the intervention arm than waitlist control, while the mean difference was 9.0 (95% CI: 1.7-16.2, P=0.017). Ten (30.3%) participants reported a total of 16 adverse events - all of which were graded "mild" except for one graded "moderate". Our BCI training system shows potential in improving cognition in both English- and Chinese-speaking elderly, and deserves further evaluation in a Phase III trial. Overall, participants responded positively on the usability and acceptability questionnaire.

Transferring brain-computer interfaces beyond the laboratory: successful application control for motor-disabled users.

PubMed

Leeb, Robert; Perdikis, Serafeim; Tonin, Luca; Biasiucci, Andrea; Tavella, Michele; Creatura, Marco; Molina, Alberto; Al-Khodairy, Abdul; Carlson, Tom; Millán, José D R

2013-10-01

Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough for this? Especially the successful operation of applications - like text entry systems or assistive mobility devices such as tele-presence robots - requires a good level of BCI control. How much training is needed to achieve such a level? Is it possible to train naïve end-users in 10 days to successfully control such applications? In this work, we report our experiences of training 24 motor-disabled participants at rehabilitation clinics or at the end-users' homes, without BCI experts present. We also share the lessons that we have learned through transferring BCI technologies from the lab to the user's home or clinics. The most important outcome is that 50% of the participants achieved good BCI performance and could successfully control the applications (tele-presence robot and text-entry system). In the case of the tele-presence robot the participants achieved an average performance ratio of 0.87 (max. 0.97) and for the text entry application a mean of 0.93 (max. 1.0). The lessons learned and the gathered user feedback range from pure BCI problems (technical and handling), to common communication issues among the different people involved, and issues encountered while controlling the applications. The points raised in this paper are very widely applicable and we anticipate that they might be faced similarly by other groups, if they move on to bringing the BCI technology to the end-user, to home environments and towards application prototype control. Copyright © 2013 Elsevier B.V. All rights reserved.

Adaptive hybrid brain-computer interaction: ask a trainer for assistance!

PubMed

Müller-Putz, Gernot R; Steyrl, David; Faller, Josef

2014-01-01

In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.

Incorporating advanced language models into the P300 speller using particle filtering

NASA Astrophysics Data System (ADS)

Speier, W.; Arnold, C. W.; Deshpande, A.; Knall, J.; Pouratian, N.

2015-08-01

Objective. The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject’s electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. Approach. Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. Main result. This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. Significance. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.

The Self-Paced Graz Brain-Computer Interface: Methods and Applications

PubMed Central

Scherer, Reinhold; Schloegl, Alois; Lee, Felix; Bischof, Horst; Janša, Janez; Pfurtscheller, Gert

2007-01-01

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth. PMID:18350133

Workload measurement in a communication application operated through a P300-based brain-computer interface

NASA Astrophysics Data System (ADS)

Riccio, A.; Leotta, F.; Bianchi, L.; Aloise, F.; Zickler, C.; Hoogerwerf, E.-J.; Kübler, A.; Mattia, D.; Cincotti, F.

2011-04-01

Advancing the brain-computer interface (BCI) towards practical applications in technology-based assistive solutions for people with disabilities requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on evaluation of the user's subjective workload and satisfaction. The secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates the P300-based BCI with commercially available software under two conditions—visual stimuli needed to evoke the P300 response were either overlaid onto the application's graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by means of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.

Context-aware adaptive spelling in motor imagery BCI

NASA Astrophysics Data System (ADS)

Perdikis, S.; Leeb, R.; Millán, J. d. R.

2016-06-01

Objective. This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject’s performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. Approach. Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree’s language model to improve online expectation-maximization maximum-likelihood estimation. Main results. Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. Significance. We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

Context-aware adaptive spelling in motor imagery BCI.

PubMed

Perdikis, S; Leeb, R; Millán, J D R

2016-06-01

This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject's performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation. Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree's language model to improve online expectation-maximization maximum-likelihood estimation. Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users. We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.

The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces

PubMed Central

Carabalona, Roberta

2017-01-01

Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type. We manipulated stimulus type (factor STIM: either characters or icons), color (factor COLOR: white, green) and timing (factor SPEED: fast, slow). Each BCI session consisted of training (without feedback) and performance phase (with feedback), both in copy-spelling. For fast flashing spellers, we observed a performance worsening for white icon-speller. Our findings are consistent with existing results reported on end-users using identical white×fast spellers, indicating independence of worsening trend from users' group. The use of slow stimulation timing shed a new light on the perceptual and cognitive phenomena related to the use of a BCI-speller during both the training and the performance phase. We found a significant STIM main effect for the N1 component on Pz and PO7 during the training phase and on PO8 during the performance phase, whereas in both phases neither the STIM×COLOR interaction nor the COLOR main effect was statistically significant. After collapsing data for factor COLOR, it emerged a statistically significant modulation of N1 amplitude depending to the phase of BCI session: N1 was more negative for icons than for characters both on Pz and PO7 (training), whereas the opposite modulation was observed for PO8 (performance). Results indicate that both feedback and expertise with respect to the stimulus type can modulate the N1 component and that icons require more perceptual analysis. Therefore, fast flashing is likely to be more detrimental for end-users' performance in case of icon-spellers. In conclusion, the interplay between stimulus type and timing seems relevant for a satisfactory and efficient end-user's BCI-experience. PMID:28713233

The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces.

PubMed

Carabalona, Roberta

2017-01-01

Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type. We manipulated stimulus type (factor STIM: either characters or icons), color (factor COLOR: white, green) and timing (factor SPEED: fast, slow). Each BCI session consisted of training (without feedback) and performance phase (with feedback), both in copy-spelling. For fast flashing spellers, we observed a performance worsening for white icon-speller. Our findings are consistent with existing results reported on end-users using identical white×fast spellers, indicating independence of worsening trend from users' group. The use of slow stimulation timing shed a new light on the perceptual and cognitive phenomena related to the use of a BCI-speller during both the training and the performance phase. We found a significant STIM main effect for the N1 component on P z and PO 7 during the training phase and on PO 8 during the performance phase, whereas in both phases neither the STIM×COLOR interaction nor the COLOR main effect was statistically significant. After collapsing data for factor COLOR, it emerged a statistically significant modulation of N1 amplitude depending to the phase of BCI session: N1 was more negative for icons than for characters both on P z and PO 7 (training), whereas the opposite modulation was observed for PO 8 (performance). Results indicate that both feedback and expertise with respect to the stimulus type can modulate the N1 component and that icons require more perceptual analysis. Therefore, fast flashing is likely to be more detrimental for end-users' performance in case of icon-spellers. In conclusion, the interplay between stimulus type and timing seems relevant for a satisfactory and efficient end-user's BCI-experience.

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project.

PubMed

Carmichael, Clare; Carmichael, Patrick

2014-01-01

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants. The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of "ideal types" of disabled users may reinforce stereotypes or drown out participant "voices". Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a "duty of care" while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies.

An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy.

PubMed

Leamy, Darren J; Kocijan, Juš; Domijan, Katarina; Duffin, Joseph; Roche, Richard Ap; Commins, Sean; Collins, Ronan; Ward, Tomas E

2014-01-28

Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset. This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

Alpha neurofeedback training improves SSVEP-based BCI performance

NASA Astrophysics Data System (ADS)

Wan, Feng; Nuno da Cruz, Janir; Nan, Wenya; Wong, Chi Man; Vai, Mang I.; Rosa, Agostinho

2016-06-01

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user’s SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs. Approach. An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with ‘low’ performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison. Main results. The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group. Significance. These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects’ performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

PubMed Central

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; Ball, Kenneth R.; Lance, Brent J.

2016-01-01

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system. PMID:27713685

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

PubMed

Waytowich, Nicholas R; Lawhern, Vernon J; Bohannon, Addison W; Ball, Kenneth R; Lance, Brent J

2016-01-01

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

A validation of 11 body-condition indices in a giant snake species that exhibits positive allometry.

PubMed

Falk, Bryan G; Snow, Ray W; Reed, Robert N

2017-01-01

Body condition is a gauge of the energy stores of an animal, and though it has important implications for fitness, survival, competition, and disease, it is difficult to measure directly. Instead, body condition is frequently estimated as a body condition index (BCI) using length and mass measurements. A desirable BCI should accurately reflect true body condition and be unbiased with respect to size (i.e., mean BCI estimates should not change across different length or mass ranges), and choosing the most-appropriate BCI is not straightforward. We evaluated 11 different BCIs in 248 Burmese pythons (Python bivittatus), organisms that, like other snakes, exhibit simple body plans well characterized by length and mass. We found that the length-mass relationship in Burmese pythons is positively allometric, where mass increases rapidly with respect to length, and this allowed us to explore the effects of allometry on BCI verification. We employed three alternative measures of 'true' body condition: percent fat, scaled fat, and residual fat. The latter two measures mostly accommodated allometry in true body condition, but percent fat did not. Our inferences of the best-performing BCIs depended heavily on our measure of true body condition, with most BCIs falling into one of two groups. The first group contained most BCIs based on ratios, and these were associated with percent fat and body length (i.e., were biased). The second group contained the scaled mass index and most of the BCIs based on linear regressions, and these were associated with both scaled and residual fat but not body length (i.e., were unbiased). Our results show that potential differences in measures of true body condition should be explored in BCI verification studies, particularly in organisms undergoing allometric growth. Furthermore, the caveats of each BCI and similarities to other BCIs are important to consider when determining which BCI is appropriate for any particular taxon.

A validation of 11 body-condition indices in a giant snake species that exhibits positive allometry

USGS Publications Warehouse

Falk, Bryan; Snow, Ray W.; Reed, Robert N.

2017-01-01

Body condition is a gauge of the energy stores of an animal, and though it has important implications for fitness, survival, competition, and disease, it is difficult to measure directly. Instead, body condition is frequently estimated as a body condition index (BCI) using length and mass measurements. A desirable BCI should accurately reflect true body condition and be unbiased with respect to size (i.e., mean BCI estimates should not change across different length or mass ranges), and choosing the most-appropriate BCI is not straightforward. We evaluated 11 different BCIs in 248 Burmese pythons (Python bivittatus), organisms that, like other snakes, exhibit simple body plans well characterized by length and mass. We found that the length-mass relationship in Burmese pythons is positively allometric, where mass increases rapidly with respect to length, and this allowed us to explore the effects of allometry on BCI verification. We employed three alternative measures of ‘true’ body condition: percent fat, scaled fat, and residual fat. The latter two measures mostly accommodated allometry in true body condition, but percent fat did not. Our inferences of the best-performing BCIs depended heavily on our measure of true body condition, with most BCIs falling into one of two groups. The first group contained most BCIs based on ratios, and these were associated with percent fat and body length (i.e., were biased). The second group contained the scaled mass index and most of the BCIs based on linear regressions, and these were associated with both scaled and residual fat but not body length (i.e., were unbiased). Our results show that potential differences in measures of true body condition should be explored in BCI verification studies, particularly in organisms undergoing allometric growth. Furthermore, the caveats of each BCI and similarities to other BCIs are important to consider when determining which BCI is appropriate for any particular taxon.

A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces.

PubMed

Wang, Yijun; Chen, Xiaogang; Gao, Xiaorong; Gao, Shangkai

2017-10-01

This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain- computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was . For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds. The dataset can be used as a benchmark dataset to compare the methods for stimulus coding and target identification in SSVEP-based BCIs. Through offline simulation, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data. The dataset also provides high-quality data for computational modeling of SSVEPs. The dataset is freely available fromhttp://bci.med.tsinghua.edu.cn/download.html.

Designing Guiding Systems for Brain-Computer Interfaces

PubMed Central

Kosmyna, Nataliya; Lécuyer, Anatole

2017-01-01

Brain–Computer Interface (BCI) community has focused the majority of its research efforts on signal processing and machine learning, mostly neglecting the human in the loop. Guiding users on how to use a BCI is crucial in order to teach them to produce stable brain patterns. In this work, we explore the instructions and feedback for BCIs in order to provide a systematic taxonomy to describe the BCI guiding systems. The purpose of our work is to give necessary clues to the researchers and designers in Human–Computer Interaction (HCI) in making the fusion between BCIs and HCI more fruitful but also to better understand the possibilities BCIs can provide to them. PMID:28824400

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study

NASA Astrophysics Data System (ADS)

Mainsah, B. O.; Collins, L. M.; Colwell, K. A.; Sellers, E. W.; Ryan, D. B.; Caves, K.; Throckmorton, C. S.

2015-02-01

Objective. The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. Approach. We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user’s EEG data. We further enhanced the algorithm by incorporating information about the user’s language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Main results. Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. Significance. We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.

PubMed

Mainsah, B O; Collins, L M; Colwell, K A; Sellers, E W; Ryan, D B; Caves, K; Throckmorton, C S

2015-02-01

The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user's EEG data. We further enhanced the algorithm by incorporating information about the user's language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.

Increasing BCI Communication Rates with Dynamic Stopping Towards More Practical Use: An ALS Study

PubMed Central

Mainsah, B. O.; Collins, L. M.; Colwell, K. A.; Sellers, E. W.; Ryan, D. B.; Caves, K.; Throckmorton, C. S.

2015-01-01

Objective The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. Approach We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute signal-to-noise ratio of a user’s electroencephalography data. We further enhanced the algorithm by incorporating information about the user’s language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Main Results Results from online testing of the dynamic stopping algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/sec (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the dynamic stopping algorithms. Significance We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication. PMID:25588137

Classifying BCI signals from novice users with extreme learning machine

NASA Astrophysics Data System (ADS)

Rodríguez-Bermúdez, Germán; Bueno-Crespo, Andrés; José Martinez-Albaladejo, F.

2017-07-01

Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

PubMed

Michie, Susan; Thomas, James; Johnston, Marie; Aonghusa, Pol Mac; Shawe-Taylor, John; Kelly, Michael P; Deleris, Léa A; Finnerty, Ailbhe N; Marques, Marta M; Norris, Emma; O'Mara-Eves, Alison; West, Robert

2017-10-18

Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a 'Knowledge System' that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question 'What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?'. The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury.

PubMed

Rohm, Martin; Schneiders, Matthias; Müller, Constantin; Kreilinger, Alex; Kaiser, Vera; Müller-Putz, Gernot R; Rupp, Rüdiger

2013-10-01

The bilateral loss of the grasp function associated with a lesion of the cervical spinal cord severely limits the affected individuals' ability to live independently and return to gainful employment after sustaining a spinal cord injury (SCI). Any improvement in lost or limited grasp function is highly desirable. With current neuroprostheses, relevant improvements can be achieved in end users with preserved shoulder and elbow, but missing hand function. The aim of this single case study is to show that (1) with the support of hybrid neuroprostheses combining functional electrical stimulation (FES) with orthoses, restoration of hand, finger and elbow function is possible in users with high-level SCI and (2) shared control principles can be effectively used to allow for a brain-computer interface (BCI) control, even if only moderate BCI performance is achieved after extensive training. The individual in this study is a right-handed 41-year-old man who sustained a traumatic SCI in 2009 and has a complete motor and sensory lesion at the level of C4. He is unable to generate functionally relevant movements of the elbow, hand and fingers on either side. He underwent extensive FES training (30-45min, 2-3 times per week for 6 months) and motor imagery (MI) BCI training (415 runs in 43 sessions over 12 months). To meet individual needs, the system was designed in a modular fashion including an intelligent control approach encompassing two input modalities, namely an MI-BCI and shoulder movements. After one year of training, the end user's MI-BCI performance ranged from 50% to 93% (average: 70.5%). The performance of the hybrid system was evaluated with different functional assessments. The user was able to transfer objects of the grasp-and-release-test and he succeeded in eating a pretzel stick, signing a document and eating an ice cream cone, which he was unable to do without the system. This proof-of-concept study has demonstrated that with the support of hybrid FES systems consisting of FES and a semiactive orthosis, restoring hand, finger and elbow function is possible in a tetraplegic end-user. Remarkably, even after one year of training and 415 MI-BCI runs, the end user's average BCI performance remained at about 70%. This supports the view that in high-level tetraplegic subjects, an initially moderate BCI performance cannot be improved by extensive training. However, this aspect has to be validated in future studies with a larger population. Copyright © 2013 Elsevier B.V. All rights reserved.

Brain computer interface for operating a robot

NASA Astrophysics Data System (ADS)

Nisar, Humaira; Balasubramaniam, Hari Chand; Malik, Aamir Saeed

2013-10-01

A Brain-Computer Interface (BCI) is a hardware/software based system that translates the Electroencephalogram (EEG) signals produced by the brain activity to control computers and other external devices. In this paper, we will present a non-invasive BCI system that reads the EEG signals from a trained brain activity using a neuro-signal acquisition headset and translates it into computer readable form; to control the motion of a robot. The robot performs the actions that are instructed to it in real time. We have used the cognitive states like Push, Pull to control the motion of the robot. The sensitivity and specificity of the system is above 90 percent. Subjective results show a mixed trend of the difficulty level of the training activities. The quantitative EEG data analysis complements the subjective results. This technology may become very useful for the rehabilitation of disabled and elderly people.

Effects of training pre-movement sensorimotor rhythms on behavioral performance

NASA Astrophysics Data System (ADS)

McFarland, Dennis J.; Sarnacki, William A.; Wolpaw, Jonathan R.

2015-12-01

Objective. Brain-computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects. Approach. Eight individuals first performed a joystick-based cursor-movement task with variable warning periods. Targets appeared randomly on a video monitor and subjects moved the cursor to the target and pressed a select button within 2 s. SMR features in the pre-movement EEG that correlated with performance speed and accuracy were identified. The subjects then learned to increase or decrease these features to control a two-target BCI task. Following successful BCI training, they were asked to increase or decrease SMR amplitude in order to initiate the joystick task. Main results. After BCI training, pre-movement SMR amplitude was correlated with performance in subjects with initial poor performance: lower amplitude was associated with faster and more accurate movement. The beneficial effect on performance of lower SMR amplitude was greater in subjects with lower initial performance levels. Significance. These results indicate that BCI-based SMR training can affect a standard motor behavior. They provide a rationale for studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful motor function.

Probing command following in patients with disorders of consciousness using a brain-computer interface.

PubMed

Lulé, Dorothée; Noirhomme, Quentin; Kleih, Sonja C; Chatelle, Camille; Halder, Sebastian; Demertzi, Athena; Bruno, Marie-Aurélie; Gosseries, Olivia; Vanhaudenhuyse, Audrey; Schnakers, Caroline; Thonnard, Marie; Soddu, Andrea; Kübler, Andrea; Laureys, Steven

2013-01-01

To determine if brain-computer interfaces (BCIs) could serve as supportive tools for detecting consciousness in patients with disorders of consciousness by detecting response to command and communication. We tested a 4-choice auditory oddball EEG-BCI paradigm on 16 healthy subjects and 18 patients in a vegetative state/unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and in locked-in syndrome (LIS). Subjects were exposed to 4 training trials and 10 -12 questions. Thirteen healthy subjects and one LIS patient were able to communicate using the BCI. Four of those did not present with a P3. One MCS patient showed command following with the BCI while no behavioral response could be detected at bedside. All other patients did not show any response to command and could not communicate with the BCI. The present study provides evidence that EEG based BCI can detect command following in patients with altered states of consciousness and functional communication in patients with locked-in syndrome. However, BCI approaches have to be simplified to increase sensitivity. For some patients without any clinical sign of consciousness, a BCI might bear the potential to employ a "yes-no" spelling device offering the hope of functional interactive communication. Copyright © 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A sLORETA study for gaze-independent BCI speller.

PubMed

Xingwei An; Jinwen Wei; Shuang Liu; Dong Ming

2017-07-01

EEG-based BCI (brain-computer-interface) speller, especially gaze-independent BCI speller, has become a hot topic in recent years. It provides direct spelling device by non-muscular method for people with severe motor impairments and with limited gaze movement. Brain needs to conduct both stimuli-driven and stimuli-related attention in fast presented BCI paradigms for such BCI speller applications. Few researchers studied the mechanism of brain response to such fast presented BCI applications. In this study, we compared the distribution of brain activation in visual, auditory, and audio-visual combined stimuli paradigms using sLORETA (standardized low-resolution brain electromagnetic tomography). Between groups comparisons showed the importance of visual and auditory stimuli in audio-visual combined paradigm. They both contribute to the activation of brain regions, with visual stimuli being the predominate stimuli. Visual stimuli related brain region was mainly located at parietal and occipital lobe, whereas response in frontal-temporal lobes might be caused by auditory stimuli. These regions played an important role in audio-visual bimodal paradigms. These new findings are important for future study of ERP speller as well as the mechanism of fast presented stimuli.

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

NASA Astrophysics Data System (ADS)

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

2017-09-01

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

NASA Astrophysics Data System (ADS)

Meng, Jianjun; Zhang, Shuying; Bekyo, Angeliki; Olsoe, Jaron; Baxter, Bryan; He, Bin

2016-12-01

Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

Short progressive muscle relaxation or motor coordination training does not increase performance in a brain-computer interface based on sensorimotor rhythms (SMR).

PubMed

Botrel, L; Acqualagna, L; Blankertz, B; Kübler, A

2017-11-01

Brain computer interfaces (BCIs) allow for controlling devices through modulation of sensorimotor rhythms (SMR), yet a profound number of users is unable to achieve sufficient accuracy. Here, we investigated if visuo-motor coordination (VMC) training or Jacobsen's progressive muscle relaxation (PMR) prior to BCI use would increase later performance compared to a control group who performed a reading task (CG). Running the study in two different BCI-labs, we achieved a joint sample size of N=154 naïve participants. No significant effect of either intervention (VMC, PMR, control) was found on resulting BCI performance. Relaxation level and visuo-motor performance were associated with later BCI performance in one BCI-lab but not in the other. These mixed results do not indicate a strong potential of VMC or PMR for boosting performance. Yet further research with different training parameters or experimental designs is needed to complete the picture. Copyright © 2017 Elsevier B.V. All rights reserved.

On the control of brain-computer interfaces by users with cerebral palsy.

PubMed

Daly, Ian; Billinger, Martin; Laparra-Hernández, José; Aloise, Fabio; García, Mariano Lloria; Faller, Josef; Scherer, Reinhold; Müller-Putz, Gernot

2013-09-01

Brain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs. This study is conducted with 14 individuals with CP attempting to control two standard online BCIs (1) based upon sensorimotor rhythm modulations, and (2) based upon steady state visual evoked potentials. Of the 14 users, 8 are able to use one or other of the BCIs, online, with a statistically significant level of accuracy, without prior training. Classification results are driven by neurophysiological activity and not seen to correlate with occurrences of artifacts. However, many of these users' accuracies, while statistically significant, would require either more training or more advanced methods before practical BCI control would be possible. The results indicate that BCIs may be controlled by individuals with CP but that many issues need to be overcome before practical application use may be achieved. This is the first study to assess the ability of a large group of different individuals with CP to gain control of an online BCI system. The results indicate that six users could control a sensorimotor rhythm BCI and three a steady state visual evoked potential BCI at statistically significant levels of accuracy (SMR accuracies; mean ± STD, 0.821 ± 0.116, SSVEP accuracies; 0.422 ± 0.069). Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Understanding Randomness and Its Impact on Student Learning: Lessons Learned from Building the Biology Concept Inventory (BCI)

ERIC Educational Resources Information Center

Garvin-Doxas, Kathy; Klymkowsky, Michael W.

2008-01-01

While researching student assumptions for the development of the Biology Concept Inventory (BCI; http://bioliteracy.net), we found that a wide class of student difficulties in molecular and evolutionary biology appears to be based on deep-seated, and often unaddressed, misconceptions about random processes. Data were based on more than 500…

Neurophysiological substrates of stroke patients with motor imagery-based Brain-Computer Interface training.

PubMed

Li, Mingfen; Liu, Ye; Wu, Yi; Liu, Sirao; Jia, Jie; Zhang, Liqing

2014-06-01

We investigated the efficacy of motor imagery-based Brain Computer Interface (MI-based BCI) training for eight stroke patients with severe upper extremity paralysis using longitudinal clinical assessments. The results were compared with those of a control group (n = 7) that only received FES (Functional Electrical Stimulation) treatment besides conventional therapies. During rehabilitation training, changes in the motor function of the upper extremity and in the neurophysiologic electroencephalographic (EEG) were observed for two groups. After 8 weeks of training, a significant improvement in the motor function of the upper extremity for the BCI group was confirmed (p < 0.05 for ARAT), simultaneously with the activation of bilateral cerebral hemispheres. Additionally, event-related desynchronization (ERD) of the affected sensorimotor cortexes (SMCs) was significantly enhanced when compared to the pretraining course, which was only observed in the BCI group (p < 0.05). Furthermore, the activation of affected SMC and parietal lobe were determined to contribute to motor function recovery (p < 0.05). In brief, our findings demonstrate that MI-based BCI training can enhance the motor function of the upper extremity for stroke patients by inducing the optimal cerebral motor functional reorganization.

BCI Performance and Brain Metabolism Profile in Severely Brain-Injured Patients Without Response to Command at Bedside.

PubMed

Annen, Jitka; Blandiaux, Séverine; Lejeune, Nicolas; Bahri, Mohamed A; Thibaut, Aurore; Cho, Woosang; Guger, Christoph; Chatelle, Camille; Laureys, Steven

2018-01-01

Detection and interpretation of signs of "covert command following" in patients with disorders of consciousness (DOC) remains a challenge for clinicians. In this study, we used a tactile P3-based BCI in 12 patients without behavioral command following, attempting to establish "covert command following." These results were then confronted to cerebral metabolism preservation as measured with glucose PET (FDG-PET). One patient showed "covert command following" (i.e., above-threshold BCI performance) during the active tactile paradigm. This patient also showed a higher cerebral glucose metabolism within the language network (presumably required for command following) when compared with the other patients without "covert command-following" but having a cerebral glucose metabolism indicative of minimally conscious state. Our results suggest that the P3-based BCI might probe "covert command following" in patients without behavioral response to command and therefore could be a valuable addition in the clinical assessment of patients with DOC.

Utilizing gamma band to improve mental task based brain-computer interface design.

PubMed

Palaniappan, Ramaswamy

2006-09-01

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.

Performance improvement of ERP-based brain-computer interface via varied geometric patterns.

PubMed

Ma, Zheng; Qiu, Tianshuang

2017-12-01

Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.

P300 brain computer interface: current challenges and emerging trends

PubMed Central

Fazel-Rezai, Reza; Allison, Brendan Z.; Guger, Christoph; Sellers, Eric W.; Kleih, Sonja C.; Kübler, Andrea

2012-01-01

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility. PMID:22822397

Brain-Computer Interfaces for 1-D and 2-D Cursor Control: Designs Using Volitional Control of the EEG Spectrum or Steady-State Visual Evoked Potentials

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Matthews, Bryan; Rosipal, Roman

2005-01-01

We have developed and tested two EEG-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KF LS classifier to map power spectra of 30-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject s average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: a) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal EOG signals, b) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from eight electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about three minutes. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 seconds for movement initiation and turning.

Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise.

PubMed

Boyce, Sarah J; Choudhury, Kingshuk Roy; Samei, Ehsan

2013-09-01

Stereoscopic chest biplane correlation imaging (stereo∕BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo∕BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes. Measurements for the eMTF were taken for two phantom sizes with an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI. Stereo∕BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by ∼20%. For both phantoms, at isodose, eDQE(0) for stereo∕BCI was ∼100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by ∼10 times for stereo∕BCI. Increasing the dose did not improve eDQE. The detectability index for stereo∕BCI was ∼100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose. The findings indicate that stereo∕BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.

Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise

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

Boyce, Sarah J.; Choudhury, Kingshuk Roy; Samei, Ehsan

2013-09-15

Purpose: Stereoscopic chest biplane correlation imaging (stereo/BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo/BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes.Methods: Measurements for the eMTF were taken for two phantom sizes withmore » an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI.Results: Stereo/BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by ∼20%. For both phantoms, at isodose, eDQE(0) for stereo/BCI was ∼100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by ∼10 times for stereo/BCI. Increasing the dose did not improve eDQE. The detectability index for stereo/BCI was ∼100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose.Conclusions: The findings indicate that stereo/BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.« less

Composing only by thought: Novel application of the P300 brain-computer interface.

PubMed

Pinegger, Andreas; Hiebel, Hannah; Wriessnegger, Selina C; Müller-Putz, Gernot R

2017-01-01

The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people.

Composing only by thought: Novel application of the P300 brain-computer interface

PubMed Central

Hiebel, Hannah; Wriessnegger, Selina C.; Müller-Putz, Gernot R.

2017-01-01

The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people. PMID:28877175

SSVEP-BCI implementation for 37-40 Hz frequency range.

PubMed

Müller, Sandra Mara Torres; Diez, Pablo F; Bastos-Filho, Teodiano Freire; Sarcinelli-Filho, Mário; Mut, Vicente; Laciar, Eric

2011-01-01

This work presents a Brain-Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), using higher stimulus frequencies (>30 Hz). Using a statistical test and a decision tree, the real-time EEG registers of six volunteers are analyzed, with the classification result updated each second. The BCI developed does not need any kind of settings or adjustments, which makes it more general. Offline results are presented, which corresponds to a correct classification rate of up to 99% and a Information Transfer Rate (ITR) of up to 114.2 bits/min.

Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform.

PubMed

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

2017-10-01

Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different. The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.

Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability.

PubMed

Young, Brittany M; Nigogosyan, Zack; Nair, Veena A; Walton, Léo M; Song, Jie; Tyler, Mitchell E; Edwards, Dorothy F; Caldera, Kristin; Sattin, Justin A; Williams, Justin C; Prabhakaran, Vivek

2014-01-01

Therapies involving new technologies such as brain-computer interfaces (BCI) are being studied to determine their potential for interventional rehabilitation after acute events such as stroke produce lasting impairments. While studies have examined the use of BCI devices by individuals with disabilities, many such devices are intended to address a specific limitation and have been studied when this limitation or disability is present in isolation. Little is known about the therapeutic potential of these devices for individuals with multiple disabilities with an acquired impairment overlaid on a secondary long-standing disability. We describe a case in which a male patient with congenital deafness suffered a right pontine ischemic stroke, resulting in persistent weakness of his left hand and arm. This patient volunteer completed four baseline assessments beginning at 4 months after stroke onset and subsequently underwent 6 weeks of interventional rehabilitation therapy using a closed-loop neurofeedback BCI device with visual, functional electrical stimulation, and tongue stimulation feedback modalities. Additional assessments were conducted at the midpoint of therapy, upon completion of therapy, and 1 month after completing all BCI therapy. Anatomical and functional MRI scans were obtained at each assessment, along with behavioral measures including the Stroke Impact Scale (SIS) and the Action Research Arm Test (ARAT). Clinically significant improvements in behavioral measures were noted over the course of BCI therapy, with more than 10 point gains in both the ARAT scores and scores for the SIS hand function domain. Neuroimaging during finger tapping of the impaired hand also showed changes in brain activation patterns associated with BCI therapy. This case study demonstrates the potential for individuals who have preexisting disability or possible atypical brain organization to learn to use a BCI system that may confer some rehabilitative benefit.

Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability

PubMed Central

Young, Brittany M.; Nigogosyan, Zack; Nair, Veena A.; Walton, Léo M.; Song, Jie; Tyler, Mitchell E.; Edwards, Dorothy F.; Caldera, Kristin; Sattin, Justin A.; Williams, Justin C.; Prabhakaran, Vivek

2014-01-01

Therapies involving new technologies such as brain-computer interfaces (BCI) are being studied to determine their potential for interventional rehabilitation after acute events such as stroke produce lasting impairments. While studies have examined the use of BCI devices by individuals with disabilities, many such devices are intended to address a specific limitation and have been studied when this limitation or disability is present in isolation. Little is known about the therapeutic potential of these devices for individuals with multiple disabilities with an acquired impairment overlaid on a secondary long-standing disability. We describe a case in which a male patient with congenital deafness suffered a right pontine ischemic stroke, resulting in persistent weakness of his left hand and arm. This patient volunteer completed four baseline assessments beginning at 4 months after stroke onset and subsequently underwent 6 weeks of interventional rehabilitation therapy using a closed-loop neurofeedback BCI device with visual, functional electrical stimulation, and tongue stimulation feedback modalities. Additional assessments were conducted at the midpoint of therapy, upon completion of therapy, and 1 month after completing all BCI therapy. Anatomical and functional MRI scans were obtained at each assessment, along with behavioral measures including the Stroke Impact Scale (SIS) and the Action Research Arm Test (ARAT). Clinically significant improvements in behavioral measures were noted over the course of BCI therapy, with more than 10 point gains in both the ARAT scores and scores for the SIS hand function domain. Neuroimaging during finger tapping of the impaired hand also showed changes in brain activation patterns associated with BCI therapy. This case study demonstrates the potential for individuals who have preexisting disability or possible atypical brain organization to learn to use a BCI system that may confer some rehabilitative benefit. PMID:25009491

Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.

PubMed

McFarland, Dennis J; Krusienski, Dean J; Wolpaw, Jonathan R

2006-01-01

The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.

Asynchronous brain-computer interface for cognitive assessment in people with cerebral palsy

NASA Astrophysics Data System (ADS)

Alcaide-Aguirre, R. E.; Warschausky, S. A.; Brown, D.; Aref, A.; Huggins, J. E.

2017-12-01

Objective. Typically, clinical measures of cognition require motor or speech responses. Thus, a significant percentage of people with disabilities are not able to complete standardized assessments. This situation could be resolved by employing a more accessible test administration method, such as a brain-computer interface (BCI). A BCI can circumvent motor and speech requirements by translating brain activity to identify a subject’s response. By eliminating the need for motor or speech input, one could use a BCI to assess an individual who previously did not have access to clinical tests. Approach. We developed an asynchronous, event-related potential BCI-facilitated administration procedure for the peabody picture vocabulary test (PPVT-IV). We then tested our system in typically developing individuals (N  =  11), as well as people with cerebral palsy (N  =  19) to compare results to the standardized PPVT-IV format and administration. Main results. Standard scores on the BCI-facilitated PPVT-IV, and the standard PPVT-IV were highly correlated (r  =  0.95, p  <  0.001), with a mean difference of 2.0  ±  6.4 points, which is within the standard error of the PPVT-IV. Significance. Thus, our BCI-facilitated PPVT-IV provided comparable results to the standard PPVT-IV, suggesting that populations for whom standardized cognitive tests are not accessible could benefit from our BCI-facilitated approach.

A two-class self-paced BCI to control a robot in four directions.

PubMed

Ron-Angevin, Ricardo; Velasco-Alvarez, Francisco; Sancha-Ros, Salvador; da Silva-Sauer, Leandro

2011-01-01

In this work, an electroencephalographic analysis-based, self-paced (asynchronous) brain-computer interface (BCI) is proposed to control a mobile robot using four different navigation commands: turn right, turn left, move forward and move back. In order to reduce the probability of misclassification, the BCI is to be controlled with only two mental tasks (relaxed state versus imagination of right hand movements), using an audio-cued interface. Four healthy subjects participated in the experiment. After two sessions controlling a simulated robot in a virtual environment (which allowed the user to become familiar with the interface), three subjects successfully moved the robot in a real environment. The obtained results show that the proposed interface enables control over the robot, even for subjects with low BCI performance. © 2011 IEEE

Dose-response relationships using brain–computer interface technology impact stroke rehabilitation

PubMed Central

Young, Brittany M.; Nigogosyan, Zack; Walton, Léo M.; Remsik, Alexander; Song, Jie; Nair, Veena A.; Tyler, Mitchell E.; Edwards, Dorothy F.; Caldera, Kristin; Sattin, Justin A.; Williams, Justin C.; Prabhakaran, Vivek

2015-01-01

Brain–computer interfaces (BCIs) are an emerging novel technology for stroke rehabilitation. Little is known about how dose-response relationships for BCI therapies affect brain and behavior changes. We report preliminary results on stroke patients (n = 16, 11 M) with persistent upper extremity motor impairment who received therapy using a BCI system with functional electrical stimulation of the hand and tongue stimulation. We collected MRI scans and behavioral data using the Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) before, during, and after the therapy period. Using anatomical and functional MRI, we computed Laterality Index (LI) for brain activity in the motor network during impaired hand finger tapping. Changes from baseline LI and behavioral scores were assessed for relationships with dose, intensity, and frequency of BCI therapy. We found that gains in SIS Strength were directly responsive to BCI therapy: therapy dose and intensity correlated positively with increased SIS Strength (p ≤ 0.05), although no direct relationships were identified with ARAT or 9-HPT scores. We found behavioral measures that were not directly sensitive to differences in BCI therapy administration but were associated with concurrent brain changes correlated with BCI therapy administration parameters: therapy dose and intensity showed significant (p ≤ 0.05) or trending (0.05 < p < 0.1) negative correlations with LI changes, while therapy frequency did not affect LI. Reductions in LI were then correlated (p ≤ 0.05) with increased SIS Activities of Daily Living scores and improved 9-HPT performance. Therefore, some behavioral changes may be reflected by brain changes sensitive to differences in BCI therapy administration, while others such as SIS Strength may be directly responsive to BCI therapy administration. Data preliminarily suggest that when using BCI in stroke rehabilitation, therapy frequency may be less important than dose and intensity. PMID:26157378

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

SSVEP-based BCI for manipulating three-dimensional contents and devices

NASA Astrophysics Data System (ADS)

Mun, Sungchul; Cho, Sungjin; Whang, Mincheol; Ju, Byeong-Kwon; Park, Min-Chul

2012-06-01

Brain Computer Interface (BCI) studies have been done to help people manipulate electronic devices in a 2D space but less has been done for a vigorous 3D environment. The purpose of this study was to investigate the possibility of applying Steady State Visual Evoked Potentials (SSVEPs) to a 3D LCD display. Eight subjects (4 females) ranging in age between 20 to 26 years old participated in the experiment. They performed simple navigation tasks on a simple 2D space and virtual environment with/without 3D flickers generated by a Flim-Type Patterned Retarder (FPR). The experiments were conducted in a counterbalanced order. The results showed that 3D stimuli enhanced BCI performance, but no significant effects were found due to the small number of subjects. Visual fatigue that might be evoked by 3D stimuli was negligible in this study. The proposed SSVEP BCI combined with 3D flickers can allow people to control home appliances and other equipment such as wheelchairs, prosthetics, and orthotics without encountering dangerous situations that may happen when using BCIs in real world. 3D stimuli-based SSVEP BCI would motivate people to use 3D displays and vitalize the 3D related industry due to its entertainment value and high performance.

Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly.

PubMed

Gomez-Pilar, Javier; Corralejo, Rebeca; Nicolas-Alonso, Luis F; Álvarez, Daniel; Hornero, Roberto

2016-11-01

Neurofeedback training (NFT) has shown to be promising and useful to rehabilitate cognitive functions. Recently, brain-computer interfaces (BCIs) were used to restore brain plasticity by inducing brain activity with an NFT. In our study, we hypothesized that an NFT with a motor imagery-based BCI (MI-BCI) could enhance cognitive functions related to aging effects. To assess the effectiveness of our MI-BCI application, 63 subjects (older than 60 years) were recruited. This novel application was used by 31 subjects (NFT group). Their Luria neuropsychological test scores were compared with the remaining 32 subjects, who did not perform NFT (control group). Electroencephalogram changes measured by relative power (RP) endorsed cognitive potential findings under study: visuospatial, oral language, memory, intellectual and attention functions. Three frequency bands were selected to assess cognitive changes: 12, 18, and 21 Hz (bandwidth 3 Hz). Significant increases (p < 0.01) in the RP of these frequency bands were found. Moreover, results from cognitive tests showed significant improvements (p < 0.01) in four cognitive functions after performing five NFT sessions: visuospatial, oral language, memory, and intellectual. This established evidence in the association between NFT performed by a MI-BCI and enhanced cognitive performance. Therefore, it could be a novel approach to help elderly people.

Evaluating brain-computer interface performance using color in the P300 checkerboard speller.

PubMed

Ryan, D B; Townsend, G; Gates, N A; Colwell, K; Sellers, E W

2017-10-01

Current Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli. In addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i.e., Jumpwise). Online results (n=36) showed that GC provides higher accuracy and information transfer rate than the CI and GW conditions. Waveform analysis showed that GC produced higher amplitude ERPs than CI and GW. Information transfer rate was improved by the Jumpwise-selected channel locations in all conditions. Unique color stimuli (GC) improved BCI performance and enhanced ERPs. Jumpwise-selected electrode locations improved offline performance. These results show that in a checkerboard paradigm, unique color stimuli increase BCI performance, are preferred by participants, and are important to the design of end-user applications; thus, could lead to an increase in end-user performance and acceptance of BCI technology. Copyright © 2017 International Federation of Clinical Neurophysiology. All rights reserved.

Bipolar electrode selection for a motor imagery based brain computer interface

NASA Astrophysics Data System (ADS)

Lou, Bin; Hong, Bo; Gao, Xiaorong; Gao, Shangkai

2008-09-01

A motor imagery based brain-computer interface (BCI) provides a non-muscular communication channel that enables people with paralysis to control external devices using their motor imagination. Reducing the number of electrodes is critical to improving the portability and practicability of the BCI system. A novel method is proposed to reduce the number of electrodes to a total of four by finding the optimal positions of two bipolar electrodes. Independent component analysis (ICA) is applied to find the source components of mu and alpha rhythms, and optimal electrodes are chosen by comparing the projection weights of sources on each channel. The results of eight subjects demonstrate the better classification performance of the optimal layout compared with traditional layouts, and the stability of this optimal layout over a one week interval was further verified.

Practical Designs of Brain-Computer Interfaces Based on the Modulation of EEG Rhythms

NASA Astrophysics Data System (ADS)

Wang, Yijun; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

A brain-computer interface (BCI) is a communication channel which does not depend on the brain's normal output pathways of peripheral nerves and muscles [1-3]. It supplies paralyzed patients with a new approach to communicate with the environment. Among various brain monitoring methods employed in current BCI research, electroencephalogram (EEG) is the main interest due to its advantages of low cost, convenient operation and non-invasiveness. In present-day EEG-based BCIs, the following signals have been paid much attention: visual evoked potential (VEP), sensorimotor mu/beta rhythms, P300 evoked potential, slow cortical potential (SCP), and movement-related cortical potential (MRCP). Details about these signals can be found in chapter "Brain Signals for Brain-Computer Interfaces". These systems offer some practical solutions (e.g., cursor movement and word processing) for patients with motor disabilities.

Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

NASA Astrophysics Data System (ADS)

Fomina, Tatiana; Lohmann, Gabriele; Erb, Michael; Ethofer, Thomas; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2016-12-01

Objective. Electroencephalographic (EEG) brain-computer interfaces (BCIs) hold promise in restoring communication for patients with completely locked-in stage amyotrophic lateral sclerosis (ALS). However, these patients cannot use existing EEG-based BCIs, arguably because such systems rely on brain processes that are impaired in the late stages of ALS. In this work, we introduce a novel BCI designed for patients in late stages of ALS based on high-level cognitive processes that are less likely to be affected by ALS. Approach. We trained two ALS patients via EEG-based neurofeedback to use self-regulation of theta or gamma oscillations in the precuneus for basic communication. Because there is a tight connection between the precuneus and consciousness, precuneus oscillations are arguably generated by high-level cognitive processes, which are less likely to be affected by ALS than processes linked to the peripheral nervous system. Main results. Both patients learned to self-regulate their precuneus oscillations and achieved stable online decoding accuracy over the course of disease progression. One patient achieved a mean online decoding accuracy in a binary decision task of 70.55% across 26 training sessions, and the other patient achieved 59.44% across 16 training sessions. We provide empirical evidence that these oscillations were cortical in nature and originated from the intersection of the precuneus, cuneus, and posterior cingulate. Significance. Our results establish that ALS patients can employ self-regulation of precuneus oscillations for communication. Such a BCI is likely to be available to ALS patients as long as their consciousness supports communication.

Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Geneva.

PubMed

Pun, Thierry; Alecu, Teodor Iulian; Chanel, Guillaume; Kronegg, Julien; Voloshynovskiy, Sviatoslav

2006-06-01

This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.

Balancing a simulated inverted pendulum through motor imagery: an EEG-based real-time control paradigm.

PubMed

Yue, Jingwei; Zhou, Zongtan; Jiang, Jun; Liu, Yadong; Hu, Dewen

2012-08-30

Most brain-computer interfaces (BCIs) are non-time-restraint systems. However, the method used to design a real-time BCI paradigm for controlling unstable devices is still a challenging problem. This paper presents a real-time feedback BCI paradigm for controlling an inverted pendulum on a cart (IPC). In this paradigm, sensorimotor rhythms (SMRs) were recorded using 15 active electrodes placed on the surface of the subject's scalp. Subsequently, common spatial pattern (CSP) was used as the basic filter to extract spatial patterns. Finally, linear discriminant analysis (LDA) was used to translate the patterns into control commands that could stabilize the simulated inverted pendulum. Offline trainings were employed to teach the subjects to execute corresponding mental tasks, such as left/right hand motor imagery. Five subjects could successfully balance the online inverted pendulum for more than 35s. The results demonstrated that BCIs are able to control nonlinear unstable devices. Furthermore, the demonstration and extension of real-time continuous control might be useful for the real-life application and generalization of BCI. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Brain-computer interfaces in neurological rehabilitation.

PubMed

Daly, Janis J; Wolpaw, Jonathan R

2008-11-01

Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance

PubMed Central

Meng, Jianjun; Edelman, Bradley J.; Olsoe, Jaron; Jacobs, Gabriel; Zhang, Shuying; Beyko, Angeliki; He, Bin

2018-01-01

Motor imagery–based brain–computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session—performance increases asymptotically by increasing the number of channels, saturates, and then decreases—no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method. PMID:29681792

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.

PubMed

Meng, Jianjun; Edelman, Bradley J; Olsoe, Jaron; Jacobs, Gabriel; Zhang, Shuying; Beyko, Angeliki; He, Bin

2018-01-01

Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.

EEG datasets for motor imagery brain-computer interface.

PubMed

Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

2017-07-01

Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude

PubMed Central

Halder, Sebastian; Hammer, Eva Maria; Kleih, Sonja Claudia; Bogdan, Martin; Rosenstiel, Wolfgang; Birbaumer, Niels; Kübler, Andrea

2013-01-01

Objective Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball. Methods Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude. Results Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy. Conclusions Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection. Significance Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population. PMID:23457444

Novel Features for Brain-Computer Interfaces

PubMed Central

Woon, W. L.; Cichocki, A.

2007-01-01

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques. PMID:18364991

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

PubMed

Shin, Jaeyoung; Kim, Do-Won; Müller, Klaus-Robert; Hwang, Han-Jeong

2018-06-05

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

DOE PAGES

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.; ...

2016-09-22

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIGmore » method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.« less

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

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

Waytowich, Nicholas R.; Lawhern, Vernon J.; Bohannon, Addison W.

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIGmore » method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.« less

MindEdit: A P300-based text editor for mobile devices.

PubMed

Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

2017-01-01

Practical application of Brain-Computer Interfaces (BCIs) requires that the whole BCI system be portable. The mobility of BCI systems involves two aspects: making the electroencephalography (EEG) recording devices portable, and developing software applications with low computational complexity to be able to run on low computational-power devices such as tablets and smartphones. This paper addresses the development of MindEdit; a P300-based text editor for Android-based devices. Given the limited resources of mobile devices and their limited computational power, a novel ensemble classifier is utilized that uses Principal Component Analysis (PCA) features to identify P300 evoked potentials from EEG recordings. PCA computations in the proposed method are channel-based as opposed to concatenating all channels as in traditional feature extraction methods; thus, this method has less computational complexity compared to traditional P300 detection methods. The performance of the method is demonstrated on data recorded from MindEdit on an Android tablet using the Emotiv wireless neuroheadset. Results demonstrate the capability of the introduced PCA ensemble classifier to classify P300 data with maximum average accuracy of 78.37±16.09% for cross-validation data and 77.5±19.69% for online test data using only 10 trials per symbol and a 33-character training dataset. Our analysis indicates that the introduced method outperforms traditional feature extraction methods. For a faster operation of MindEdit, a variable number of trials scheme is introduced that resulted in an online average accuracy of 64.17±19.6% and a maximum bitrate of 6.25bit/min. These results demonstrate the efficacy of using the developed BCI application with mobile devices. Copyright © 2016 Elsevier Ltd. All rights reserved.

Critical issues using brain-computer interfaces for augmentative and alternative communication.

PubMed

Hill, Katya; Kovacs, Thomas; Shin, Sangeun

2015-03-01

Brain-computer interfaces (BCIs) may potentially be of significant practical value to patients in advanced stages of amyotrophic lateral sclerosis and locked-in syndrome for whom conventional augmentative and alternative communication (AAC) systems, which require some measure of consistent voluntary muscle control, are not satisfactory options. However, BCIs have primarily been used for communication in laboratory research settings. This article discusses 4 critical issues that should be addressed as BCIs are translated out of laboratory settings to become fully functional BCI/AAC systems that may be implemented clinically. These issues include (1) identification of primary, secondary, and tertiary system features; (2) integrating BCI/AAC systems in the World Health Organization's International Classification of Functioning, Disability and Health framework; (3) implementing language-based assessment and intervention; and (4) performance measurement. A clinical demonstration project is presented as an example of research beginning to address these critical issues. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

A brain-computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex

NASA Astrophysics Data System (ADS)

Grosse-Wentrup, Moritz; Schölkopf, Bernhard

2014-10-01

Objective. Brain-computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain-computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.

A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.

PubMed

Placidi, Giuseppe; Petracca, Andrea; Spezialetti, Matteo; Iacoviello, Daniela

2016-01-01

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.

Papers from the Fifth International Brain-Computer Interface Meeting

NASA Astrophysics Data System (ADS)

Huggins, Jane E.; Wolpaw, Jonathan R.

2014-06-01

Brain-computer interfaces (BCIs), also known as brain-machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use. The papers in this special section report some of the work presented at the Fifth International BCI Meeting held on 3-7 June 2013 at the Asilomar Conference Center in Pacific Grove, California, USA. Like its predecessors over the past 15 years, this meeting was supported by the National Institutes of Health, the National Science Foundation, and a variety of other governmental and private sponsors [1]. This fifth meeting was organized and managed by a program committee of BCI researchers from throughout the world [2]. It retained the distinctive retreat-style format developed by the Wadsworth Center researchers who organized and managed the first four meetings. The 301 attendees came from 165 research groups in 29 countries; 37% were students or postdoctoral fellows. Of more than 200 extended abstracts submitted for peer review, 25 were selected for oral presentation [3], and 181 were presented as posters [4] and published in the open-access conference proceedings [5]. The meeting featured 19 highly interactive workshops [6] covering the broad spectrum of BCI research and development, as well as many demonstrations of BCI systems and associated technology. Like the first four meetings, this one included attendees and embraced topics from across the broad spectrum of disciplines essential to effective BCI research and development, including neuroscience, engineering, applied mathematics, computer science, psychology and rehabilitation. In addition, this fifth meeting extended the spectrum in two very important ways. For the first time, presentations were given by several people who could potentially benefit from current BCI technology-people with severe disabilities who need assistive technology for communication. One presented in person and one remotely. A Virtual BCI User's Forum allowed these presenters and other potential BCI users to speak directly to the BCI research community about the advantages and disadvantages of current BCIs and important directions for future study (see [7]). Their personal experiences and desires can help guide BCI research and development. Their active participation, particularly in regard to the selection of goals and the evaluation and optimization of new methods and systems, is essential if BCIs are to become clinically valuable and widely used technology. The second major innovation in this meeting was the strong emphasis on ethical issues related to BCI development and use. The meeting opened with a keynote presentation entitled 'Neuroethics, BCIs and the Cyborg Myth' by Dr Joseph Fins, a noted authority on neuroethics from the Weill Cornell Medical College and the Rockefeller University. He focused on the ability of BCIs to relieve suffering and restore function, while cautioning against applications that take intentional control away from the user. Ethical issues were also addressed in several of the workshops, and arose on multiple occasions and in multiple contexts over the course of the meeting. Their prominence reflected the growing importance and difficulty of ethical issues as BCI capacities and applications grow and extend to potentially enhancing or supplementing normal nervous system function. The 16 articles in this special section reflect the breadth, depth, growing maturity and future directions of BCI research. The first paper presents a tutorial on best practices in BCI performance measurement [8]. The following eight papers focus on specific BCI applications and on methods for increasing their usefulness for people with severe disabilities. The next two examine how brain activity and BCI use affect each other. The final five studies investigate brain signals and evaluate new signal processing algorithms in order to improve BCI performance and broaden its possible applications in some of the newest areas of BCI research, including the direct interpretation of speech from electrocorticographic (ECoG) activity [9]. Together, these papers span many aspects of BCI research, including different recording modalities (i.e. electroencephalogram (EEG), ECoG, functional magnetic resonance imaging (fMRI)) and signal types (e.g. P300 event-related potentials (ERPs), sensorimotor rhythms, steady-state visual evoked potentials (SSVEPs)). Furthermore, additional clinically related studies that were presented at the meeting but were considered to be outside the scope of the Journal of Neural Engineering will appear in a special issue of the Archives of Physical Medicine and Rehabilitation . With a theme of 'Defining the Future' the Fifth International BCI Meeting tackled the issues of a rapidly growing multidisciplinary research and development enterprise that is now entering clinical use. Important new areas that received attention included the need for active involvement of the people with severe disabilities who are the primary initial users of BCI technology and the growing importance and difficulty of the multiple ethical questions raised by BCIs and their potential applications. The meeting also marked the launching of the new journal Brain--Computer Interfaces , dedicated to BCI research and development, and initiated the establishment of the Brain--Computer Interface Society, which will organize and manage the Sixth International BCI Meeting to be held in 2016. References [1] http://bcimeeting.org/2013/sponsors.html [2] http://bcimeeting.org/2013/meetinginfo.html [3] http://bcimeeting.org/2013/researchsessions.html (indexes individual abstracts) [4] http://bcimeeting.org/2013/posters.html (indexes individual abstracts) [5] http://castor.tugraz.at/doku/BCIMeeting2013/BCIMeeting2013_all.pdf [6] Huggins J E et al 2014 Workshops of the Fifth International Brain--Computer Interface Meeting: Defining the Future Brain--Computer Interface J. 1 27-49 [7] Peters B, Bieker G, Heckman S M, Huggins J E, Wolf C, Zeitlin D and Fried-Oken M 2014 Brain--computer interface users speak up: the Virtual Users' Forum at the 2013 International BCI Meeting Archives of Physical Medicine and Rehabilitation vol 95 fall supplement at press [8] Thompson D E et al 2014 Performance measurement for brain-computer or brain-machine interfaces: a tutorial J. Neural Eng. 11 035001 [9] Mugler E, Patton J, Flint R, Wright Z, Schuele S, Rosenow J, Shih J, Krusienski D and Slutzky M 2014 Direct classification of all American English phonemes using signals from functional speech motor cortex J. Neural Eng. 11 035015

Designing a hands-on brain computer interface laboratory course.

PubMed

Khalighinejad, Bahar; Long, Laura Kathleen; Mesgarani, Nima

2016-08-01

Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students. The course provides an effective introduction to the experimental design, neuroscience concepts, data analysis techniques, and technical skills required in the field of BCI.

Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

PubMed

Mora, Niccolò; De Munari, Ilaria; Ciampolini, Paolo; Del R Millán, José

2017-08-01

Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min -1 , which outperform current literature findings.

A Development Architecture for Serious Games Using BCI (Brain Computer Interface) Sensors

PubMed Central

Sung, Yunsick; Cho, Kyungeun; Um, Kyhyun

2012-01-01

Games that use brainwaves via brain–computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories. PMID:23202227

A development architecture for serious games using BCI (brain computer interface) sensors.

PubMed

Sung, Yunsick; Cho, Kyungeun; Um, Kyhyun

2012-11-12

Games that use brainwaves via brain-computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

NASA Astrophysics Data System (ADS)

Zink, Rob; Hunyadi, Borbála; Van Huffel, Sabine; De Vos, Maarten

2016-04-01

Objective. One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase.

PubMed

Zink, Rob; Hunyadi, Borbála; Huffel, Sabine Van; Vos, Maarten De

2016-04-01

One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

A new brain-computer interface design using fuzzy ARTMAP.

PubMed

Palaniappan, Ramaswamy; Paramesran, Raveendran; Nishida, Shogo; Saiwaki, Naoki

2002-09-01

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.

Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials.

PubMed

Trejo, Leonard J; Rosipal, Roman; Matthews, Bryan

2006-06-01

We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KPLS classifier to map power spectra of 62-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject's average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: 1) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal electrooculograms (EOG) signals, 2) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from 12 electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about 3 min. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 s for movement initiation and turning. We have also developed a realistic demonstration of our system for control of a moving map display (http://ti.arc.nasa.gov/).

Influence of P300 latency jitter on event related potential-based brain-computer interface performance

NASA Astrophysics Data System (ADS)

Aricò, P.; Aloise, F.; Schettini, F.; Salinari, S.; Mattia, D.; Cincotti, F.

2014-06-01

Objective. Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

NASA Astrophysics Data System (ADS)

Ceballos, G. A.; Hernández, L. F.

2015-04-01

Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb.

PubMed

Cincotti, F; Pichiorri, F; Aricò, P; Aloise, F; Leotta, F; de Vico Fallani, F; Millán, J del R; Molinari, M; Mattia, D

2012-01-01

Brain-Computer Interfaces (BCIs) process brain activity in real time, and mediate non-muscular interaction between and individual and the environment. The subserving algorithms can be used to provide a quantitative measurement of physiological or pathological cognitive processes - such as Motor Imagery (MI) - and feed it back the user. In this paper we propose the clinical application of a BCI-based rehabilitation device, to promote motor recovery after stroke. The BCI-based device and the therapy exploiting its use follow the same principles that drive classical neuromotor rehabilitation, and (i) provides the physical therapist with a monitoring instrument, to assess the patient's participation in the rehabilitative cognitive exercise; (ii) assists the patient in the practice of MI. The device was installed in the ward of a rehabilitation hospital and a group of 29 patients were involved in its testing. Among them, eight have already undergone a one-month training with the device, as an add-on to the regular therapy. An improved system, which includes analysis of Electromyographic (EMG) patterns and Functional Electrical Stimulation (FES) of the arm muscles, is also under clinical evaluation. We found that the rehabilitation exercise based on BCI-mediated neurofeedback mechanisms enables a better engagement of motor areas with respect to motor imagery alone and thus it can promote neuroplasticity in brain regions affected by a cerebrovascular accident. Preliminary results also suggest that the functional outcome of motor rehabilitation may be improved by the use of the proposed device.

The Evaluations of Hydrogen Permeation and Life Cycle Assessment on Nanocrystallined TiN-BCY Hydrogen Membrane.

PubMed

Lee, Soo-Sun; Hong, Tae-Whan

2016-02-01

Recently, Membrane technologies are used for the separation of mixtures in various industries. The promising method to reduce the CO2 emission and production of H2 from the coal based power plants is membrane separation with polymer, metal, ceramic and cermet materials. In this study, TiN ceramic material was selected, that is much less expensive than Pd. Also it has resistance to acids and chemically steady. Yttrium doped barium cerate (BCY) is a proton conductor. This perovskite exhibit both high proton conductivity and thermodynamic stability. But its chemical stability is very low under real operating environments. Thus, TiN-BCY may provide'a new membrane material for application. Life cycle assessment (LCA) based on fabrication of membrane and it was carried out to evaluate the energy demand and environmental impact. The analysis is performed according to the recommendations of ISO norms 14040 and obtained using the Gabi 6 software. This LCA will contribute to optimizing the eco-design, reducing the energy consumption and pollutant emissions during the eco-profiles of the TiN-BCY membrane.

Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2014-02-01

Objective. Near-infrared spectroscopy (NIRS) has recently gained attention as a modality for brain-computer interfaces (BCIs), which may serve as an alternative access pathway for individuals with severe motor impairments. For NIRS-BCIs to be used as a real communication pathway, reliable online operation must be achieved. Yet, only a limited number of studies have been conducted online to date. These few studies were carried out under a synchronous paradigm and did not accommodate an unconstrained resting state, precluding their practical clinical implication. Furthermore, the potentially discriminative power of spatiotemporal characteristics of activation has yet to be considered in an online NIRS system. Approach. In this study, we developed and evaluated an online system-paced NIRS-BCI which was driven by a mental arithmetic activation task and accommodated an unconstrained rest state. With a dual-wavelength, frequency domain near-infrared spectrometer, measurements were acquired over nine sites of the prefrontal cortex, while ten able-bodied participants selected letters from an on-screen scanning keyboard via intentionally controlled brain activity (using mental arithmetic). Participants were provided dynamic NIR topograms as continuous visual feedback of their brain activity as well as binary feedback of the BCI's decision (i.e. if the letter was selected or not). To classify the hemodynamic activity, temporal features extracted from the NIRS signals and spatiotemporal features extracted from the dynamic NIR topograms were used in a majority vote combination of multiple linear classifiers. Main results. An overall online classification accuracy of 77.4 ± 10.5% was achieved across all participants. The binary feedback was found to be very useful during BCI use, while not all participants found value in the continuous feedback provided. Significance. These results demonstrate that mental arithmetic is a potent mental task for driving an online system-paced NIRS-BCI. BCI feedback that reflects the classifier's decision has the potential to improve user performance. The proposed system can provide a framework for future online NIRS-BCI development and testing.

The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications.

PubMed

Kübler, Andrea; Holz, Elisa M; Riccio, Angela; Zickler, Claudia; Kaufmann, Tobias; Kleih, Sonja C; Staiger-Sälzer, Pit; Desideri, Lorenzo; Hoogerwerf, Evert-Jan; Mattia, Donatella

2014-01-01

Albeit research on brain-computer interfaces (BCI) for controlling applications has expanded tremendously, we still face a translational gap when bringing BCI to end-users. To bridge this gap, we adapted the user-centered design (UCD) to BCI research and development which implies a shift from focusing on single aspects, such as accuracy and information transfer rate (ITR), to a more holistic user experience. The UCD implements an iterative process between end-users and developers based on a valid evaluation procedure. Within the UCD framework usability of a device can be defined with regard to its effectiveness, efficiency, and satisfaction. We operationalized these aspects to evaluate BCI-controlled applications. Effectiveness was regarded equivalent to accuracy of selections and efficiency to the amount of information transferred per time unit and the effort invested (workload). Satisfaction was assessed with questionnaires and visual-analogue scales. These metrics have been successfully applied to several BCI-controlled applications for communication and entertainment, which were evaluated by end-users with severe motor impairment. Results of four studies, involving a total of N = 19 end-users revealed: effectiveness was moderate to high; efficiency in terms of ITR was low to high and workload low to medium; depending on the match between user and technology, and type of application satisfaction was moderate to high. The here suggested evaluation metrics within the framework of the UCD proved to be an applicable and informative approach to evaluate BCI controlled applications, and end-users with severe impairment and in the locked-in state were able to participate in this process.

The User-Centered Design as Novel Perspective for Evaluating the Usability of BCI-Controlled Applications

PubMed Central

Kübler, Andrea; Holz, Elisa M.; Riccio, Angela; Zickler, Claudia; Kaufmann, Tobias; Kleih, Sonja C.; Staiger-Sälzer, Pit; Desideri, Lorenzo; Hoogerwerf, Evert-Jan; Mattia, Donatella

2014-01-01

Albeit research on brain-computer interfaces (BCI) for controlling applications has expanded tremendously, we still face a translational gap when bringing BCI to end-users. To bridge this gap, we adapted the user-centered design (UCD) to BCI research and development which implies a shift from focusing on single aspects, such as accuracy and information transfer rate (ITR), to a more holistic user experience. The UCD implements an iterative process between end-users and developers based on a valid evaluation procedure. Within the UCD framework usability of a device can be defined with regard to its effectiveness, efficiency, and satisfaction. We operationalized these aspects to evaluate BCI-controlled applications. Effectiveness was regarded equivalent to accuracy of selections and efficiency to the amount of information transferred per time unit and the effort invested (workload). Satisfaction was assessed with questionnaires and visual-analogue scales. These metrics have been successfully applied to several BCI-controlled applications for communication and entertainment, which were evaluated by end-users with severe motor impairment. Results of four studies, involving a total of N = 19 end-users revealed: effectiveness was moderate to high; efficiency in terms of ITR was low to high and workload low to medium; depending on the match between user and technology, and type of application satisfaction was moderate to high. The here suggested evaluation metrics within the framework of the UCD proved to be an applicable and informative approach to evaluate BCI controlled applications, and end-users with severe impairment and in the locked-in state were able to participate in this process. PMID:25469774

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

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

A whole brain atlas with sub-parcellation of cortical gyri using resting fMRI

NASA Astrophysics Data System (ADS)

Joshi, Anand A.; Choi, Soyoung; Sonkar, Gaurav; Chong, Minqi; Gonzalez-Martinez, Jorge; Nair, Dileep; Shattuck, David W.; Damasio, Hanna; Leahy, Richard M.

2017-02-01

The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid) BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the subparcellated hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by sub-parcellation of each subject's resting fMRI data as described above. We then computed consistency between the anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data. The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative measure of reliability of each subdivision to users of the atlas.

Classification of mouth movements using 7 T fMRI.

PubMed

Bleichner, M G; Jansma, J M; Salari, E; Freudenburg, Z V; Raemaekers, M; Ramsey, N F

2015-12-01

A brain-computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements. In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a 'winner-takes-all' design. Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%). The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.

P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier

NASA Astrophysics Data System (ADS)

De Vos, Maarten; Kroesen, Markus; Emkes, Reiner; Debener, Stefan

2014-06-01

Objective. In a previous study, we presented a low-cost, small and wireless EEG system enabling the recording of single-trial P300 amplitudes in a truly mobile, outdoor walking condition (Debener et al (2012 Psychophysiology 49 1449-53)). Small and wireless mobile EEG systems have substantial practical advantages as they allow for brain activity recordings in natural environments, but these systems may compromise the EEG signal quality. In this study, we aim to evaluate the EEG signal quality that can be obtained with the mobile system. Approach. We compared our mobile 14-channel EEG system with a state-of-the-art wired laboratory EEG system in a popular brain-computer interface (BCI) application. N = 13 individuals repeatedly performed a 6 × 6 matrix P300 spelling task. Between conditions, only the amplifier was changed, while electrode placement and electrode preparation, recording conditions, experimental stimulation and signal processing were identical. Main results. Analysis of training and testing accuracies and information transfer rate (ITR) revealed that the wireless mobile EEG amplifier performed as good as the wired laboratory EEG system. A very high correlation for testing ITR between both amplifiers was evident (r = 0.92). Moreover the P300 topographies and amplitudes were very similar for both devices, as reflected by high degrees of association (r > = 0.77). Significance. We conclude that efficient P300 spelling with a small, lightweight and quick to set up mobile EEG amplifier is possible. This technology facilitates the transfer of BCI applications from the laboratory to natural daily life environments, one of the key challenges in current BCI research.

Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke

NASA Astrophysics Data System (ADS)

Johnson, N. N.; Carey, J.; Edelman, B. J.; Doud, A.; Grande, A.; Lakshminarayan, K.; He, B.

2018-02-01

Objective. Combining repetitive transcranial magnetic stimulation (rTMS) with brain-computer interface (BCI) training can address motor impairment after stroke by down-regulating exaggerated inhibition from the contralesional hemisphere and encouraging ipsilesional activation. The objective was to evaluate the efficacy of combined rTMS  +  BCI, compared to sham rTMS  +  BCI, on motor recovery after stroke in subjects with lasting motor paresis. Approach. Three stroke subjects approximately one year post-stroke participated in three weeks of combined rTMS (real or sham) and BCI, followed by three weeks of BCI alone. Behavioral and electrophysiological differences were evaluated at baseline, after three weeks, and after six weeks of treatment. Main results. Motor improvements were observed in both real rTMS  +  BCI and sham groups, but only the former showed significant alterations in inter-hemispheric inhibition in the desired direction and increased relative ipsilesional cortical activation from fMRI. In addition, significant improvements in BCI performance over time and adequate control of the virtual reality BCI paradigm were observed only in the former group. Significance. When combined, the results highlight the feasibility and efficacy of combined rTMS  +  BCI for motor recovery, demonstrated by increased ipsilesional motor activity and improvements in behavioral function for the real rTMS  +  BCI condition in particular. Our findings also demonstrate the utility of BCI training alone, as shown by behavioral improvements for the sham rTMS  +  BCI condition. This study is the first to evaluate combined rTMS and BCI training for motor rehabilitation and provides a foundation for continued work to evaluate the potential of both rTMS and virtual reality BCI training for motor recovery after stroke.

fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

PubMed Central

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Rota, Giuseppina; Kuebler, Andrea; Birbaumer, Niels

2007-01-01

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment. PMID:18274615

P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.

PubMed

McCane, Lynn M; Heckman, Susan M; McFarland, Dennis J; Townsend, George; Mak, Joseph N; Sellers, Eric W; Zeitlin, Debra; Tenteromano, Laura M; Wolpaw, Jonathan R; Vaughan, Theresa M

2015-11-01

Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.