TY - JOUR T1 - The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders. JF - J Vis Exp Y1 - 2022 A1 - Hill, N Jeremy A1 - Gupta, Disha A1 - Eftekhar, Amir A1 - Brangaccio, Jodi A A1 - Norton, James J S A1 - McLeod, Michelle A1 - Fake, Tim A1 - Wolpaw, Jonathan R A1 - Thompson, Aiko K KW - Chronic Disease KW - Conditioning, Operant KW - Electromyography KW - Evoked Potentials KW - H-Reflex KW - Humans KW - Neuromuscular Diseases KW - Spinal Cord Injuries AB -

The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles-e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill-and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway's excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value. To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex-the electrically-elicited analog of the spinal stretch reflex-in the soleus muscle. Down-conditioning this pathway's excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.

IS - 186 ER - TY - JOUR T1 - Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis JF - Neurology Y1 - 2018 A1 - Jonathan Wolpaw A1 - Bedlack, RS A1 - Reda, DJ A1 - Ringer, RJ A1 - Banks, PG A1 - Vaughan, TM A1 - Heckman, SM A1 - McCrane, LM A1 - Carmack, CS A1 - Winden, S A1 - McFarland, DJ A1 - Sellers, EW A1 - Shi, H A1 - Paine, T A1 - Higgins, DS A1 - Lo, AC A1 - Patwa, HS A1 - Hill, KJ A1 - Huang, GS A1 - Ruff, RL KW - All clinical neurophysiology KW - All Neuromuscular Disease KW - Evoked Potentials KW - visual AB - Objective: To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months. Methods: Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28–79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life. Results: Over subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use. Conclusion: The Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used. UR - http://n.neurology.org/content/neurology/early/2018/06/27/WNL.0000000000005812.full.pdf ER - TY - JOUR T1 - A graphical model framework for decoding in the visual ERP-based BCI speller. JF - Neural Comput Y1 - 2011 A1 - Martens, S M M A1 - Mooij, J M A1 - Jeremy Jeremy Hill A1 - Farquhar, Jason A1 - Schölkopf, B KW - Artificial Intelligence KW - Computer User Training KW - Discrimination Learning KW - Electroencephalography KW - Evoked Potentials KW - Evoked Potentials, Visual KW - Humans KW - Language KW - Models, Neurological KW - Models, Theoretical KW - Reading KW - Signal Processing, Computer-Assisted KW - User-Computer Interface KW - Visual Cortex KW - Visual Perception AB -

We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.

VL - 23 UR - http://www.ncbi.nlm.nih.gov/pubmed/20964540 IS - 1 ER - TY - JOUR T1 - Nonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex. JF - J Neurosci Y1 - 2011 A1 - Charles M Gaona A1 - Sharma, Mohit A1 - Zachary V. Freudenberg A1 - Breshears, Jonathan A1 - Bundy, David T A1 - Roland, Jarod A1 - Barbour, Dennis L A1 - Gerwin Schalk A1 - Leuthardt, E C KW - Acoustic Stimulation KW - Adolescent KW - Adult KW - Analysis of Variance KW - Brain Mapping KW - Brain Waves KW - Cerebral Cortex KW - Cognition Disorders KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Female KW - Humans KW - Male KW - Middle Aged KW - Neuropsychological Tests KW - Nonlinear Dynamics KW - Photic Stimulation KW - Reaction Time KW - Spectrum Analysis KW - Time Factors KW - Vocabulary AB -

High-gamma-band (>60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60-500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca's area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform trends across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.

VL - 31 UR - http://www.ncbi.nlm.nih.gov/pubmed/21307246 IS - 6 ER - TY - JOUR T1 - Using the electrocorticographic speech network to control a brain-computer interface in humans. JF - J Neural Eng Y1 - 2011 A1 - Leuthardt, E C A1 - Charles M Gaona A1 - Sharma, Mohit A1 - Szrama, Nicholas A1 - Roland, Jarod A1 - Zachary V. Freudenberg A1 - Solisb, Jamie A1 - Breshears, Jonathan A1 - Gerwin Schalk KW - Adult KW - Brain KW - Brain Mapping KW - Computer Peripherals KW - Electroencephalography KW - Evoked Potentials KW - Feedback, Physiological KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Nerve Net KW - Speech Production Measurement KW - User-Computer Interface AB -

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/21471638 IS - 3 ER - TY - JOUR T1 - A procedure for measuring latencies in brain-computer interfaces. JF - IEEE Trans Biomed Eng Y1 - 2010 A1 - Adam J Wilson A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Williams, Justin C KW - Brain KW - Computer Systems KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Models, Neurological KW - Reproducibility of Results KW - Signal Processing, Computer-Assisted KW - Time Factors KW - User-Computer Interface AB -

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 and 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.

VL - 57 UR - http://www.ncbi.nlm.nih.gov/pubmed/20403781 IS - 7 ER - TY - JOUR T1 - Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. JF - J Neurosci Y1 - 2008 A1 - Kipke, Daryl R A1 - Shain, William A1 - Buzsáki, György A1 - Fetz, Eberhard E A1 - Henderson, Jaimie M A1 - Hetke, Jamille F A1 - Gerwin Schalk KW - Cerebral Cortex KW - Electrodes, Implanted KW - Electroencephalography KW - Electronics, Medical KW - Electrophysiology KW - Evoked Potentials KW - Movement Disorders KW - Neurons KW - Prostheses and Implants KW - User-Computer Interface VL - 28 UR - http://www.ncbi.nlm.nih.gov/pubmed/19005048?report=abstract IS - 46 ER - TY - JOUR T1 - Real-time detection of event-related brain activity. JF - Neuroimage Y1 - 2008 A1 - Gerwin Schalk A1 - Leuthardt, E C A1 - Peter Brunner A1 - Ojemann, J G A1 - Lester A Gerhardt A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain Mapping KW - Computer Systems KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Female KW - Humans KW - Male KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity AB -

The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

VL - 43 UR - http://www.ncbi.nlm.nih.gov/pubmed/18718544 IS - 2 ER - TY - JOUR T1 - A µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface. JF - IEEE Trans Biomed Eng Y1 - 2007 A1 - Krusienski, Dean J A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - Algorithms KW - Cerebral Cortex KW - Cortical Synchronization KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Imagination KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the continuousamplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

VL - 54 UR - http://www.ncbi.nlm.nih.gov/pubmed/17278584 IS - 2 ER - TY - JOUR T1 - The BCI competition III: Validating alternative approaches to actual BCI problems. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Krusienski, Dean J A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Pfurtscheller, Gert A1 - Millán, José del R A1 - Schröder, Michael A1 - Niels Birbaumer KW - Algorithms KW - Brain KW - Communication Aids for Disabled KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Software Validation KW - Technology Assessment, Biomedical KW - User-Computer Interface AB -

brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. 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.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792282 IS - 2 ER - TY - JOUR T1 - Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Jeremy Jeremy Hill A1 - Lal, T.N A1 - Schröder, Michael A1 - Hinterberger, T. A1 - Wilhelm, Barbara A1 - Nijboer, F A1 - Mochty, Ursula A1 - Widman, Guido A1 - Elger, Christian A1 - Schölkopf, B A1 - Kübler, A. A1 - Niels Birbaumer KW - Algorithms KW - Artificial Intelligence KW - Cluster Analysis KW - Computer User Training KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Paralysis KW - Pattern Recognition, Automated KW - User-Computer Interface AB -

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792289 IS - 2 ER - TY - JOUR T1 - ECoG factors underlying multimodal control of a brain-computer interface. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Adam J Wilson A1 - Felton, Elizabeth A A1 - Garell, P Charles A1 - Gerwin Schalk A1 - Williams, Justin C KW - Adult KW - Brain Mapping KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Computer Peripherals KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Man-Machine Systems KW - Neuromuscular Diseases KW - Systems Integration KW - User-Computer Interface KW - Volition AB -

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792305 IS - 2 ER - TY - JOUR T1 - Electrocorticography-based brain computer interface--the Seattle experience. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Leuthardt, E C A1 - Miller, John W A1 - Gerwin Schalk A1 - Rao, Rajesh P N A1 - Ojemann, J G KW - Cerebral Cortex KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Humans KW - Therapy, Computer-Assisted KW - User-Computer Interface KW - Washington AB -

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792292 IS - 2 ER - TY - JOUR T1 - The Wadsworth BCI Research and Development Program: At Home with BCI. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Theresa M Vaughan A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Sarnacki, William A A1 - Krusienski, Dean J A1 - Sellers, Eric W A1 - Jonathan Wolpaw KW - Animals KW - Brain KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - New York KW - Research KW - Switzerland KW - Therapy, Computer-Assisted KW - Universities KW - User-Computer Interface AB -

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792301 IS - 2 ER - TY - JOUR T1 - The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, T. A1 - Schröder, Michael A1 - Niels Birbaumer KW - Adult KW - Algorithms KW - Amyotrophic Lateral Sclerosis KW - Artificial Intelligence KW - Brain KW - Cognition KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB - Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 IS - 6 ER - TY - JOUR T1 - BCI2000: a general-purpose brain-computer interface (BCI) system. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Hinterberger, T. A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Cognition KW - Communication Aids for Disabled KW - Computer Peripherals KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Evoked Potentials KW - Humans KW - Systems Integration KW - User-Computer Interface AB - Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups. VL - 51 IS - 6 ER - TY - JOUR T1 - A brain-computer interface using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2004 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Ojemann, J G A1 - Moran, D KW - Adult KW - Brain KW - Communication Aids for Disabled KW - Computer Peripherals KW - Diagnosis, Computer-Assisted KW - Electrodes, Implanted KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Movement Disorders KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/15876624 IS - 2 ER - TY - JOUR T1 - Brain-computer interface technology: a review of the first international meeting. JF - IEEE Trans Rehabil Eng Y1 - 2000 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Heetderks, W J A1 - Dennis J. McFarland A1 - Peckham, P H A1 - Gerwin Schalk A1 - Emanuel Donchin A1 - Quatrano, L A A1 - Robinson, C J A1 - Theresa M Vaughan KW - Algorithms KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Disabled Persons KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/10896178 IS - 2 ER -