TY - JOUR T1 - Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. JF - Amyotroph Lateral Scler Frontotemporal Degener Y1 - 2014 A1 - McCane, Lynn M A1 - Sellers, Eric W A1 - Dennis J. McFarland A1 - Mak, Joseph N A1 - Carmack, C Steve A1 - Zeitlin, Debra A1 - Jonathan Wolpaw A1 - Theresa M Vaughan KW - Adult KW - Aged KW - Amyotrophic Lateral Sclerosis KW - Biofeedback, Psychology KW - brain-computer interfaces KW - Communication Disorders KW - Electroencephalography KW - Event-Related Potentials, P300 KW - Female KW - Humans KW - Male KW - Middle Aged KW - Online Systems KW - Photic Stimulation KW - Psychomotor Performance KW - Reaction Time AB - 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. VL - 15 UR - http://www.ncbi.nlm.nih.gov/pubmed/24555843 IS - 3-4 ER - TY - JOUR T1 - A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening. JF - J Neural Eng Y1 - 2014 A1 - Jeremy Jeremy Hill A1 - Ricci, Erin A1 - Haider, Sameah A1 - McCane, Lynn M A1 - Susan M Heckman A1 - Jonathan Wolpaw A1 - Theresa M Vaughan KW - Adult KW - Aged KW - Algorithms KW - Auditory Perception KW - brain-computer interfaces KW - Communication Aids for Disabled KW - Electroencephalography KW - Equipment Design KW - Equipment Failure Analysis KW - Female KW - Humans KW - Male KW - Man-Machine Systems KW - Middle Aged KW - Quadriplegia KW - Treatment Outcome KW - User-Computer Interface AB - 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. VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/24838278 IS - 3 ER - TY - JOUR T1 - The simplest motor skill: mechanisms and applications of reflex operant conditioning. JF - Exerc Sport Sci Rev Y1 - 2014 A1 - Thompson, Aiko K A1 - Jonathan Wolpaw KW - Animals KW - Conditioning, Operant KW - H-Reflex KW - Humans KW - Motor Skills KW - Muscle, Skeletal KW - Neuronal Plasticity KW - Reflex KW - Spinal Cord KW - Spinal Cord Injuries AB - Operant conditioning protocols can change spinal reflexes gradually, which are the simplest behaviors. This article summarizes the evidence supporting two propositions: that these protocols provide excellent models for defining the substrates of learning and that they can induce and guide plasticity to help restore skills, such as locomotion, that have been impaired by spinal cord injury or other disorders. VL - 42 UR - http://www.ncbi.nlm.nih.gov/pubmed/24508738 IS - 2 ER - TY - JOUR T1 - Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. JF - Brain Res Bull Y1 - 2012 A1 - Krusienski, Dean J A1 - Dennis J. McFarland A1 - Jonathan Wolpaw KW - Algorithms KW - Brain KW - Electroencephalography KW - Humans KW - Motor Cortex KW - User-Computer Interface AB - Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source. VL - 87 UR - http://www.ncbi.nlm.nih.gov/pubmed/21985984 IS - 1 ER - TY - JOUR T1 - Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition. JF - Neuroimage Y1 - 2011 A1 - Pei, Xiao-Mei A1 - Leuthardt, E C A1 - Charles M Gaona A1 - Peter Brunner A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Female KW - Humans KW - Male KW - Middle Aged KW - Signal Processing, Computer-Assisted KW - Verbal Behavior AB -

Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the brain combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to current understanding of word processing in humans.

VL - 54 UR - http://www.ncbi.nlm.nih.gov/pubmed/21029784 IS - 4 ER - TY - JOUR T1 - Does the 'P300' speller depend on eye gaze?. JF - J Neural Eng Y1 - 2010 A1 - Peter Brunner A1 - Joshi, S A1 - S Briskin A1 - Jonathan Wolpaw A1 - H Bischof A1 - Gerwin Schalk KW - Adult KW - Event-Related Potentials, P300 KW - Eye Movements KW - Female KW - Humans KW - Male KW - Middle Aged KW - Models, Neurological KW - Photic Stimulation KW - User-Computer Interface KW - Young Adult AB -

Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.

VL - 7 UR - http://www.ncbi.nlm.nih.gov/pubmed/20858924 IS - 5 ER - TY - JOUR T1 - Decoding flexion of individual fingers using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2009 A1 - Kubánek, J A1 - Miller, John W A1 - Ojemann, J G A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Biomechanics KW - Brain KW - Electrodiagnosis KW - Epilepsy KW - Female KW - Fingers KW - Humans KW - Male KW - Microelectrodes KW - Middle Aged KW - Motor Activity KW - Rest KW - Thumb KW - Time Factors KW - Young Adult AB -

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

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19794237 IS - 6 ER - TY - JOUR T1 - Brain-computer interfaces (BCIs): Detection Instead of Classification. JF - J Neurosci Methods Y1 - 2008 A1 - Gerwin Schalk A1 - Peter Brunner A1 - Lester A Gerhardt A1 - H Bischof A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain KW - Brain Mapping KW - Electrocardiography KW - Electroencephalography KW - Humans KW - Male KW - Man-Machine Systems KW - Normal Distribution KW - Online Systems KW - Signal Detection, Psychological KW - Signal Processing, Computer-Assisted KW - Software Validation KW - User-Computer Interface AB -

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

VL - 167 UR - http://www.ncbi.nlm.nih.gov/pubmed/17920134 IS - 1 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 - Towards an independent brain-computer interface using steady state visual evoked potentials. JF - Clin Neurophysiol Y1 - 2008 A1 - Brendan Z. Allison A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Zheng, Shi Dong A1 - Moore-Jackson, Melody A1 - Jonathan Wolpaw KW - Adolescent KW - Adult KW - Attention KW - Brain KW - Brain Mapping KW - Dose-Response Relationship, Radiation KW - Electroencephalography KW - Evoked Potentials, Visual KW - Female KW - Humans KW - Male KW - Pattern Recognition, Visual KW - Photic Stimulation KW - Spectrum Analysis KW - User-Computer Interface AB -

OBJECTIVE: 

Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.

METHODS: 

Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.

RESULTS: 

Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.

CONCLUSIONS: 

The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.

SIGNIFICANCE: 

SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

VL - 119 UR - http://www.ncbi.nlm.nih.gov/pubmed/18077208 IS - 2 ER - TY - JOUR T1 - Two-dimensional movement control using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2008 A1 - Gerwin Schalk A1 - Miller, K.J. A1 - Nicholas R Anderson A1 - Adam J Wilson A1 - Smyth, Matt A1 - Ojemann, J G A1 - Moran, D A1 - Jonathan Wolpaw A1 - Leuthardt, E C KW - Adolescent KW - Adult KW - Brain Mapping KW - Data Interpretation, Statistical KW - Drug Resistance KW - Electrocardiography KW - Electrodes, Implanted KW - Electroencephalography KW - Epilepsy KW - Female KW - Humans KW - Male KW - Movement KW - User-Computer Interface AB -

We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.

VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18310813 IS - 1 ER - TY - JOUR T1 - Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2007 A1 - Gerwin Schalk A1 - Kubánek, J A1 - Miller, John W A1 - Nicholas R Anderson A1 - Leuthardt, E C A1 - Ojemann, J G A1 - Limbrick, D A1 - Moran, D A1 - Lester A Gerhardt A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Arm KW - Brain Mapping KW - Cerebral Cortex KW - Electroencephalography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Male KW - Movement AB -

Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.

VL - 4 UR - http://www.ncbi.nlm.nih.gov/pubmed/17873429 IS - 3 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 - 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 - Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. JF - Neurology Y1 - 2005 A1 - Kübler, A. A1 - Nijboer, F A1 - Mellinger, Jürgen A1 - Theresa M Vaughan A1 - Pawelzik, H A1 - Gerwin Schalk A1 - Dennis J. McFarland A1 - Niels Birbaumer A1 - Jonathan Wolpaw KW - Aged KW - Amyotrophic Lateral Sclerosis KW - Electroencephalography KW - Evoked Potentials, Motor KW - Evoked Potentials, Somatosensory KW - Female KW - Humans KW - Imagination KW - Male KW - Middle Aged KW - Motor Cortex KW - Movement KW - Paralysis KW - Photic Stimulation KW - Prostheses and Implants KW - Somatosensory Cortex KW - Treatment Outcome KW - User-Computer Interface AB -

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.

VL - 64 UR - http://www.ncbi.nlm.nih.gov/pubmed/15911809 IS - 10 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 - The Wadsworth Center brain-computer interface (BCI) research and development program. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2003 A1 - Jonathan Wolpaw A1 - Dennis J. McFarland A1 - Theresa M Vaughan A1 - Gerwin Schalk KW - Academic Medical Centers KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Brain Mapping KW - Electroencephalography KW - Evoked Potentials, Visual KW - Feedback KW - Humans KW - Middle Aged KW - Nervous System Diseases KW - Research KW - Research Design KW - User-Computer Interface KW - Visual Perception AB -

Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.

VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/12899275 IS - 2 ER - TY - JOUR T1 - Brain-computer interfaces for communication and control. JF - Clin Neurophysiol Y1 - 2002 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Dennis J. McFarland A1 - Pfurtscheller, Gert A1 - Theresa M Vaughan KW - Brain Diseases KW - Communication Aids for Disabled KW - Computer Systems KW - Electroencephalography KW - Humans KW - User-Computer Interface AB -

For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and controltechnology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.

VL - 113 UR - http://www.ncbi.nlm.nih.gov/pubmed/12048038 IS - 6 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 - TY - JOUR T1 - The influence of stimulus intensity, contralateral masking and handedness on the temporal N1 and the T complex components of the auditory N1 wave, by John F. Connolly. JF - Electroencephalography and clinical neurophysiology Y1 - 1994 A1 - Jonathan Wolpaw A1 - Anthony T. Cacace KW - Humans VL - 91 UR - http://www.ncbi.nlm.nih.gov/pubmed/7517847 ER - TY - JOUR T1 - Late auditory evoked potentials can occur without brain stem potentials. JF - Electroencephalography and clinical neurophysiology Y1 - 1983 A1 - Satya-Murti, S. A1 - Jonathan Wolpaw A1 - Anthony T. Cacace A1 - Schaffer, C. A. KW - Humans AB - The sequence of early, middle and late auditory evoked potentials is well known. However, it is unknown whether the late (60-250 msec) potentials can occur independently of the early, brain stem potentials. Therefore, in 6 subjects with markedly abnormal or absent brain stem potentials, we recorded two of the late potentials: the vertex potential and the T-complex. The latter is a putative product of auditory cortex. Both of these potentials were clearly evident in all patients in spite of the absence of or marked abnormalities in brain stem potentials. VL - 56 UR - http://www.ncbi.nlm.nih.gov/pubmed/6193943 ER -