%0 Journal Article %J Journal of neural engineering %D 2012 %T EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis. %A Mak, Joseph N. %A Dennis J. McFarland %A Theresa M Vaughan %A McCane, Lynn M. %A Tsui, Phillippa Z. %A Zeitlin, Debra J. %A Sellers, Eric W. %A Jonathan Wolpaw %K User-Computer Interface %X 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 R(2)?= 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. %B Journal of neural engineering %V 9 %P 026014 %8 04/2012 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/22350501 %R 10.1088/1741-2560/9/2/026014 %0 Journal Article %J Brain Res Bull %D 2012 %T Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface. %A Krusienski, Dean J %A Dennis J. McFarland %A Jonathan Wolpaw %K Algorithms %K Brain %K Electroencephalography %K Humans %K Motor Cortex %K User-Computer Interface %X 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. %B Brain Res Bull %V 87 %P 130-4 %8 01/2012 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21985984 %N 1 %R 10.1016/j.brainresbull.2011.09.019 %0 Journal Article %J Journal of neural engineering %D 2010 %T Electroencephalographic (EEG) control of three-dimensional movement. %A Dennis J. McFarland %A Sarnacki, William A. %A Jonathan Wolpaw %K User-Computer Interface %X Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses. %B Journal of neural engineering %V 7 %P 036007 %8 06/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20460690 %R 10.1088/1741-2560/7/3/036007 %0 Journal Article %J Journal of neural engineering %D 2008 %T Emulation of computer mouse control with a noninvasive brain-computer interface. %A Dennis J. McFarland %A Krusienski, Dean J. %A Sarnacki, William A. %A Jonathan Wolpaw %K User-Computer Interface %X Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection. %B Journal of neural engineering %V 5 %P 101–110 %8 06/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18367779 %R 10.1088/1741-2560/5/2/001 %0 Journal Article %J Journal of neural engineering %D 2008 %T Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis. %A Dennis J. McFarland %A Jonathan Wolpaw %K User-Computer Interface %X People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance. %B Journal of neural engineering %V 5 %P 155–162 %8 06/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18430974 %R 10.1088/1741-2560/5/2/006 %0 Journal Article %J Clin Neurophysiol %D 2008 %T Towards an independent brain-computer interface using steady state visual evoked potentials. %A Brendan Z. Allison %A Dennis J. McFarland %A Gerwin Schalk %A Zheng, Shi Dong %A Moore-Jackson, Melody %A Jonathan Wolpaw %K Adolescent %K Adult %K Attention %K Brain %K Brain Mapping %K Dose-Response Relationship, Radiation %K Electroencephalography %K Evoked Potentials, Visual %K Female %K Humans %K Male %K Pattern Recognition, Visual %K Photic Stimulation %K Spectrum Analysis %K User-Computer Interface %X

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.

%B Clin Neurophysiol %V 119 %P 399-408 %8 02/2008 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/18077208 %N 2 %R 10.1016/j.clinph.2007.09.121 %0 Journal Article %J IEEE Trans Biomed Eng %D 2007 %T A µ-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface. %A Krusienski, Dean J %A Gerwin Schalk %A Dennis J. McFarland %A Jonathan Wolpaw %K Algorithms %K Cerebral Cortex %K Cortical Synchronization %K Electroencephalography %K Evoked Potentials %K Humans %K Imagination %K Pattern Recognition, Automated %K User-Computer Interface %X

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.

%B IEEE Trans Biomed Eng %V 54 %P 273-80 %8 02/2007 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17278584 %N 2 %R 10.1109/TBME.2006.886661 %0 Journal Article %J Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference %D 2006 %T An evaluation of autoregressive spectral estimation model order for brain-computer interface applications. %A Krusienski, D. J. %A Dennis J. McFarland %A Jonathan Wolpaw %K User-Computer Interface %X Autoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not necessarily equate to the optimal AR model order for sensorimotor rhythm (SMR)-based BCI control applications. The present study confirms this by evaluating the EEG spectra of data obtained during control of SMR-BCI using different AR model orders and model evaluation criteria. The results indicate that the AR model order that optimizes SMR-BCI control performance is generally higher than the model orders that are frequently used in SMR-BCI studies. %B Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference %V 1 %P 1323–1326 %8 09/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17946038 %R 10.1109/IEMBS.2006.259822 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2006 %T The Wadsworth BCI Research and Development Program: At Home with BCI. %A Theresa M Vaughan %A Dennis J. McFarland %A Gerwin Schalk %A Sarnacki, William A %A Krusienski, Dean J %A Sellers, Eric W %A Jonathan Wolpaw %K Animals %K Brain %K Electroencephalography %K Evoked Potentials %K Humans %K Neuromuscular Diseases %K New York %K Research %K Switzerland %K Therapy, Computer-Assisted %K Universities %K User-Computer Interface %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 14 %P 229-33 %8 06/2006 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/16792301 %N 2 %R 10.1109/TNSRE.2006.875577 %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F %A Mellinger, Jürgen %A Theresa M Vaughan %A Pawelzik, H %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K Aged %K Amyotrophic Lateral Sclerosis %K Electroencephalography %K Evoked Potentials, Motor %K Evoked Potentials, Somatosensory %K Female %K Humans %K Imagination %K Male %K Middle Aged %K Motor Cortex %K Movement %K Paralysis %K Photic Stimulation %K Prostheses and Implants %K Somatosensory Cortex %K Treatment Outcome %K User-Computer Interface %X

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.

%B Neurology %V 64 %P 1775-7 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %N 10 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J Neurology %D 2005 %T Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. %A Kübler, A. %A Nijboer, F. %A Mellinger, J. %A Theresa M Vaughan %A Pawelzik, H. %A Gerwin Schalk %A Dennis J. McFarland %A Niels Birbaumer %A Jonathan Wolpaw %K User-Computer Interface %X 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. %B Neurology %V 64 %P 1775–1777 %8 05/2005 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15911809 %R 10.1212/01.WNL.0000158616.43002.6D %0 Journal Article %J IEEE Trans Biomed Eng %D 2004 %T BCI2000: a general-purpose brain-computer interface (BCI) system. %A Gerwin Schalk %A Dennis J. McFarland %A Hinterberger, T. %A Niels Birbaumer %A Jonathan Wolpaw %K Algorithms %K Brain %K Cognition %K Communication Aids for Disabled %K Computer Peripherals %K Electroencephalography %K Equipment Design %K Equipment Failure Analysis %K Evoked Potentials %K Humans %K Systems Integration %K User-Computer Interface %X 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. %B IEEE Trans Biomed Eng %V 51 %P 1034-43 %8 06/2004 %G eng %N 6 %R 10.1109/TBME.2004.827072 %0 Journal Article %J IEEE transactions on bio-medical engineering %D 2004 %T BCI2000: a general-purpose brain-computer interface (BCI) system. %A Gerwin Schalk %A Dennis J. McFarland %A Hinterberger, Thilo %A Niels Birbaumer %A Jonathan Wolpaw %K User-Computer Interface %X 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. %B IEEE transactions on bio-medical engineering %V 51 %P 1034–1043 %8 06/2004 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15188875 %R 10.1109/TBME.2004.827072 %0 Journal Article %J IEEE Trans Neural Syst Rehabil Eng %D 2003 %T The Wadsworth Center brain-computer interface (BCI) research and development program. %A Jonathan Wolpaw %A Dennis J. McFarland %A Theresa M Vaughan %A Gerwin Schalk %K Academic Medical Centers %K Adult %K Algorithms %K Artifacts %K Brain %K Brain Mapping %K Electroencephalography %K Evoked Potentials, Visual %K Feedback %K Humans %K Middle Aged %K Nervous System Diseases %K Research %K Research Design %K User-Computer Interface %K Visual Perception %X

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.

%B IEEE Trans Neural Syst Rehabil Eng %V 11 %P 204-7 %8 06/2003 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/12899275 %N 2 %R 10.1109/TNSRE.2003.814442 %0 Journal Article %J Clin Neurophysiol %D 2002 %T Brain-computer interfaces for communication and control. %A Jonathan Wolpaw %A Niels Birbaumer %A Dennis J. McFarland %A Pfurtscheller, Gert %A Theresa M Vaughan %K Brain Diseases %K Communication Aids for Disabled %K Computer Systems %K Electroencephalography %K Humans %K User-Computer Interface %X

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.

%B Clin Neurophysiol %V 113 %P 767-91 %8 06/2002 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/12048038 %N 6 %R 10.1016/S1388-2457(02)00057-3 %0 Journal Article %J IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society %D 2000 %T Brain-computer interface research at the Wadsworth Center. %A Jonathan Wolpaw %A Dennis J. McFarland %A Theresa M Vaughan %K User-Computer Interface %X Studies at the Wadsworth Center over the past 14 years have shown that people with or without motor disabilities can learn to control the amplitude of mu or beta rhythms in electroencephalographic (EEG) activity recorded from the scalp over sensorimotor cortex and can use that control to move a cursor on a computer screen in one or two dimensions. This EEG-based brain-computer interface (BCI) could provide a new augmentative communication technology for those who are totally paralyzed or have other severe motor impairments. Present research focuses on improving the speed and accuracy of BCI communication. %B IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society %V 8 %P 222–226 %8 06/2000 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/10896194 %R 10.1109/86.847823 %0 Journal Article %J IEEE Trans Rehabil Eng %D 2000 %T Brain-computer interface technology: a review of the first international meeting. %A Jonathan Wolpaw %A Niels Birbaumer %A Heetderks, W J %A Dennis J. McFarland %A Peckham, P H %A Gerwin Schalk %A Emanuel Donchin %A Quatrano, L A %A Robinson, C J %A Theresa M Vaughan %K Algorithms %K Cerebral Cortex %K Communication Aids for Disabled %K Disabled Persons %K Electroencephalography %K Evoked Potentials %K Humans %K Neuromuscular Diseases %K Signal Processing, Computer-Assisted %K User-Computer Interface %X

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.

%B IEEE Trans Rehabil Eng %V 8 %P 164-73 %8 06/2000 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/10896178 %N 2 %R 10.1109/TRE.2000.847807 %0 Journal Article %J Archives of physical medicine and rehabilitation %D 1998 %T Answering questions with an electroencephalogram-based brain-computer interface. %A Miner, L. A. %A Dennis J. McFarland %A Jonathan Wolpaw %K User-Computer Interface %X OBJECTIVE: To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions. METHODS: Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again. RESULTS: For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly. CONCLUSIONS: The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities. %B Archives of physical medicine and rehabilitation %V 79 %P 1029–1033 %8 09/1998 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/9749678 %R 10.1016/S0003-9993(98)90165-4 %0 Journal Article %J Medical progress through technology %D 1995 %T EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components. %A Pfurtscheller, G. %A Flotzinger, D. %A Pregenzer, M. %A Jonathan Wolpaw %A Dennis J. McFarland %K User-Computer Interface %X Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control. %B Medical progress through technology %V 21 %P 111–121 %8 1996 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/8776708