%0 Journal Article %J Journal of motor behavior %D 2010 %T Brain-computer interface research comes of age: traditional assumptions meet emerging realities. %A Jonathan Wolpaw %K brain-computer interface %K brain-machine interface %K EEG %K human %K neuroprosthesis %X Brain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions. %B Journal of motor behavior %V 42 %P 351–353 %8 11/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21184352 %R 10.1080/00222895.2010.526471 %0 Journal Article %J Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology %D 2010 %T A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns. %A Townsend, G. %A LaPallo, B. K. %A Chadwick B. Boulay %A Krusienski, D. J. %A Frye, G. E. %A Hauser, C. K. %A Schwartz, N. E. %A Theresa M Vaughan %A Jonathan Wolpaw %A Sellers, E. W. %K brain-computer interface %K brain-machine interface %K EEG %K event-related potential %K P300 %K Rehabilitation %X OBJECTIVE: An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). METHODS: Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. RESULTS: Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. CONCLUSIONS: These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. SIGNIFICANCE: The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities. %B Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology %V 121 %P 1109–1120 %8 07/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/20347387 %R 10.1016/j.clinph.2010.01.030 %0 Journal Article %J Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology %D 2009 %T Toward a high-throughput auditory P300-based brain-computer interface. %A Klobassa, D. S. %A Theresa M Vaughan %A Peter Brunner %A Schwartz, N. E. %A Jonathan Wolpaw %A Neuper, C. %A Sellers, E. W. %K brain-computer interface %K brain-machine interface %K EEG %K event-related potential %K P300 %K Rehabilitation %X OBJECTIVE: Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6x6 P300 Speller. METHODS: A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N=5) received only auditory stimuli during all 11 sessions, whereas Group AV (N=5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice. RESULTS: Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50% or more, and four of these achieved 75% or more, online accuracy (2.8% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min. CONCLUSIONS: This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility. SIGNIFICANCE: With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI. %B Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology %V 120 %P 1252–1261 %8 07/2009 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/19574091 %R 10.1016/j.clinph.2009.04.019 %0 Journal Article %J Expert review of medical devices %D 2007 %T Brain-computer interface systems: progress and prospects. %A Brendan Z. Allison %A Wolpaw, Elizabeth Winter %A Jonathan Wolpaw %K ALS %K assistive communication %K BCI %K BMI %K brain-acuated control %K brain-computer interface %K brain-machine interface %K EEG %K ERP %K locked-in syndrome %K slow cortical potential %K SSVEP %K Stroke %X Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated. %B Expert review of medical devices %V 4 %P 463–474 %8 07/2007 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/17605682 %R 10.1586/17434440.4.4.463 %0 Journal Article %J Proceedings of the National Academy of Sciences of the United States of America %D 2004 %T Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. %A Jonathan Wolpaw %A Dennis J. McFarland %K brain-machine interface %K Electroencephalography %X Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains. %B Proceedings of the National Academy of Sciences of the United States of America %V 101 %P 17849–17854 %8 12/2004 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15585584 %R 10.1073/pnas.0403504101 %0 Journal Article %J Neuroscience letters %D 2003 %T Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans. %A Sheikh, Hesham %A Dennis J. McFarland %A Sarnacki, William A. %A Jonathan Wolpaw %K augmentative communication %K brain-computer interface %K brain-machine interface %K Electroencephalography %K mu and beta rhythms %K neuroprosthesis %K Rehabilitation %X People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement. %B Neuroscience letters %V 345 %P 89–92 %8 07/2002 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/12821178 %R 10.1016/S0304-3940(03)00470-1