02079nas a2200205   4500008004100000022001400041245010100055210006900156260001200225300001400237490000700251520145100258653002901709653002801738653000801766653001001774653002001784100002101804856004801825       2010                        eng d  a1940-102700aBrain-computer interface research comes of age: traditional assumptions meet emerging realities.0 aBraincomputer interface research comes of age traditional assump  c11/2010  a351–3530 v423 aBrain-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.10abrain-computer interface10abrain-machine interface10aEEG10ahuman10aneuroprosthesis1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2118435202444nas a2200325   4500008004100000022001400041245011200055210006900167260001200236300001600248490000800264520147000272653002901742653002801771653000801799653002801807653000901835653001901844100001701863700002001880700002501900700002301925700001701948700001901965700002101984700002402005700002102029700002002050856004802070       2010                        eng d  a1872-895200aA novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.0 anovel P300based braincomputer interface stimulus presentation pa  c07/2010  a1109–11200 v1213 aOBJECTIVE:
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.10abrain-computer interface10abrain-machine interface10aEEG10aevent-related potential10aP30010aRehabilitation1 aTownsend, G.1 aLaPallo, B., K.1 aBoulay, Chadwick, B.1 aKrusienski, D., J.1 aFrye, G., E.1 aHauser, C., K.1 aSchwartz, N., E.1 aVaughan, Theresa, M1 aWolpaw, Jonathan1 aSellers, E., W.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2034738702473nas a2200289   4500008004100000022001400041245007500055210006900130260001200199300001600211490000800227520163800235653002901873653002801902653000801930653002801938653000901966653001901975100002101994700002402015700001902039700002102058700002102079700001502100700002002115856004802135       2009                        eng d  a1872-895200aToward a high-throughput auditory P300-based brain-computer interface.0 aToward a highthroughput auditory P300based braincomputer interfa  c07/2009  a1252–12610 v1203 aOBJECTIVE:
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.10abrain-computer interface10abrain-machine interface10aEEG10aevent-related potential10aP30010aRehabilitation1 aKlobassa, D., S.1 aVaughan, Theresa, M1 aBrunner, Peter1 aSchwartz, N., E.1 aWolpaw, Jonathan1 aNeuper, C.1 aSellers, E., W.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1957409101471nas a2200325   4500008004100000022001400041245006200055210005900117260001200176300001400188490000600202520059000208653000800798653002800806653000800834653000800842653002600850653002900876653002800905653000800933653000800941653002300949653002800972653001001000653001101010100002501021700003001046700002101076856004801097       2007                        eng d  a1743-444000aBrain-computer interface systems: progress and prospects.0 aBraincomputer interface systems progress and prospects  c07/2007  a463–4740 v43 aBrain-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.10aALS10aassistive communication10aBCI10aBMI10abrain-acuated control10abrain-computer interface10abrain-machine interface10aEEG10aERP10alocked-in syndrome10aslow cortical potential10aSSVEP10aStroke1 aAllison, Brendan, Z.1 aWolpaw, Elizabeth, Winter1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1760568201886nas a2200181   4500008004100000022001400041245010200055210006900157260001200226300001800238490000800256520129000264653002801554653002701582100002101609700002601630856004801656       2004                        eng d  a0027-842400aControl of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.0 aControl of a twodimensional movement signal by a noninvasive bra  c12/2004  a17849–178540 v1013 aBrain-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.10abrain-machine interface10aElectroencephalography1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1558558401629nas a2200265   4500008004100000022001400041245010300055210006900158260001200227300001200239490000800251520078600259653003101045653002901076653002801105653002701133653002401160653002001184653001901204100001901223700002601242700002601268700002101294856004801315       2003                        eng d  a0304-394000aElectroencephalographic(EEG)-based communication: EEG control versus system performance in humans.0 aElectroencephalographicEEGbased communication EEG control versus  c07/2002  a89–920 v3453 aPeople 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.10aaugmentative communication10abrain-computer interface10abrain-machine interface10aElectroencephalography10amu and beta rhythms10aneuroprosthesis10aRehabilitation1 aSheikh, Hesham1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/12821178