02016nas a2200265   4500008004100000022001400041245013800055210006900193260001200262300001400274490000700288520114400295653003401439653002901473653002501502653002901527653000901556653001901565100002201584700002501606700002601631700002401657700002101681856004801702       2006                        eng d  a0301-051100aA P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.0 aP300 eventrelated potential braincomputer interface BCI the effe  c10/2006  a242–2520 v733 aWe describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.10aAmyotrophic Lateral Sclerosis10abrain-computer interface10aelectroencephalogram10aevent-related potentials10aP30010aRehabilitation1 aSellers, Eric, W.1 aKrusienski, Dean, J.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1686092002522nas a2200277   4500008004100000022001400041245008500055210006900140260001200209300001400221490000700235520169700242653003101939653002701970653001601997653001302013653001402026653002502040653001502065653001902080653002402099100002602123700002602149700002102175856004802196       2003                        eng d  a0301-051100aBrain-computer interface (BCI) operation: optimizing information transfer rates.0 aBraincomputer interface BCI operation optimizing information tra  c07/2003  a237–2510 v633 aPeople can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In the present version of the cursor movement task, vertical cursor movement is a linear function of mu or beta rhythm amplitude. At the same time the cursor moves horizontally from left to right at a fixed rate. A target occupies 50% (2-target task) to 20% (5-target task) of the right edge of the screen. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The goal of the present study was to optimize system performance. To accomplish this, we evaluated the impact on system performance of number of targets (i.e. 2-5) and trial duration (i.e. horizontal movement time from 1 to 4 s). Performance was measured as accuracy (percent of targets selected correctly) and also as bit rate (bits/min) (which incorporates, in addition to accuracy, speed and the number of possible targets). Accuracy declined as target number increased. At the same time, for six of eight users, four targets yielded the maximum bit rate. Accuracy increased as movement time increased. At the same time, the movement time with the highest bit rate varied across users from 2 to 4 s. These results indicate that task parameters such as target number and trial duration can markedly affect system performance. They also indicate that optimal parameter values vary across users. Selection of parameters suited both to the specific user and the requirements of the specific application is likely to be a key factor in maximizing the success of EEG-based communication and control.10aaugmentative communication10aElectroencephalography10ainformation10aLearning10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1285316901629nas 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/1282117802517nas a2200241   4500008004100000022001400041245007400055210006900129260001200198300001600210490000800226520179400234653001302028653002902041653002502070653001902095653001902114100002302133700002602156700002402182700002102206856004802227       2003                        eng d  a1388-245700aEMG contamination of EEG: spectral and topographical characteristics.0 aEMG contamination of EEG spectral and topographical characterist  c09/2003  a1580–15930 v1143 aOBJECTIVE:
Electromyogram (EMG) contamination is often a problem in electroencephalogram (EEG) recording, particularly, for those applications such as EEG-based brain-computer interfaces that rely on automated measurements of EEG features. As an essential prelude to developing methods for recognizing and eliminating EMG contamination of EEG, this study defines the spectral and topographical characteristics of frontalis and temporalis muscle EMG over the entire scalp. It describes both average data and the range of individual differences.
METHODS:
In 25 healthy adults, signals from 64 scalp and 4 facial locations were recorded during relaxation and during defined (15, 30, or 70% of maximum) contractions of frontalis or temporalis muscles.
RESULTS:
In the average data, EMG had a broad frequency distribution from 0 to >200 Hz. Amplitude was greatest at 20-30 Hz frontally and 40-80 Hz temporally. Temporalis spectra also showed a smaller peak around 20 Hz. These spectral components attenuated and broadened centrally. Even with weak (15%) contraction, EMG was detectable (P<0.001) near the vertex at frequencies >12 Hz in the average data and >8 Hz in some individuals.
CONCLUSIONS:
Frontalis or temporalis muscle EMG recorded from the scalp has spectral and topographical features that vary substantially across individuals. EMG spectra often have peaks in the beta frequency range that resemble EEG beta peaks.
SIGNIFICANCE:
While EMG contamination is greatest at the periphery of the scalp near the active muscles, even weak contractions can produce EMG that obscures or mimics EEG alpha, mu, or beta rhythms over the entire scalp. Recognition and elimination of this contamination is likely to require recording from an appropriate set of peripheral scalp locations.10aartifact10abrain-computer interface10aelectroencephalogram10aelectromyogram10aRehabilitation1 aGoncharova, I., I.1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1294878701315nas a2200289   4500008004100000022001400041245006100055210005800116260001200174300001600186490000800202520045900210653003100669653002900700653002700729653002000756653002900776653002800805653001400833653001900847653002400866100001900890700002100909700002600930700002100956856004800977       2000                        eng d  a1388-245700aEEG-based communication: presence of an error potential.0 aEEGbased communication presence of an error potential  c12/2000  a2138–21440 v1113 aEEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.10aaugmentative communication10abrain-computer interface10aElectroencephalography10aerror potential10aerror related negativity10aevent related potential10amu rhythm10aRehabilitation10asensorimotor cortex1 aSchalk, Gerwin1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aPfurtscheller, G  uhttp://www.ncbi.nlm.nih.gov/pubmed/1109076302088nas a2200265   4500008004100000022001400041245006600055210006300121260001200184300001400196490000800210520131500218653003101533653001701564653002701581653002101608653001401629653001901643653002401662100002401686700001801710700002601728700002101754856004701775       1998                        eng d  a0013-469400aEEG-based communication: analysis of concurrent EMG activity.0 aEEGbased communication analysis of concurrent EMG activity  c12/1998  a428–4330 v1073 aOBJECTIVE:
Recent studies indicate that people can learn to control the amplitude of mu or beta rhythms in the EEG recorded from the scalp over sensorimotor cortex and can use that control to move a cursor to targets on the computer screen. While subjects do not move during performance, it is possible that inapparent or unconscious muscle contractions contribute to the changes in the mu and beta rhythm activity responsible for cursor movement. We evaluated this possibility.
METHODS:
EMG was recorded from 10 distal limb muscle groups while five trained subjects used mu or beta rhythms to move a cursor to targets at the bottom or top edge of a computer screen.
RESULTS:
EMG activity was very low during performance, averaging 4.0+/-4.4% (SD) of maximum voluntary contraction. Most important, the correlation, measured as r2, between target position and EMG activity averaged only 0.01+/-0.02, much lower than the correlation between target position and the EEG activity that controlled cursor movement, which averaged 0.39+/-0.18.
CONCLUSIONS:
These results strongly support the conclusion that EEG-based cursor control does no depend on concurrent muscle activity. EEG-based communication and control might provide a new augmentative communication option for those with severe motor disabilities.10aaugmentative communication10aconditioning10aElectroencephalography10aElectromyography10amu rhythm10aRehabilitation10asensorimotor cortex1 aVaughan, Theresa, M1 aMiner, L., A.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/992208902193nas a2200265   4500008004100000022001400041245005800055210005600113260001200169300001400181490000800195520144100203653002801644653002701672653001401699653002501713653001501738653001901753653002401772100002601796700001901822700001801841700002101859856004701880       1997                        eng d  a0013-469400aSpatial filter selection for EEG-based communication.0 aSpatial filter selection for EEGbased communication  c09/1997  a386–3940 v1033 aIndividuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aMcFarland, Dennis, J.1 aMcCane, L., M.1 aDavid, S., V.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/930528702490nas a2200265   4500008004100000022001400041245004000055210003800095260001200133300001400145490000700159520177200166653002801938653002701966653001401993653002502007653001502032653001902047653002402066100002102090700001902111700002102130700002602151856004702177       1997                        eng d  a0736-025800aTiming of EEG-based cursor control.0 aTiming of EEGbased cursor control  c11/1997  a529–5380 v143 aRecent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject's video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into cursor movement.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aWolpaw, Jonathan1 aFlotzinger, D.1 aPfurtscheller, G1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/945806001539nas a2200241   4500008004100000022001400041245005700055210005400112260001200166300001400178490000700192520085200199653002801051653002701079653001401106653002501120653001501145653001901160653002401179100002101203700002601224856004701250       1994                        eng d  a0013-469400aMultichannel EEG-based brain-computer communication.0 aMultichannel EEGbased braincomputer communication  c06/1994  a444–4490 v903 aIndividuals who are paralyzed or have other severe movement disorders often need alternative means for communicating with and controlling their environments. In this study, human subjects learned to use two channels of bipolar EEG activity to control 2-dimensional movement of a cursor on a computer screen. Amplitudes of 8-12 Hz activity in the EEG recorded from the scalp across right and left central sulci were determined by fast Fourier transform and combined to control vertical and horizontal cursor movements simultaneously. This independent control of two separate EEG channels cannot be attributed to a non-specific change in brain activity and appeared to be specific to the mu rhythm frequency range. With further development, multichannel EEG-based communication may prove of significant value to those with severe motor disabilities.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/7515787