01319nas a2200205   4500008004100000022001400041245008200055210006900137260001200206300001400218490000800232520070000240653001500940653002900955653000800984100002600992700002601018700002101044856004801065       2011                        eng d  a1872-678X00aShould the parameters of a BCI translation algorithm be continually adapted?.0 aShould the parameters of a BCI translation algorithm be continua  c07/2011  a103–1070 v1993 aPeople with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.10aadaptation10abrain-computer interface10aEEG1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2157100401826nas a2200217   4500008004100000022001400041245007300055210006900128260001200197300001600209490000800225520117900233653002901412653000801441653001801449100002501467700002101492700002101513700002601534856004801560       2011                        eng d  a1872-895200aTrained modulation of sensorimotor rhythms can affect reaction time.0 aTrained modulation of sensorimotor rhythms can affect reaction t  c09/2011  a1820–18260 v1223 aOBJECTIVE:
Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects.
METHODS:
Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task.
RESULTS:
Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms.
CONCLUSIONS:
Voluntary modulation of EEG SMR can affect motor behavior.
SIGNIFICANCE:
These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.10abrain-computer interface10aEEG10aReaction Time1 aBoulay, Chadwick, B.1 aSarnacki, W., A.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/2141136602112nas a2200289   4500008004100000022001400041245004800055210004100103260001200144300001200156490000800168520133600176653002201512653002901534653000801563653002001571653001501591653002401606100001901630700001901649700001601668700002301684700002601707700002101733700002001754856004801774       2008                        eng d  a0165-027000aAn auditory brain-computer interface (BCI).0 aauditory braincomputer interface BCI  c01/2008  a43–500 v1673 aBrain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.10aauditory feedback10abrain-computer interface10aEEG10alocked-in state10amotivation10asensorimotor rhythm1 aNijboer, Femke1 aFurdea, Adrian1 aGunst, Ingo1 aMellinger, Jürgen1 aMcFarland, Dennis, J.1 aBirbaumer, Niels1 aKübler, Andrea  uhttp://www.ncbi.nlm.nih.gov/pubmed/1739979702437nas a2200241   4500008004100000022001400041245006700055210006500122260001200187300001400199490000700213520176200220653000801982653000801990653002001998653001902018100002402037700002602061700001802087700001802105700002402123856004802147       2008                        eng d  a0301-051100aElectrophysiological markers of skill-related neuroplasticity.0 aElectrophysiological markers of skillrelated neuroplasticity  c07/2008  a221–2300 v783 aNeuroplasticity involved in acquiring a new cognitive skill was investigated with standard time domain event-related potentials (ERPs) of scalp-recorded electroencephalographic (EEG) activity and frequency domain analysis of EEG oscillations looking at the event-related synchronization (ERS) and desynchronization (ERD) of neural activity. Electroencephalographic activity was recorded before and after practice, while participants performed alphabet addition (i.e., E+3=G, true or false?). Participant's performance became automated with practice through a switch in cognitive strategy from mentally counting-up in the alphabet to retrieving the answer from memory. Time domain analysis of the ERPs revealed a prominent positive peak at approximately 300 ms that was not reactive to problem attributes but was reduced with practice. A second prominent positive peak observed at approximately 500 ms was found to be larger after practice, mainly for problems presented with correct answers. Frequency domain spectral analyses yielded two distinct findings: (1) a frontal midline ERS of theta activity that was greater after practice, and (2) a beta band ERD that increased with problem difficulty before, but not after practice. Because the EEG oscillations were not phase locked to the stimulus, they were viewed as being independent of the time domain results. Consequently, use of time and frequency domain analyses provides a more comprehensive account of the underlying electrophysiological data than either method alone. When used in combination with a well-defined cognitive/behavioral paradigm, this approach serves to constrain the interpretations of EEG data and sets a new standard for studying the neuroplasticity involved in skill acquisition.10aEEG10aERP10aneuroplasticity10askill learning1 aRomero, Stephen, G.1 aMcFarland, Dennis, J.1 aFaust, Robert1 aFarrell, Lori1 aCacace, Anthony, T.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1845586103537nas a2200253   4500008004100000022001400041245009500055210006900150260001200219300001200231490000800243520277200251653002903023653000803052653002703060653001303087653001403100653002403114100002603138700002603164700002403190700002103214856004803235       2005                        eng d  a1388-245700aBrain-computer interface (BCI) operation: signal and noise during early training sessions.0 aBraincomputer interface BCI operation signal and noise during ea  c01/2005  a56–620 v1163 aOBJECTIVE:
People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users' initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target.
METHODS:
In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users.
RESULTS:
Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10.
CONCLUSIONS:
EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination.
SIGNIFICANCE:
Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.10abrain-computer interface10aEEG10aElectroencephalography10aLearning10amu rhythm10asensorimotor cortex1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1558918401351nas a2200409   4500008004100000020001800041245007500059210006900134260003300203653002700236653003000263653001900293653002900312653002500341653003300366653003400399653000800433653002700441653003900468653002100507653001700528653002000545653002900565653003000594653001500624653001800639653002300657653002600680653001100706653002300717653004000740100002400780700001900804700002600823700002100849856007100870       2005                        eng d  a0-7803-8710-400aTracking of the mu rhythm using an empirically derived matched filter.0 aTracking of the mu rhythm using an empirically derived matched f  aArlington, VAbIEEEc03/200510abioelectric potentials10aBrain Computer Interfaces10abrain modeling10abrain-computer interface10acommunication device10acommunication system control10acortical mu rhythm modulation10aEEG10aElectroencephalography10aempirically derived matched filter10ahandicapped aids10alaboratories10amatched filters10amedical signal detection10amedical signal processing10amonitoring10amotor imagery10amu rhythm tracking10anoninvasive treatment10arhythm10asynchronous motors10atwo-dimensional cursor control data1 aKrusienski, Dean, J1 aSchalk, Gerwin1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=141955902265nas a2200241   4500008004100000022001400041245007900055210006900134260001200203300001400215490000700229520157600236653001601812653000801828653001201836653001401848653002401862100002601886700001801912700002401930700002101954856004801975       2000                        eng d  a0896-026700aMu and beta rhythm topographies during motor imagery and actual movements.0 aMu and beta rhythm topographies during motor imagery and actual   c03/2000  a177–1860 v123 aPeople can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.10abeta rhythm10aEEG10aimagery10amu rhythm10asensorimotor cortex1 aMcFarland, Dennis, J.1 aMiner, L., A.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/1079168102018nas a2200265   4500008004100000022001400041245006200055210005600117260001200173300001400185490000700199520128900206653001801495653002101513653000801534653001401542653002501556653001501581653002401596100002101620700002601641700001701667700002101684856004701705       1991                        eng d  a0013-469400aAn EEG-based brain-computer interface for cursor control.0 aEEGbased braincomputer interface for cursor control  c03/1991  a252–2590 v783 aThis study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.10aCommunication10acomputer control10aEEG10amu rhythm10aoperant conditioning10aprosthesis10asensorimotor rhythm1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.1 aNeat, G., W.1 aForneris, C., A.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1707798