@article {3138,
	title = {Brain-computer interface (BCI) operation: signal and noise during early training sessions.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {116},
	year = {2005},
	month = {01/2005},
	pages = {56{\textendash}62},
	abstract = {OBJECTIVE:
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{\textquoteright} 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{\textquoteright}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.},
	keywords = {brain-computer interface, EEG, Electroencephalography, Learning, mu rhythm, sensorimotor cortex},
	issn = {1388-2457},
	doi = {10.1016/j.clinph.2004.07.004},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15589184},
	author = {Dennis J. McFarland and Sarnacki, William A. and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3238,
	title = {Mu and beta rhythm topographies during motor imagery and actual movements.},
	journal = {Brain topography},
	volume = {12},
	year = {2000},
	month = {03/2000},
	pages = {177{\textendash}186},
	abstract = {People 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.},
	keywords = {beta rhythm, EEG, imagery, mu rhythm, sensorimotor cortex},
	issn = {0896-0267},
	doi = {10.1023/A:1023437823106},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/10791681},
	author = {Dennis J. McFarland and Miner, L. A. and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3241,
	title = {EEG-based communication: analysis of concurrent EMG activity.},
	journal = {Electroencephalography and clinical neurophysiology},
	volume = {107},
	year = {1998},
	month = {12/1998},
	pages = {428{\textendash}433},
	abstract = {OBJECTIVE:
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.},
	keywords = {augmentative communication, conditioning, Electroencephalography, Electromyography, mu rhythm, Rehabilitation, sensorimotor cortex},
	issn = {0013-4694},
	doi = {10.1016/S0013-4694(98)00107-2},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/9922089},
	author = {Theresa M Vaughan and Miner, L. A. and Dennis J. McFarland and Jonathan Wolpaw}
}
