@article {3091,
	title = {Should the parameters of a BCI translation algorithm be continually adapted?.},
	journal = {Journal of neuroscience methods},
	volume = {199},
	year = {2011},
	month = {07/2011},
	pages = {103{\textendash}107},
	abstract = {People 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.},
	keywords = {adaptation, brain-computer interface, EEG},
	issn = {1872-678X},
	doi = {10.1016/j.jneumeth.2011.04.037},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21571004},
	author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3093,
	title = {Trained modulation of sensorimotor rhythms can affect reaction time.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {122},
	year = {2011},
	month = {09/2011},
	pages = {1820{\textendash}1826},
	abstract = {OBJECTIVE:
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.},
	keywords = {brain-computer interface, EEG, Reaction Time},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2011.02.016},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21411366},
	author = {Chadwick B. Boulay and Sarnacki, W. A. and Jonathan Wolpaw and Dennis J. McFarland}
}
@article {3132,
	title = {An auditory brain-computer interface (BCI).},
	journal = {Journal of neuroscience methods},
	volume = {167},
	year = {2008},
	month = {01/2008},
	pages = {43{\textendash}50},
	abstract = {Brain-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.},
	keywords = {auditory feedback, brain-computer interface, EEG, locked-in state, motivation, sensorimotor rhythm},
	issn = {0165-0270},
	doi = {10.1016/j.jneumeth.2007.02.009},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17399797},
	author = {Nijboer, Femke and Adrian Furdea and Gunst, Ingo and Mellinger, J{\"u}rgen and Dennis J. McFarland and Niels Birbaumer and K{\"u}bler, Andrea}
}
@article {3128,
	title = {Electrophysiological markers of skill-related neuroplasticity.},
	journal = {Biological psychology},
	volume = {78},
	year = {2008},
	month = {07/2008},
	pages = {221{\textendash}230},
	abstract = {Neuroplasticity 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{\textquoteright}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.},
	keywords = {EEG, ERP, neuroplasticity, skill learning},
	issn = {0301-0511},
	doi = {10.1016/j.biopsycho.2008.03.014},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18455861},
	author = {Romero, Stephen G. and Dennis J. McFarland and Faust, Robert and Farrell, Lori and Anthony T. Cacace}
}
@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}
}
@conference {3436,
	title = {Tracking of the mu rhythm using an empirically derived matched filter.},
	booktitle = {Proc. IEEE International Conference of Neural Engineering},
	year = {2005},
	month = {03/2005},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Arlington, VA},
	keywords = {bioelectric potentials, Brain Computer Interfaces, brain modeling, brain-computer interface, communication device, communication system control, cortical mu rhythm modulation, EEG, Electroencephalography, empirically derived matched filter, handicapped aids, laboratories, matched filters, medical signal detection, medical signal processing, monitoring, motor imagery, mu rhythm tracking, noninvasive treatment, rhythm, synchronous motors, two-dimensional cursor control data},
	isbn = {0-7803-8710-4},
	doi = {10.1109/CNE.2005.1419559},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559},
	author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland 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 {3267,
	title = {An EEG-based brain-computer interface for cursor control.},
	journal = {Electroencephalography and clinical neurophysiology},
	volume = {78},
	year = {1991},
	month = {03/1991},
	pages = {252{\textendash}259},
	abstract = {This 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.},
	keywords = {Communication, computer control, EEG, mu rhythm, operant conditioning, prosthesis, sensorimotor rhythm},
	issn = {0013-4694},
	doi = {10.1016/0013-4694(91)90040-B},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/1707798},
	author = {Jonathan Wolpaw and Dennis J. McFarland and Neat, G. W. and Forneris, C. A.}
}
