@article {3394,
	title = {The advantages of the surface Laplacian in brain-computer interface research.},
	journal = {Int J Psychophysiol},
	year = {2014},
	month = {08/2014},
	abstract = {<p>Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user{\textquoteright}s intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control.</p>},
	keywords = {brain-computer interface, sensorimotor rhythms, surface laplacian},
	issn = {1872-7697},
	doi = {10.1016/j.ijpsycho.2014.07.009},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/25091286},
	author = {Dennis J. McFarland}
}
@article {3120,
	title = {Characterizing multivariate decoding models based on correlated EEG spectral features.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {124},
	year = {2013},
	month = {07/2013},
	pages = {1297{\textendash}1302},
	abstract = {OBJECTIVE:
Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated.
METHODS:
Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity).
RESULTS:
The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features.
CONCLUSIONS:
Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable.
SIGNIFICANCE:
While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated.},
	keywords = {brain-computer interface, multicollinearity, multivariate decoding, sensorimotor rhythm},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2013.01.015},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/23466267},
	author = {Dennis J. McFarland}
}
@article {3096,
	title = {The P300-based brain-computer interface (BCI): effects of stimulus rate.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {122},
	year = {2011},
	month = {04/2011},
	pages = {731{\textendash}737},
	abstract = {OBJECTIVE:
Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8{\texttimes}9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate.
METHODS:
In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time.
RESULTS:
For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results.
CONCLUSIONS:
The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance.
SIGNIFICANCE:
These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.},
	keywords = {brain-computer interface, neuroprosthesis, P300},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2010.10.029},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21067970},
	author = {Dennis J. McFarland and Sarnacki, William A. and Townsend, George and Theresa M Vaughan and Jonathan Wolpaw}
}
@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 {3113,
	title = {A scanning protocol for a sensorimotor rhythm-based brain-computer interface.},
	journal = {Biological psychology},
	volume = {80},
	year = {2009},
	month = {02/2009},
	pages = {169{\textendash}175},
	abstract = {The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80\% mean accuracy of the last three sessions (chance accuracy=25\%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.},
	keywords = {BCI, brain-computer interface, scanning protocol, sensorimotor rhythm},
	issn = {1873-6246},
	doi = {10.1016/j.biopsycho.2008.08.004},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18786603},
	author = {Friedrich, Elisabeth V. C. and Dennis J. McFarland and Neuper, Christa and Theresa M Vaughan and Peter Brunner and Jonathan Wolpaw}
}
@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 {3205,
	title = {Toward enhanced P300 speller performance.},
	journal = {Journal of neuroscience methods},
	volume = {167},
	year = {2008},
	month = {01/2008},
	pages = {15{\textendash}21},
	abstract = {This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.},
	keywords = {brain-computer interface, event related potentials, P300 speller, stepwise linear discriminant analysis},
	issn = {0165-0270},
	doi = {10.1016/j.jneumeth.2007.07.017},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17822777},
	author = {Krusienski, D. J. and Sellers, E. W. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3134,
	title = {A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.},
	journal = {Biological psychology},
	volume = {73},
	year = {2006},
	month = {10/2006},
	pages = {242{\textendash}252},
	abstract = {We 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.},
	keywords = {Amyotrophic Lateral Sclerosis, brain-computer interface, electroencephalogram, event-related potentials, P300, Rehabilitation},
	issn = {0301-0511},
	doi = {10.1016/j.biopsycho.2006.04.007},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/16860920},
	author = {Sellers, Eric W. and Krusienski, Dean J. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw}
}
@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 {3145,
	title = {Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.},
	journal = {Neuroscience letters},
	volume = {345},
	year = {2003},
	month = {07/2002},
	pages = {89{\textendash}92},
	abstract = {People 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.},
	keywords = {augmentative communication, brain-computer interface, brain-machine interface, Electroencephalography, mu and beta rhythms, neuroprosthesis, Rehabilitation},
	issn = {0304-3940},
	doi = {10.1016/S0304-3940(03)00470-1},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12821178},
	author = {Sheikh, Hesham and Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3224,
	title = {EMG contamination of EEG: spectral and topographical characteristics.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {114},
	year = {2003},
	month = {09/2003},
	pages = {1580{\textendash}1593},
	abstract = {OBJECTIVE:
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.},
	keywords = {artifact, brain-computer interface, electroencephalogram, electromyogram, Rehabilitation},
	issn = {1388-2457},
	doi = {10.1016/S1388-2457(03)00093-2},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12948787},
	author = {Goncharova, I. I. and Dennis J. McFarland and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3234,
	title = {EEG-based communication: presence of an error potential.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {111},
	year = {2000},
	month = {12/2000},
	pages = {2138{\textendash}2144},
	abstract = {EEG-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.},
	keywords = {augmentative communication, brain-computer interface, Electroencephalography, error potential, error related negativity, event related potential, mu rhythm, Rehabilitation, sensorimotor cortex},
	issn = {1388-2457},
	doi = {10.1016/S1388-2457(00)00457-0},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/11090763},
	author = {Gerwin Schalk and Jonathan Wolpaw and Dennis J. McFarland and Pfurtscheller, G.}
}
