@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 {3144,
	title = {Brain-computer interface (BCI) operation: optimizing information transfer rates.},
	journal = {Biological psychology},
	volume = {63},
	year = {2003},
	month = {07/2003},
	pages = {237{\textendash}251},
	abstract = {People 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{\textquoteright}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.},
	keywords = {augmentative communication, Electroencephalography, information, Learning, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex},
	issn = {0301-0511},
	doi = {10.1016/S0301-0511(03)00073-5},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12853169},
	author = {Dennis J. McFarland and Sarnacki, William A. 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.}
}
@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}
}
@article {3249,
	title = {Spatial filter selection for EEG-based communication.},
	journal = {Electroencephalography and clinical neurophysiology},
	volume = {103},
	year = {1997},
	month = {09/1997},
	pages = {386{\textendash}394},
	abstract = {Individuals 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.},
	keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex},
	issn = {0013-4694},
	doi = {10.1016/S0013-4694(97)00022-2},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/9305287},
	author = {Dennis J. McFarland and McCane, L. M. and David, S. V. and Jonathan Wolpaw}
}
@article {3245,
	title = {Timing of EEG-based cursor control.},
	journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
	volume = {14},
	year = {1997},
	month = {11/1997},
	pages = {529{\textendash}538},
	abstract = {Recent 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{\textquoteright}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.},
	keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex},
	issn = {0736-0258},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/9458060},
	author = {Jonathan Wolpaw and Flotzinger, D. and Pfurtscheller, G. and Dennis J. McFarland}
}
@article {3262,
	title = {Multichannel EEG-based brain-computer communication.},
	journal = {Electroencephalography and clinical neurophysiology},
	volume = {90},
	year = {1994},
	month = {06/1994},
	pages = {444{\textendash}449},
	abstract = {Individuals 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.},
	keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex},
	issn = {0013-4694},
	doi = {10.1016/0013-4694(94)90135-X},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/7515787},
	author = {Jonathan Wolpaw and Dennis J. McFarland}
}
