@article {3104,
	title = {A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {121},
	year = {2010},
	month = {07/2010},
	pages = {1109{\textendash}1120},
	abstract = {OBJECTIVE:
An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988).
METHODS:
Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data.
RESULTS:
Mean online accuracy was significantly higher for the CBP, 92\%, than for the RCP, 77\%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP.
CONCLUSIONS:
These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability.
SIGNIFICANCE:
The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.},
	keywords = {brain-computer interface, brain-machine interface, EEG, event-related potential, P300, Rehabilitation},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2010.01.030},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/20347387},
	author = {Townsend, G. and LaPallo, B. K. and Chadwick B. Boulay and Krusienski, D. J. and Frye, G. E. and Hauser, C. K. and Schwartz, N. E. and Theresa M Vaughan and Jonathan Wolpaw and Sellers, E. W.}
}
@article {3108,
	title = {Toward a high-throughput auditory P300-based brain-computer interface.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {120},
	year = {2009},
	month = {07/2009},
	pages = {1252{\textendash}1261},
	abstract = {OBJECTIVE:
Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6x6 P300 Speller.
METHODS:
A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N=5) received only auditory stimuli during all 11 sessions, whereas Group AV (N=5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice.
RESULTS:
Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50\% or more, and four of these achieved 75\% or more, online accuracy (2.8\% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min.
CONCLUSIONS:
This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility.
SIGNIFICANCE:
With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI.},
	keywords = {brain-computer interface, brain-machine interface, EEG, event-related potential, P300, Rehabilitation},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2009.04.019},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/19574091},
	author = {Klobassa, D. S. and Theresa M Vaughan and Peter Brunner and Schwartz, N. E. and Jonathan Wolpaw and Neuper, C. and Sellers, E. W.}
}
@article {3199,
	title = {A P300-based brain-computer interface for people with amyotrophic lateral sclerosis.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {119},
	year = {2008},
	month = {08/2008},
	pages = {1909{\textendash}1916},
	abstract = {OBJECTIVE:
The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS.
METHODS:
Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback.
RESULTS:
In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62\% and 82\%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79\%. The amplitude and latency of the P300 remained stable over 40 weeks.
CONCLUSIONS:
Participants could communicate with the P300-based BCI and performance was stable over many months.
SIGNIFICANCE:
BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.},
	keywords = {Amyotrophic Lateral Sclerosis, brain-computer interface, electroencephalogram, event-related potentials, P300, Rehabilitation},
	issn = {1388-2457},
	doi = {10.1016/j.clinph.2008.03.034},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18571984},
	author = {Nijboer, F. and Sellers, E. W. and Mellinger, J. and Jordan, M. A. and Matuz, T. and Adrian Furdea and S Halder and Mochty, U. and Krusienski, D. J. and Theresa M Vaughan and Jonathan Wolpaw and Niels Birbaumer and K{\"u}bler, A.}
}
@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 {3222,
	title = {The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.},
	journal = {IEEE transactions on bio-medical engineering},
	volume = {51},
	year = {2004},
	month = {06/2004},
	pages = {1044{\textendash}1051},
	abstract = {Interest in developing a new method of man-to-machine communication{\textendash}a brain-computer interface (BCI){\textendash}has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.},
	keywords = {augmentative communication, BCI, beta-rhythm, brain-computer interface, EEG, ERP, imagined hand movements, lateralized readiness potential, mu-rhythm, P300, Rehabilitation, single-trial classification, slow cortical potentials},
	issn = {0018-9294},
	doi = {10.1109/TBME.2004.826692},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15188876},
	author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, Thilo and Schr{\"o}der, Michael and Niels Birbaumer}
}
@article {3226,
	title = {Brain-computer interface technology: a review of the Second International Meeting.},
	journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
	volume = {11},
	year = {2003},
	month = {06/2003},
	pages = {94{\textendash}109},
	abstract = {This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI that recognizes the commands contained in the input and expresses them in device control. Current BCIs have maximum information transfer rates of up to 25 b/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. These improvements depend on interdisciplinary cooperation among neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective criteria for evaluating alternative methods. The practical use of BCI technology will be determined by the development of appropriate applications and identification of appropriate user groups, and will require careful attention to the needs and desires of individual users.},
	keywords = {augmentative communication, Brain-computer interface (BCI), electroencephalography (EEG), Rehabilitation},
	issn = {1534-4320},
	doi = {10.1109/TNSRE.2003.814799},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12899247},
	author = {Theresa M Vaughan and Heetderks, William J. and Trejo, Leonard J. and Rymer, William Z. and Weinrich, Michael and Moore, Melody M. and K{\"u}bler, Andrea and Dobkin, Bruce H. and Niels Birbaumer and Emanuel Donchin and Wolpaw, Elizabeth Winter 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 {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}
}
