@article {3370,
	title = {Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.},
	journal = {Amyotroph Lateral Scler Frontotemporal Degener},
	volume = {15},
	year = {2014},
	month = {06/2014},
	pages = {207-15},
	abstract = {Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 {\texttimes} 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3\%). Seventeen averaged 92 ({\textpm} 3)\% (range 71-100\%), which is adequate for communication (G70 group). Eight averaged 12 ({\textpm} 6)\% (range 0-36\%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65\%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.},
	keywords = {Adult, Aged, Amyotrophic Lateral Sclerosis, Biofeedback, Psychology, brain-computer interfaces, Communication Disorders, Electroencephalography, Event-Related Potentials, P300, Female, Humans, Male, Middle Aged, Online Systems, Photic Stimulation, Psychomotor Performance, Reaction Time},
	issn = {2167-9223},
	doi = {10.3109/21678421.2013.865750},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24555843},
	author = {McCane, Lynn M and Sellers, Eric W and Dennis J. McFarland and Mak, Joseph N and Carmack, C Steve and Zeitlin, Debra and Jonathan Wolpaw and Theresa M Vaughan}
}
@article {3386,
	title = {A practical, intuitive brain-computer interface for communicating {\textquoteright}yes{\textquoteright} or {\textquoteright}no{\textquoteright} by listening.},
	journal = {J Neural Eng},
	volume = {11},
	year = {2014},
	month = {06/2014},
	pages = {035003},
	abstract = {OBJECTIVE:
Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words {\textquoteright}yes{\textquoteright} and {\textquoteright}no{\textquoteright}) without loss of performance, and whether the system could be used by people in the locked-in state.
APPROACH:
We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions.
MAIN RESULTS:
The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects{\textquoteright} performance with word stimuli (mean 77\% {\textpm} 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73\% {\textpm} 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects.
SIGNIFICANCE:
Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.},
	keywords = {Adult, Aged, Algorithms, Auditory Perception, brain-computer interfaces, Communication Aids for Disabled, Electroencephalography, Equipment Design, Equipment Failure Analysis, Female, Humans, Male, Man-Machine Systems, Middle Aged, Quadriplegia, Treatment Outcome, User-Computer Interface},
	issn = {1741-2552},
	doi = {10.1088/1741-2560/11/3/035003},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24838278},
	author = {Jeremy Jeremy Hill and Ricci, Erin and Haider, Sameah and McCane, Lynn M and Susan M Heckman and Jonathan Wolpaw and Theresa M Vaughan}
}
@inbook {2865,
	title = {BCIs That Use Electrocorticographic Activity.},
	booktitle = {Brain-Computer Interfaces: Principles and Practice},
	year = {2012},
	publisher = {Oxford University Press},
	organization = {Oxford University Press},
	abstract = {This chapter discusses the potential of electrocorticography (ECoG) as a clinically useful brain-computer interface signal modality. ECoG has greater amplitude, higher topographical resolution, and a much broader frequency range than scalp-recorded electroencephalography and is less susceptible to artifacts. With current and foreseeable recording methodologies, ECoG is likely to have greater long-term stability than intracortically recorded signals. Furthermore, it can more readily be recorded from larger cortical areas, and it requires much lower digitization rates, thus greatly reducing the power requirements of wholly implanted systems.},
	keywords = {brain signals, brain-computer interfaces, ECoG, intracortically recorded signals},
	issn = {ISBN: 978-0195388855},
	doi = {10.1093/acprof:oso/9780195388855.003.0015},
	url = {http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780195388855.001.0001/acprof-9780195388855-chapter-015},
	author = {Jonathan Wolpaw and E. Winter-Wolpaw and Gerwin Schalk}
}
@article {3219,
	title = {Brain-computer interfaces (BCIs) for communication and control: a mini-review.},
	journal = {Supplements to Clinical neurophysiology},
	volume = {57},
	year = {2004},
	month = {02/2004},
	pages = {607{\textendash}613},
	keywords = {augmentative communication and control, brain-computer interfaces, neuro-muscular disorders},
	issn = {1567-424X},
	doi = {10.1145/1296843.1296845},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/16106662},
	author = {Jonathan Wolpaw}
}
