T
Theresa M. Vaughan
Researcher at New York State Department of Health
Publications - 46
Citations - 17543
Theresa M. Vaughan is an academic researcher from New York State Department of Health. The author has contributed to research in topics: Brain–computer interface & Sensorimotor rhythm. The author has an hindex of 30, co-authored 44 publications receiving 16164 citations. Previous affiliations of Theresa M. Vaughan include Wadsworth Center & Oklahoma State Department of Health.
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Journal ArticleDOI
Brain-computer interfaces for communication and control.
Jonathan R. Wolpaw,Jonathan R. Wolpaw,Niels Birbaumer,Niels Birbaumer,Dennis J. McFarland,Gert Pfurtscheller,Theresa M. Vaughan +6 more
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
Journal ArticleDOI
Brain-computer interface technology: a review of the first international meeting
Jonathan R. Wolpaw,Niels Birbaumer,W.J. Heetderks,Dennis J. McFarland,Paul Hunter Peckham,Gerwin Schalk,Emanuel Donchin,L.A. Quatrano,C.J. Robinson,C.J. Robinson,Theresa M. Vaughan +10 more
TL;DR: The first international meeting devoted to brain-computer interface research and development is summarized, which focuses on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users.
Journal ArticleDOI
Mu and beta rhythm topographies during motor imagery and actual movements.
TL;DR: Evidence that motor imagery could play an important role in EEG-based communication is supplied, and it is suggested that mu and beta rhythms might provide independent control signals.
Journal ArticleDOI
A comparison of classification techniques for the P300 Speller
Dean J. Krusienski,Eric W. Sellers,François Cabestaing,Sabri Bayoudh,Dennis J. McFarland,Theresa M. Vaughan,Jonathan R. Wolpaw +6 more
TL;DR: The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.
Journal ArticleDOI
Toward enhanced P300 speller performance
Dean J. Krusienski,Eric W. Sellers,Dennis J. McFarland,Theresa M. Vaughan,Jonathan R. Wolpaw +4 more
TL;DR: 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.