Literature DB >> 16860920

A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.

Eric W Sellers1, Dean J Krusienski, Dennis J McFarland, Theresa M Vaughan, Jonathan R Wolpaw.   

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.

Mesh:

Year:  2006        PMID: 16860920     DOI: 10.1016/j.biopsycho.2006.04.007

Source DB:  PubMed          Journal:  Biol Psychol        ISSN: 0301-0511            Impact factor:   3.251


  102 in total

1.  Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication.

Authors:  D B Ryan; G E Frye; G Townsend; D R Berry; S Mesa-G; N A Gates; E W Sellers
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2.  Does the 'P300' speller depend on eye gaze?

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4.  A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.

Authors:  G Townsend; B K LaPallo; C B Boulay; D J Krusienski; G E Frye; C K Hauser; N E Schwartz; T M Vaughan; J R Wolpaw; E W Sellers
Journal:  Clin Neurophysiol       Date:  2010-03-26       Impact factor: 3.708

5.  Access interface strategies.

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6.  Describing different brain computer interface systems through a unique model: a UML implementation.

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Review 7.  Human visual skills for brain-computer interface use: a tutorial.

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Review 8.  Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia.

Authors:  John P Donoghue; Arto Nurmikko; Michael Black; Leigh R Hochberg
Journal:  J Physiol       Date:  2007-02-01       Impact factor: 5.182

9.  Real-time two-dimensional asynchronous control of a computer cursor with a single subdural electrode.

Authors:  César Márquez-Chin; Milos R Popovic; Egor Sanin; Robert Chen; Andres M Lozano
Journal:  J Spinal Cord Med       Date:  2012-09       Impact factor: 1.985

10.  Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.

Authors:  Lynn M McCane; Eric W Sellers; Dennis J McFarland; Joseph N Mak; C Steve Carmack; Debra Zeitlin; Jonathan R Wolpaw; Theresa M Vaughan
Journal:  Amyotroph Lateral Scler Frontotemporal Degener       Date:  2014-02-20       Impact factor: 4.092

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