Toward a high-throughput auditory P300-based brain-computer interface

D. S. Klobassa, T. M. Vaughan, P. Brunner, N. E. Schwartz, J. R. Wolpaw, C. Neuper, E. W. Sellers

Research output: Contribution to journalArticlepeer-review

129 Scopus citations

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 6 × 6 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.

Original languageEnglish
Pages (from-to)1252-1261
Number of pages10
JournalClinical Neurophysiology
Volume120
Issue number7
DOIs
StatePublished - Jul 2009

Keywords

  • Brain-computer interface
  • Brain-machine interface
  • EEG
  • Event-related potential
  • P300
  • Rehabilitation

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