BCI competition 2003 - Data set Ia: Combining gamma-band power with slow cortical potentials to improve single-trial classification of electrolencephallographic signals

Brett D. Mensh, Justin Werfel, H. Sebastian Seung

Research output: Contribution to journalArticle

118 Scopus citations

Abstract

In one type of brain-computer interface (BCI), users self-modulate brain activity as detected by electroencephalography (EEG). To infer user intent, EEG signals are classified by algorithms which typically use only one of the several types of information available in these signals. One such BCI uses slow cortical potential (SCP) measures to classify single trials. We complemented these measures with estimates of high-frequency (gamma-band) activity, which has been associated with attentional and intentional states. Using a simple linear classifier, we obtained significantly greater classification accuracy using both types of information from the same recording epochs compared to using SCPs alone.

Original languageEnglish (US)
Pages (from-to)1052-1056
Number of pages5
JournalIEEE Transactions on Biomedical Engineering
Volume51
Issue number6
DOIs
StatePublished - Jun 1 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Keywords

  • Multitaper
  • Spectral analysis

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