An EEG brain–computer interface approach for classifying vigilance states in humans: a gamma band focus supports low misclassification rates

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DOIResolve DOI: http://doi.org/10.1080/10447318.2017.1342942
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TypeArticle
Journal titleInternational Journal of Human–Computer Interaction
ISSN1044-7318
1532-7590
Pages112
AbstractThis research investigated a brain–computer interface (BCI) for classifying vigilance states. A BCI approach based on event-related potential (ERP) and spectral features of electroencephalographic (EEG) data derived from an auditory oddball paradigm was employed. A 128-channel EEG system recorded potentials while participants simultaneously completed a prolonged visual match-to-sample task. It was hypothesized that better classification rates would be found for a group of “bored” participants, as compared to the not-bored participants. The best classification rates were found by extracting EEG features from gamma frequencies at middle and late latencies (for the standard tones). Critically, the BCI paradigm was most effective for the bored participants (mean misclassification rate = 13%), as compared to the not-bored participants (mean misclassification rate = 21%). These findings extend our knowledge of reliable latencies and spectral features of ERP data most salient to the classification of vigilance states.
Publication date
PublisherTaylor & Francis
LanguageEnglish
AffiliationAerospace; National Research Council Canada
Peer reviewedYes
NPARC number23002597
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Record identifier9c146dcc-b0e5-4a2f-a56c-1abef0d61f78
Record created2017-12-05
Record modified2017-12-05
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