EEG-Based Classification of Implicit Intention During Self-Relevant Sentence Reading

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From electroencephalography (EEG) data during self-relevant sentence reading, we were able to discriminate two implicit intentions: 1) "agreement" and 2) "disagreement" to the read sentence. To improve the classification accuracy, discriminant features were selected based on Fisher score among EEG frequency bands and electrodes. Especially, the time-frequency representation with Morlet wavelet transforms showed clear differences in gamma, beta, and alpha band powers at frontocentral area, and theta band power at centroparietal area. The best classification accuracy of 75.5% was obtained by a support vector machine classifier with the gamma band features at frontocentral area. This result may enable a new intelligent user-interface which understands users' implicit intention, i.e., unexpressed or hidden intention.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-11
Language
English
Article Type
Article
Keywords

INDEPENDENT COMPONENT ANALYSIS; BRAIN-COMPUTER INTERFACE; LANGUAGE COMPREHENSION; LIE DETECTION; COMMUNICATION; INFORMATION; RESPONSES

Citation

IEEE TRANSACTIONS ON CYBERNETICS, v.46, no.11, pp.2535 - 2542

ISSN
2168-2267
DOI
10.1109/TCYB.2015.2479240
URI
http://hdl.handle.net/10203/214229
Appears in Collection
EE-Journal Papers(저널논문)
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