The Possibility of Single-trial Classification of Viewed Characters using EEG Waveforms
نویسندگان
چکیده
Electroencephalograms (EEGs) contain responses to visual stimulus, however, signal noise often prevents these from being easily obtained. To classify EEG waveforms, a signal processing procedure using the relationship between EEG and ERP, which is the summation of EEG waveforms, was developed. The processing technique involves the prediction of signals using Support Vector Regression. The procedure was developed and applied to a Kanji recognition task used to classify viewing characters, symbols or Kanji. The accuracy of classification between using EEG waveforms with ERP references and without ERP references was compared. The accuracy with references was significantly more than by chance and was higher than EEG waveforms without references.
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Single-trial Classification of Viewed Characters using Single-channel EEG Waveforms
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