Subject-Independent Classification of Japanese Spoken Sentences by Multiple Frequency Bands Phase Pattern of EEG Response During Speech Perception
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چکیده
Recent speech perception models propose that neural oscillations in theta band show phase locking to speech envelope to extract syllabic information and rapid temporal information is processed by the corresponding higher frequency band (e.g., low gamma). It is suggested that phase-locked responses to acoustic features show consistent patterns across subjects. Previous magnetoencephalographic (MEG) experiment showed that subject-dependent template matching classification by theta phase patterns could discriminate three English spoken sentences. In this paper, we adopt electroencephalography (EEG) to the spoken sentence discrimination on Japanese language, and we investigate the performances in various different settings by using: (1) template matching and support vector machine (SVM) classifiers; (2) subject dependent and independent models; (3) multiple frequency bands including theta, alpha, beta, low gamma, and the combination of all frequency bands. The performances in almost settings were higher than the chance level. While performances of SVM and template matching did not differ, the performance with combination of multiple frequency bands outperformed the one that trained only on single frequency bands. Best accuracies in subject dependent and independent models achieved 55.2% by SVM on the combination of all frequency bands and 44.0% by template matching on the combination of all frequency bands, respectively.
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تاریخ انتشار 2017