[2G6-OS-18b] 脳波から音声言語情報を抽出・識別・利用する(2/2)

[2G6-OS-18b-02] Classification of speech-imagery and non-recollection in EEG

〇Daisuke Suzuki¹, Motoharu Yamao¹, Yurie Iribe¹, Ryo Taguchi², Kouichi Katsurada³, Tsuneo Nitta⁴ (1. Aichi Prefectural University, 2. Nagoya Institute of Technology, 3. Tokyo University of Science, 4. Toyohashi University of Technology)

Keywords:BCI, EEG, speech-imagery, non-recollection

Brain Computer Interface (BCI) research has been started to identify recalled syllables from Electroencephalogram (EEG) during speech-imagery. Currently, it is difficult to identify the true recall duration from EEG data. Therefore, inaccurate recall data including non-recollection duration or recall sections labeled by visual determination of spectrum outline are often used to identify the recalled syllables. Because the visual syllable labeling takes a lot of time and labor, it is desirable that the process to discriminate correct speech-imagery segments has been automated. In this paper, we constructed each model consisting of speech-imagery segments and non-recollection segments to obtain the true syllable sections. We extracted the complex cepstrum from the syllable-labeled speech-imagery/non-recollection data by visual determination and identified speech-imagery/non-recollection segments using the features. Lastly, we report the classification results by 10-fold cross validation.