Classification of speech-imagery and non-recollection in EEG

Abstract

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.