[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-01] Phoneme recognition from speech-imagery EEG

〇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, language symbol, phoneme recognition

Speech imagery recognition from Electroencephalogram (EEG) is one of challenging technologies for non-invasive brain-computer-interface (BCI). In this report, we propose a new method to identify vowels with three features extracted from EEG signals of continuously imagined speech. The features mean line spectra detected spectral peaks using linear predictive analysis (LPA), its frequency direction derivative, and bilinear calculated tensor product of compressed line spectra. In experiments, the recognition rate was obtained approximately 73.1% by using Convolutional Neural Network (CNN). It is clear that these features are effective for vowel recognition from EEG signals of continuously imagined speech.