Speech modality classification with cortical EEG signals

The present work aimed to classify three speech modalities (i.e., spoken speech, intended speech and imagined speech) by using their corresponding cortical EEG signals. Eleven participants were recruited to take part in the experiments to produce 70 Mandarin-Chinese monosyllables in different speech modalities. The EEG signals were recorded during the experiments and processed by discrete wavelet transform (DWT), and the extracted features were selected by a minimum redundancy maximum relevance (MRMR) algorithm. Finally, 50 selected features were trained and tested by a multi-class Support Vector Machine for the task of speech modality classification. The average classification accuracy across participants for distinguishing three speech modalities was 66.0%, with pairwise classification accuracies ranging from 71.5% to 85.7%. The results of this work demonstrated the feasibility of identifying different speech modalities in speech-based BCIs by employing EEG signals.