Analysis and Classification of Speech Imagery EEG Based on Chinese Initials

Abstract

Brain-computer interfaces (BCI) can provide external information communication for people with normal thinking but impaired motor functions. For patients with language disorders, speech imagery BCIs make it possible to communicate normally. However, there are few studies on Chinese speech imagery at present. Almost all studies employed fixed experimental content, without considering the diversity in subjects. With the purpose of improving the effect of a Chinese speech imagery BCI system, a novel experiment of Chinese initials imagery was designed. The experiment is divided into two parts. A preliminary experiment used to select content for subjects. Formal experiment-specific experimental content was designed for subjects. After preprocessing, feature extraction was carried out by common spatial patterns (CSP) and discrete wavelet transform (DWT), and then a support vector machine (SVM) and extreme learning machine (ELM) were used for classification. Finally, the best performance was obtained by the model using DWT and ELM with a highest accuracy of 73.04%. This study shows that the novel experiment is feasible and can potentially extend the capability of utilizing speech imagery in future BCI applications.