Analysis and classification of speech imagery EEG for BCI

Electroencephalogram (EEG) is generally used in brain-computer interface (BCI), including motor imagery, mental task, steady-state evoked potentials (SSEPs) and P300. In order to complement existing motor-based control paradigms, this paper proposed a novel imagery mode: speech imagery. Chinese characters are monosyllabic and one Chinese character can express one meaning. Thus, eight Chinese subjects were required to read two Chinese characters in mind in this experiment. There were different shapes, pronunciations and meanings between two Chinese characters. Feature vectors of EEG signals were extracted by common spatial patterns (CSP), and then these vectors were classified by support vector machine (SVM). The accuracy between two characters was not superior. However, it was still effective to distinguish whether subjects were reading one character in mind, and the accuracies were between 73.65% and 95.76%. The results were better than vowel speech imagery, and they were suitable for asynchronous BCI. BCI systems will be also extended from motor imagery to combine motor imagery and speech imagery in the future.