Improvement of mental tasks with relevant speech imagery for brain-computer interfaces

Abstract

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been attracted enough attention by researchers. In order to determine whether silent reading can improve mental tasks for BCI systems, this paper proposed a two-step experiment: mental tasks with speech imagery and mental tasks without speech imagery. Reading Chinese characters in mind is set as speech imagery. Since Chinese characters are monosyllabic, it is very convenient to read them in mind with related mental tasks simultaneously. Ten Chinese subjects are trained by two steps in this experiment. Feature vectors of EEG signals are extracted and classified by common spatial patterns (CSP) and support vector machine (SVM), respectively. Compared with just mental tasks, the accuracies between two tasks have been significantly improved by appending speech imagery, and the average of accuracies of ten subjects is increased from 76.3% to 82.3%. During the imagery period, the temporal stability of EEG signals is evaluated by Cronbach’s alpha coefficients. The steadiness of signals is different between mental tasks, and EEG signals are more stabilization with speech imagery. The stability of brain activity is conducive to the operation of BCIs.