Covert Verb Reading Contributes to Signal Classification of Motor Imagery in BCI.

Abstract

Motor imagery is widely used in the brain-computer interface (BCI) systems that can help people actively control devices to directly communicate with the external world, but its training and performance effect is usually poor for normal people. To improve operators' BCI performances, here we proposed a novel paradigm, which combined the covert verb reading in the traditional motor imagery paradigm. In our proposed paradigm, participants were asked to covertly read the presented verbs during imagining right hand or foot movements referred by those verbs. EEG signals were recorded with both our proposed paradigm and the traditional paradigm. By the common spatial pattern method, we, respectively, decomposed these signals into spatial patterns and extracted their features used in the following classification of support vector machine. Compared with the traditional paradigm, our proposed paradigm could generate clearer spatial patterns following a somatotopic distribution, which led to more distinguishable features and higher classification accuracies than those in the traditional paradigm. These results suggested that semantic processing of verbs can influence the brain activity of motor imagery and enhance the mu event-related desynchronisation. The combination of semantic processing with motor imagery is therefore a promising method for the improvement of operators' BCI performances.