A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI

Abstract

Multi-user BCI (brain computer interface) refers to a kind of BCI in which there are several users participating in one task. Differently from P300 and SSVEP (steady-state visual evoked potential), MI (motor imagery)-BCI does not rely on external stimulus, which is more widely used in the assistance of disabled people. The main problem of MI-BCI is to achieve asynchronous control, which needs to improve the rest state recognition accuracy. We proposed a CNN-based approach for three-class classification of motor imagery EEG data including rest state in hybrid multi-user BCI. Firstly, we designed a two-user hybrid MI-BCI experimental paradigm and moreover proposed a CNN-based processing framework method which contains several strategies for inter-brain phase locked value (PLV) features fusion. Results show that the alpha band shows the significantly better performance than other bands and the classification performance of multi-user MI is better than single-user MI. Multi-user BCI is a potential way to enhance the performance of asynchronous MI-BCI.