A Brain-computer Interface for Motor Imagination Based on Multi-channel EEG Analysis

Abstract

BCI (Brain Computer Interface, BCI) system means that between the human brain and a computer or other electronic device established communication system. Motor imagination EEG signals are derived from the active consciousness of the human brain, spontaneously in the brain's movement imagination process of different parts of the limbs, without external stimulation.At present, as a mainstream brain-computer interface input signal, it has been increasingly studied. Traditional motor imagination EEG signal classification research is often based on the C3 and C4 dual-channel EEG signal features for extraction and differentiation, and the classification and recognition accuracy is low. In this paper, the wavelet transform coefficients of F3, F4, C3, C4, FZ, CZ, FC1, FC2, FC5 and FC6 multi-channel motor imagination EEG signals are introduced as features, and the extracted features are classified by Support Vector Machines (SVM). Proposed multi-channel Characteristics Analysis Method based on wavelet transform (Multi-channel Characteristics Analysis Method, MCCAM) with dual channel Analysis Method and Common Spatial Pattern (Common Spatial Pattern, CSP) Method are carried on the comparative study, found that multi-channel Analysis Method to imagine the classification accuracy of EEG signals is significantly higher than other two methods, so as to verify the effectiveness of the proposed multi-channel Method.