Motor Imagery EEG Signals Analysis Based on Bayesian Network with Gaussian Distribution

摘要

A novel communication channel from brain to machine, the research of Brain-computer interfacing is attracted more and more attention recently. In this paper, a novel method based on Bayesian Network is proposed to analyze multi motor imagery task. On the one hand, the channel physical position and mean motor imagery class information are adopted as constrains in BN structure construction. On the other hand, continuous Gaussian distribution model is used to model the bayesian network nodes other than discretizing variable in traditional methods, which would reflect the real character of EEG signals. Finally, the network structure and edge inference score are used to construct SVM classifier. Experimental results on the BCI competition data and lab collected data show that the average accuracy of the two experiments are 93% and 88%, which are better comparing to current methods.