Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System

Improving classification accuracy of motor imagery-based brain computer interface (MI-BCI) systems has been discussed widely in the BCI research community. Analyses of multi-class MI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to binary-class data. This paper introduces a new model for multi-class MI-BCI data classification. The approach encompasses two main stages: feature extraction and fuzzy classification. In the feature extraction phase, a common spatial pattern algorithm is employed to extract significant discriminant features from the multi-class data. The obtained features serve as inputs to a fuzzy classifier that is designed by fusing the fuzzy standard additive model and particle swarm optimization method. The proposed fuzzy model aims to handle uncertainty, noise, and outliers existed in MI-BCI data. Experimental studies are carried out using two benchmark BCI competition data sets. Our results show the effectiveness of the fuzzy classifier against its competing techniques. The proposed model paths the way for paralysed or disordered patients to use movement imagery to control different assistive devices in their daily activities.