EEG-based multi-class motor imagery classification using variable sized filter bank and enhanced One Versus One classifier

Motor imagery BCI is a system that is very useful to help people with disabilities who can't move their limbs. These systems use brain activity patterns that are made from motor imagery without actual movement. In this paper, we proposed enhanced OVO structure to classify EEG-based multi-class motor imagery signals. Also, variable sized filter bank is proposed to overcome the weakness of fixed sized filter bank that is used several times. SFFS channel selection is one of the efficient methods which is used to obtain the best channels. The results of four-class classification of BCI competition dataset 2a, show that the performance is improved to 0.63 kappa score.