Extracting common spatial patterns from EEG time segments for classifying motor imagery classes in a Brain Computer Interface (BCI)

Brain Computer Interface (BCI) is a system which straightly converts the acquired brain signals such as Electroencephalogram (EEG) to commands for controlling external devices. One of the most successful methods in BCI applications based on Motor Imagery is Common Spatial Pattern (CSP). In the existing CSP methods, common spatial lters are applied on whole EEG signal as one time segment for feature extraction. The fact that ERD/ERS events are not steady over time motivated us to break down EEG signal into a number of sub-segments in this study. I combine this sentence with next one: \We believe the importance of EEG channels varies for dierent time segments in classication, therefore we extract features from each time segment using the analysis of CSP method. In order to classify Motor Imagery EEG signals, we apply a LDA classier based on OVR (One- Versus-the Rest) scheme on the extracted CSP features. The considered Motor Imagery consists of four classes: left hand, right hand, foot and tongue. We used dataset 2a of BCI competition IV to evaluate our method. The result of experiment shows that this method outperforms both CSP and the best competitor of the BCI competition IV.