Independent component ensemble of EEG for brain computer interface

Abstract—Recently, successful applications of independent com-ponent analysis (ICA) to electroencephalographic (EEG) signals have yielded tremendous insights into brain processes that underlie human cognition. Many studies have further established the feasi-bility of using independent processes to elucidate human cognitive states. However, various technical problems arise in the building of an online brain–computer interface (BCI). These include the lack of an automatic procedure for selecting independent components of interest (ICi) and the potential risk of not obtaining a desired ICi. Therefore, this study proposes an ICi-ensemble method that uses multiple classifiers with ICA processing to improve upon ex-isting algorithms. The mechanisms that are used in this ensemble system include: 1) automatic ICi selection; 2) extraction of fea-tures of the resultant ICi; 3) the construction of parallel pipelines for effectively training multiple classifiers; and a 4) simple process that combines the multiple decisions. The proposed ICi-ensemble is demonstrated in a typical BCI application, which is the moni-toring of participants ’ cognitive states in a realistic sustained-at-tention driving task. The results reveal that the proposed ICi-en-semble outperformed the previous method using a single ICi with (91.6 % versus 84.3%) in the cognitive state classification. Additionally, the proposed ICi-ensemble method that character-izes the EEG dynamics of multiple brain areas favors the applica-tion of BCI in natural environments. Index Terms—Brain–computer interface (BCI), independent component analysis (ICA), multiple classifier system. I