title: Independent component ensemble of EEG for brain-computer interface.
abstract: Recently, successful applications of independent component analysis (ICA) to electroencephalographic (EEG) signals have yielded tremendous insights into brain processes that underlie human cognition. Many studies have further established the feasibility 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 existing algorithms. The mechanisms that are used in this ensemble system include: 1) automatic ICi selection; 2) extraction of features 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 monitoring of participants' cognitive states in a realistic sustained-attention driving task. The results reveal that the proposed ICi-ensemble outperformed the previous method using a single ICi with  ∼ 7% (91.6% versus 84.3%) in the cognitive state classification. Additionally, the proposed ICi-ensemble method that characterizes the EEG dynamics of multiple brain areas favors the application of BCI in natural environments.
authors.forename: Chun-Hsiang
authors.initials: CH
authors.lastname: Chuang
authors.forename: Li-Wei
authors.initials: LW
authors.lastname: Ko
authors.forename: Yuan-Pin
authors.initials: YP
authors.lastname: Lin
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
authors.forename: Chin-Teng
authors.initials: CT
authors.lastname: Lin
mesh_terms: D016477:Artifacts; D001185:Artificial Intelligence; D001334:Automobile Driving; D062207:Brain-Computer Interfaces; D003071:Cognition / Q000502:physiology; D003657:Decision Making; D003658:Decision Making, Computer-Assisted; D004569:Electroencephalography / Q000706:statistics & numerical data*; D005240:Feasibility Studies; D006801:Humans; D009862:Online Systems; D025341:Principal Component Analysis; D011930:Reaction Time / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology; D014584:User-Computer Interface