title: Real-Time Adaptive EEG Source Separation Using Online Recursive Independent Component Analysis.
abstract: Independent component analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: 1) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; 2) capability to detect and adapt to nonstationarity in 64-ch simulated EEG data; and 3) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces.
authors.forename: Sheng-Hsiou
authors.initials: SH
authors.lastname: Hsu
authors.forename: Tim R
authors.initials: TR
authors.lastname: Mullen
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
authors.forename: Gert
authors.initials: G
authors.lastname: Cauwenberghs
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000295:instrumentation* / Q000706:statistics & numerical data; D006801:Humans; D016018:Least-Squares Analysis; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted