title: Online recursive independent component analysis for real-time source separation of high-density EEG.
abstract: Online Independent Component Analysis (ICA) algorithms have recently seen increasing development and application across a range of fields, including communications, biosignal processing, and brain-computer interfaces. However, prior work in this domain has primarily focused on algorithmic proofs of convergence, with application limited to small `toy' examples or to relatively low channel density EEG datasets. Furthermore, there is limited availability of computationally efficient online ICA implementations, suitable for real-time application. This study describes an optimized online recursive ICA algorithm (ORICA), with online recursive least squares (RLS) whitening, for blind source separation of high-density EEG data. It is implemented as an online-capable plugin within the open-source BCILAB (EEGLAB) framework. We further derive and evaluate a block-update modification to the ORICA learning rule. We demonstrate the algorithm's suitability for accurate and efficient source identification in high density (64-channel) realistically-simulated EEG data, as well as real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment.
authors.forename: Sheng-Hsiou
authors.initials: SH
authors.lastname: Hsu
authors.forename: Tim
authors.initials: T
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; D001921:Brain / Q000503:physiopathology; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D016018:Least-Squares Analysis; D012815:Signal Processing, Computer-Assisted