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• W2322275502 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." @default.
• W2322275502 created "2016-06-24" @default.
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• W2322275502 date "2014-08-01" @default.
• W2322275502 modified "2023-09-26" @default.
• W2322275502 title "Online recursive independent component analysis for real-time source separation of high-density EEG" @default.
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• W2322275502 doi "https://doi.org/10.1109/embc.2014.6944462" @default.
• W2322275502 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25570830" @default.
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