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The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis

Treder, Matthias S., Porbadnigk, Anne K., Avarvand, Forooz Shahbazi, Mueller, Klaus-Robert and Blankertz, Benjamin 2016. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis. NeuroImage 129 , pp. 279-291. 10.1016/j.neuroimage.2016.01.019

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We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 1053-8119
Date of Acceptance: 9 January 2016
Last Modified: 06 Mar 2020 14:46

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