Abstract：
: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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Treder MS,Porbadnigk AK,Shahbazi Avarvand F,Müller KR,Blankertz Bdoi
10.1016/j.neuroimage.2016.01.019subject
Has Abstractpub_date
2016-04-01 00:00:00pages
279-291eissn
1053-8119issn
1095-9572pii
S1053-8119(16)00025-2journal_volume
129pub_type
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