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Coupled generator decomposition for fusion of electro- and magnetoencephalography data
March 26, 2024, 4:42 a.m. | Anders Stevnhoved Olsen, Jesper Duemose Nielsen, Morten M{\o}rup
cs.LG updates on arXiv.org arxiv.org
Abstract: Data fusion modeling can identify common features across diverse data sources while accounting for source-specific variability. Here we introduce the concept of a \textit{coupled generator decomposition} and demonstrate how it generalizes sparse principal component analysis (SPCA) for data fusion. Leveraging data from a multisubject, multimodal (electro- and magnetoencephalography (EEG and MEG)) neuroimaging experiment, we demonstrate the efficacy of the framework in identifying common features in response to face perception stimuli, while accommodating modality- and subject-specific …
abstract accounting analysis arxiv concept cs.lg data data sources diverse eess.sp features fusion generator identify modeling multimodal q-bio.nc type
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