Coupled generator decomposition for fusion of electro- and magnetoencephalography data

[2403.15409] Coupled generator decomposition for fusion of electro- and magnetoencephalography data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2403.15409 (eess) [Submitted on 2 Mar 2024] Title: Coupled generator decomposition for fusion of electro- and magnetoencephalography data Authors: Anders Stevnhoved Olsen , Jesper Duemose Nielsen , Morten Mørup View a PDF of the paper titled Coupled generator decomposition for fusion of electro- and magnetoencephalography data, by Anders Stevnhoved Olsen and 2 other authors View PDF HTML (experimental) 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 variability. Through split-half cross-validation of EEG/MEG trials, we investigate the optimal model order and regularization strengths for models of varying complexity, comparing these to a group-level model assuming shared brain responses to stimuli. Our findings reveal altered $\sim170ms$ fusiform face area activation for scrambled faces, as opposed to real faces, particularly evident in the multimodal, multisubject model. Model parameters were inferred using stochastic optimization in PyTorch, demonstrating comparable performance to conventional quadratic programming inference for SPCA but with considerably faster execution. We provide an easily accessible toolbox for coupled generator decomposition that includes data fusion for SPCA, archetypal analysis and directional archetypal analysis. Overall, our approach offers a promising new avenue for data fusion. Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2403.15409 [eess.SP] (or arXiv:2403.15409v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2403.15409 Focus to learn more arXiv-issued DOI via DataCite Journal reference: EUSIPCO2024 Related DOI : https://doi.org/10.23919/EUSIPCO63174.2024.10715032 Focus to learn more DOI(s) linking to related resources Submission history From: Anders Stevnhoved Olsen [ view email ] [v1] Sat, 2 Mar 2024 12:09:16 UTC (2,278 KB) Full-text links: Access Paper: View a PDF of the paper titled Coupled generator decomposition for fusion of electro- and magnetoencephalography data, by Anders Stevnhoved Olsen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2024-03 Change to browse by: cs cs.LG eess q-bio q-bio.NC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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