Research Library

open-access-imgOpen AccessEarly soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis
Chatzichristos Christos,
Kofidis Eleftherios,
Van Paesschen Wim,
De Lathauwer Lieven,
Theodoridis Sergios,
Van Huffel Sabine
Publication year2022
Publication title
human brain mapping
Resource typeJournals
PublisherJohn Wiley & Sons
Abstract Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.
Subject(s)artificial intelligence , computer science , electroencephalography , functional magnetic resonance imaging , independent component analysis , mathematics , modalities , neuroimaging , neuroscience , pattern recognition (psychology) , psychology , pure mathematics , sensor fusion , social science , sociology , tensor (intrinsic definition)
SCImago Journal Rank2.005

Seeing content that should not be on Zendy? Contact us.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here