title: EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods.
abstract: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.
authors.affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and School of Psychology, Southwest University, Chongqing, 400715, PR China. xlei@swu.edu.cn
authors.forename: Xu
authors.initials: X
authors.lastname: Lei
authors.forename: Pedro A
authors.initials: PA
authors.lastname: Valdes-Sosa
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods* / Q000639:trends; D003071:Cognition / Q000502:physiology; D004569:Electroencephalography / Q000379:methods* / Q000639:trends; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000639:trends; D008959:Models, Neurological