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Data fusion refers to the joint analysis of multiple datasets which providecomplementary views of the same task. In this preprint, the problem of jointlyanalyzing electroencephalography (EEG) and functional Magnetic ResonanceImaging (fMRI) data is considered. Jointly analyzing EEG and fMRI measurementsis highly beneficial for studying brain function because these modalities havecomplementary spatiotemporal resolution: EEG offers good temporal resolutionwhile fMRI is better in its spatial resolution. The fusion methods reported sofar ignore the underlying multi-way nature of the data in at least one of themodalities and/or rely on very strong assumptions about the relation of the twodatasets. In this preprint, these two points are addressed by adopting for thefirst time tensor models in the two modalities while also exploring doublecoupled tensor decompositions and by following soft and flexible couplingapproaches to implement the multi-modal analysis. To cope with the EventRelated Potential (ERP) variability in EEG, the PARAFAC2 model is adopted. Theresults obtained are compared against those of parallel Independent ComponentAnalysis (ICA) and hard coupling alternatives in both simulated and real data.Our results confirm the superiority of tensorial methods over methods based onICA. In scenarios that do not meet the assumptions underlying hard coupling,the advantage of soft and flexible coupled decompositions is clearlydemonstrated.

Early soft and flexible fusion of EEG and fMRI via tensor decompositions