Early soft and flexible fusion of EEG and fMRI via tensor decompositions
This work addresses the challenge of fusing EEG and fMRI data for brain function studies, offering a more robust method for neuroscientists, though it appears incremental as it builds on existing tensor decomposition techniques.
The authors tackled the problem of jointly analyzing EEG and fMRI data by using tensor decompositions with soft and flexible coupling, addressing limitations of existing methods that ignore multi-way data structures or rely on strong assumptions. Their results showed superiority over parallel ICA and hard coupling alternatives in simulated and real data, demonstrating clear advantages in scenarios not meeting hard coupling assumptions.
Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) data is considered. Jointly analyzing EEG and fMRI measurements is highly beneficial for studying brain function because these modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The fusion methods reported so far ignore the underlying multi-way nature of the data in at least one of the modalities and/or rely on very strong assumptions about the relation of the two datasets. In this preprint, these two points are addressed by adopting for the first time tensor models in the two modalities while also exploring double coupled tensor decompositions and by following soft and flexible coupling approaches to implement the multi-modal analysis. To cope with the Event Related Potential (ERP) variability in EEG, the PARAFAC2 model is adopted. The results obtained are compared against those of parallel Independent Component Analysis (ICA) and hard coupling alternatives in both simulated and real data. Our results confirm the superiority of tensorial methods over methods based on ICA. In scenarios that do not meet the assumptions underlying hard coupling, the advantage of soft and flexible coupled decompositions is clearly demonstrated.