Tensor-based Blind Source Separation for Structured EEG-fMRI Data Fusion

In this thesis, we devise advanced signal processing techniques which integrate the multimodal data stemming from simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which are two complementary medical imaging modalities to monitor brain (dys)function. We focus on their application in refractory epilepsy, wherein some brain cells undergo hypersynchronous activity, leading to seizures that cannot be suppressed by medication. In such cases, EEG-fMRI can aid a presurgical evaluation of the patient, to infer the location in the brain where epileptic discharges originate. We develop data fusion approaches based on representations of the data as tensors ('multidimensional arrays') to capture the rich, complex nature of EEG and fMRI, and to exploit their attractive properties for data mining. Our experiments show that these customized coupled tensor decompositions are not only able to extract components that model the temporal, spatial and spectral profiles of epileptic discharges, but also to estimate the variable functional relationship between EEG and fMRI, i.e., neurovascular coupling. Clinical validation shows that these novel techniques produce complementary sets of biomarkers, which assist the characterization and presurgical planning for epilepsy.