Publicaties
Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis KU Leuven
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 ...
EEG miniaturization limits for stimulus decoding with EEG sensor networks. KU Leuven
OBJECTIVE: Unobtrusive EEG monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless 'EEG sensor network' (WESN) can be formed. However, each mini-EEG in the network only has ...
Bilinear factorizations subject to monomial equality constraints via tensor decompositions KU Leuven
Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. KU Leuven
EEG-correlated fMRI analysis is widely used to detect regional BOLD fluctuations that are synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, the typical, asymmetrical and mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular ...