Work

Incorporating Higher Dimensionality in Joint Decomposition of EEG and fMRI

Year: 2014

Type: conference-paper

Abstract: EEG-fMRI research to study brain function became popular because of the complementarity of the modalities. Through the use of data-driven approaches such as jointICA, sources extracted from EEG can be linked to regions in fMRI. Joint-ICA in its standard formulation however does not allow for the inclusion of multiple EEG electrodes, so it is a rather arbitrary choice which electrode is used in the analysis. In this study, we explore several ways to include the higher dimensionality of the EEG during a joint decomposition of EEG and fMRI. Our results show that incorporation of multiple channels in the jointICA can reveal new relations between fMRI activation maps and ERP features.

Source: Lirias (KU Leuven)

Authors: Swinnen, Wouter, Hunyadi, Borbála, Acar, E., Van Huffel, Sabine, De Vos, Maarten

Institutions: iMinds, KU Leuven, University of Copenhagen, Carl von Ossietzky Universität Oldenburg, Hearing4all

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: Fonds Wetenschappelijk Onderzoek, FP7 Ideas: European Research Council

Awards: G6182351167, Advanced algorithm for integrated EEG-fMRI data processing., Biomedical Data Fusion using Tensor based Blind Source Separation

OpenAlex ID: W2400017852

FWCI: 6.251

Cites: 7

Cited by: 15

Related to: 10