Role of independent component analysis for the integration of EEG and fMRI

Debener, S.
Biomagnetisches Zentrum, Universitätsklinikum Jena

Independent component analysis (ICA) refers to a family of blind source separation algorithms that can be used to linearly decompose otherwise overlapping sources of brain and non-brain activity from multi-channel electroencephalogram (EEG) and multi-voxel functional magnetic resonance imaging (fMRI) data. For instance, ICA has been applied to statistically remove artifacts from EEG recordings, and, less frequently, to identify event-related brain potentials and oscillations on a trial-by-trial level. The recent developments regarding the use of ICA for the removal of artifacts from EEG data recorded simultaneously with fMRI will be presented, and the virtues and limitations of ICA in this context will be discussed. It will be shown how ICA can be used for the identification and separation of source signals, and how this can help to integrate EEG and fMRI signals, thus achieving the desired good spatial and temporal resolution for the study of neurocognitive processes.

Symposium 20: Recent Advances in EEG-fMRI Integration
12.06.2009, 14:00-15:00
Seminarraum 11