The effect of mutual information on independent component analysis in EEG/MEG analysis: a simulation study.
OBJECTIVE
This study investigated the influence of mutual information (MI) on temporal and dipole reconstruction based on independent components (ICs) derived from independent component analysis (ICA).


METHODS
Artificial electroencephalogram (EEG) datasets were created by means of a neural mass model simulating cortical activity of two neural sources within a four-shell spherical head model. Mutual information between neural sources was systematicallyvaried.


RESULTS
Increasing spatial error for reconstructed locations of ICs with increasing MI was observed. By contrast, the reconstruction error for the time course of source activity was largely independent of MI but varied systematically with Gaussianity of the sources.


CONCLUSIONS
Independent component analysis is a viable tool for analyzing the temporal activity of EEG/MEG (magnetoencephalography) sources even if the underlying neural sources are mutually dependent. However, if ICA is used as a preprocessing algorithm for source localization, mutual information between sources introduces a bias in the reconstructed locations of the sources.


CONCLUSIONS
Studies using ICA-algorithms based on MI have to be aware of possible errors in the spatial reconstruction of sources if these are coupled with other neural sources.
Neuro-Cognitive Psychology Program, Ludwig-Maximilians-University, Munich, Germany.
A
A
Neumann
M
M
Grosse-Wentrup
M
M
Buss
K
K
Gramann
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology; D003198:Computer Simulation; D003344:Cortical Synchronization; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors