Mutual information based relevance network analysis: a Parkinson #x2019;S disease study

TitleMutual information based relevance network analysis: a Parkinson #x2019;S disease study
Publication TypeConference Paper
Year of Publication2008
AuthorsLee, P. W. - H., Z. J. Wang, and M. J. McKeown
Conference NameAcoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Pagination497 -500
Date Publishedmar.
KeywordsANOVA, brain, diseases, EEG segmentation method, electroencephalogram segmentation method, electroencephalography, independent component analysis, independent components, medical signal processing, mutual information, network dynamics monitoring, nonlinear statistical dependencies, Parkinson's disease, relevance network analysis, temporal dynamics

Monitoring the dynamics of networks in the brain is of central importance in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence only explore linear dependencies, which may be unsatisfactory. We propose applying mutual information as an alternative metric for assessing possible nonlinear statistical dependencies between EEG channels. However, EEG data are complicated by the fact that data are inherently non-stationary and also the brain may not work on the task continually. To address these concerns, we propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectra of computed independent components. A real case study in Parkinson's disease and further group analysis employing ANOVA demonstrate different brain connectivity between tasks and between subject groups and also a plausible mechanism for the beneficial effects of medication used in this disease. The proposed method appears to be a promising approach for EEG analysis and warrants further study.


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