NDLI: Mutual information based relevance network analysis: a Parkinson’S disease study

Content Provider	IEEE Xplore Digital Library
Author	Lee, P.W.-H. Wang, Z.J. McKeown, M.J.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC (Lee, P.W.-H.; Wang, Z.J.)
Abstract	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.
Starting Page	497
Ending Page	500
File Size	1127747
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4517655
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mutual information Information analysis Parkinson's disease Electroencephalography Independent component analysis Frequency Analysis of variance Performance gain Flowcharts Band pass filters Relevance Network EEG Cross-Spectrogram Mutual Information Parkinson’s Disease
Content Type	Text
Resource Type	Article

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