Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching
Guohun Zhu*,***, Yan Li*,***, and Peng (Paul) Wen**,***
*Department of Mathematics and Computing, University of Southern Queensland
**Faculty of Engineering and Surveying, University of Southern Queensland
***Centre for Systems Biology, University of Southern Queensland, USQ Toowoomba Campus, West Street, Toowoomba, QLD 4350, Australia
EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimuli.
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