Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching | Zendy

Guohun Zhu | Zendy; Yan Li | Zendy; Peng Wen | Zendy

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Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching

Author(s) -

Guohun Zhu,

Yan Li,

Peng Wen

Publication year - 2011

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2011.p1221

Subject(s) - computer science , matching (statistics) , electroencephalography , measure (data warehouse) , pattern recognition (psychology) , artificial intelligence , data mining , psychology , statistics , neuroscience , mathematics

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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