The present invention provides a generalized consistency-based fatigue classification method for constructing a brain function network and a relevant vector machine. Compared with the prior art, the present invention has higher reliability and accuracy, and constructs an effective fatigue classification network by means of an information flowing direction and a causal relationship, so as to classify connection characteristics of a brain network in different mental states, effectively verifying results of topological structure research, and improving the ability to detect driving fatigue. The present invention constructs the brain network by means of a generalized consistency algorithm-based method, and regards the brain as a multi-regional cooperative network, so as to research the information flowing direction and the causal relationship between nodes of the brain, and to analyze changes in the topology structure of the brain network in different mental states, revealing the mechanism of fatigue generation, and providing a new perspective for fatigue-related research. The present invention uses the relevant vector machine to classify the connection characteristics, is able to achieve classification precision of over 90%, verifies the reliability of topology structure analysis, and also provides a new method for fatigue detection.
Fatigue Classiffication Method Based on Brain Function Network Constructed via Generalized Consistency and Relevant Vector Machine