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Graph Neural Networks (GNNs) have received considerable attention since itsintroduction. It has been widely applied in various fields due to its abilityto represent graph structured data. However, the application of GNNs isconstrained by two main issues. Firstly, the "over-smoothing" problem restrictsthe use of deeper network structures. Secondly, GNNs' applicability is greatlylimited when nodes and edges are not clearly defined and expressed, as is thecase with EEG data.In this study, we proposed an innovative approach thatharnesses the distinctive properties of the graph structure's Markov Chain tooptimize the sampling depth of deep graph convolution networks. We introduced atailored method for constructing graph structures specifically designed foranalyzing EEG data, alongside the development of a vertex-level GNNclassification model for precise detection of mental disorders. In order toverify the method's performance, we conduct experiments on two disease datasetsusing a subject-independent experiment scenario. For the Schizophrenia (SZ)data, our method achieves an average accuracy of 100% using only the first 300seconds of data from each subject. Similarly, for Major Depressive Disorder(MDD) data, the method yields average accuracies of over 99%. These experimentsdemonstrate the method's ability to effectively distinguish between healthycontrol (HC) subjects and patients with mental disorders. We believe thismethod shows great promise for clinical diagnosis.

Markov Chain-Guided Graph Construction and Sampling Depth Optimization for EEG-Based Mental Disorder Detection