EEG-based high-performance depression state recognition

• Published in: Frontiers in neuroscience 2024-01, Vol.17, p.1301214-1301214
• Main Authors: Wang, Zhuozheng, Hu, Chenyang, Liu, Wei, Zhou, Xiaofan, Zhao, Xixi
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Brain research; Datasets; Disease; EEG; Electrodes; Electroencephalography; Entropy; feature dimension reduction; Feature selection; feature-weighted fusion; graph convolutional neural network; Mental depression; Mental disorders; Neural networks; recognition of depressive state; scalp EEG signals
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2023.1301214

Depression is a global disease that is harmful to people. Traditional identification methods based on various scales are not objective and accurate enough. Electroencephalogram (EEG) contains abundant physiological information, which makes it a new research direction to identify depression state. However, most EEG-based algorithms only extract the original EEG features and ignore the complex spatiotemporal information interactions, which will reduce performance. Thus, a more accurate and objective method for depression identification is urgently needed. In this work, we propose a novel depression identification model: W-GCN-GRU. In our proposed method, we censored six sensitive features based on Spearman's rank correlation coefficient and assigned different weight coefficients to each sensitive feature by AUC for the weighted fusion of sensitive features. In particular, we use the GCN and GRU cascade networks based on weighted sensitive features as depression recognition models. For the GCN, we creatively took the brain function network based on the correlation coefficient matrix as the adjacency matrix input and the weighted fused sensitive features were used as the node feature matrix input. Our proposed model performed well on our self-collected dataset and the MODMA datasets with a accuracy of 94.72%, outperforming other methods. Our findings showed that feature dimensionality reduction, weighted fusion, and EEG spatial information all had great effects on depression recognition.