AUTHOR=Wang Zhuozheng , Hu Chenyang , Liu Wei , Zhou Xiaofan , Zhao Xixi 

TITLE=EEG-based high-performance depression state recognition

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1301214

DOI=10.3389/fnins.2023.1301214

ISSN=1662-453X

ABSTRACT=Depression is a global disease that dose serious harm 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 spatio-temporal information interactions, which will reduce performance. Thus, a more accurate and objective method for depression identification is urgently needed. In this work, the Resting-state EEG of MODMA and self-collecting data was used in this study. We censored 6 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. We proposed model W-GCN-GRU having a good performance, showing the accuracy of 94.72% for recognition of depression and normal. Besides, our method significantly outperformed other methods. Our findings showed that feature dimensionality reduction, weighted fusion and EEG spatial information all had great effects on depression recognition.