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Published on: July 7, 2023
Research on Depression Recognition Based on EEG Microstate Functional Connectivity
Zhiyong Tang1,2, Lingyan Du1,2, Xi Tan3
1School of Automation and Information Engineering, Sichuan University of Science and Engineering, 643000 Zigong, Sichuan, China.
Background:
To examine potential differences in electroencephalogram (EEG) dynamic functional connectivity between patients with major depressive disorder (MDD) and healthy controls (HC), and thereby enhance the effectiveness of depression identification.
Methods:
This study presents a novel approach that combines EEG microstate analysis with functional connectivity networks. Resting-state 19-channel EEG data were obtained from 36 participants (17 healthy controls and 19 patients with depression). Through microstate analysis, significant inter-group differences were observed in the average durations of microstates A and C. Subsequently, EEG segments corresponding to microstate classes A and C were extracted. Following the surface Laplacian transformation, the phase locking value (PLV) was applied to construct functional connectivity networks, and their topological characteristics were extracted. Based on the analysis of network indicators (node degree, clustering coefficient, local efficiency, and global efficiency), global and nodal features showing significant group differences were screened and fused with equal weighting. The classification performance of the fused features and individual features was then assessed using three models: Support Vector Machine (SVM), Backpropagation Neural Network (BP), and K-Nearest Neighbors (KNN).
Results:
The findings indicate that network features derived from microstate C exhibited higher discriminative ability. Across all classification models, node degree features consistently outperformed other individual topological attributes in recognition accuracy, with the KNN model achieving the highest average accuracy of 96.48%. Furthermore, the fused feature set, incorporating more comprehensive EEG information, showed improved classification performance across all models, exceeding the results obtained using any single feature. The average accuracy reached 97.35% under different model configurations.
Conclusions:
Dynamic analysis of brain networks can effectively distinguish patients with depression from healthy controls. This study not only provides a basis for exploring dynamic activities of brain regions associated with depression, but also offers potential objective physiological indicators for disease diagnosis.
