Depression detection based on the temporal-spatial-frequency feature fusion of EEG

Depression is a prevalent affective psychiatric disorder projected to be the leading contributor to the world's disease burden by 2030. Due to its high prevalence and low recognition rate, an objective and effective detection method is urgently needed. Deep learning methods based on electroencephalography (EEG) have shown significant potential in depression detection. However, excessive channels can increase redundancy and computational complexity in EEG, while irrelevant channels may reduce accuracy. Additionally, existing models often overlook the complementarity between the temporal-, spatial-, and frequency-domain features of EEG, limiting their detection capabilities. To address these issues, we propose a method that fuses the temporal, spatial, and frequency domain features of EEG to enhance the detection accuracy while eliminating redundant channels. We introduce an EEG channel selection method based on frequency domain weighting that automatically adjusts the channel weights to select the EEG channels that best capture spatial information across the delta, theta, alpha, beta, and gamma bands, thereby optimizing the extraction of spatial-frequency features. In addition, we designed a multiscale spatiotemporal convolutional attention network to extract the spatiotemporal features of EEG. In this network, the multiscale convolutional attention module enhanced the model's ability to capture spatial features, whereas the temporal trend-aware self-attention module extracted long-term temporal features by analyzing global correlations across different time points. Experimental results on the MODMA dataset show that our method achieved a 97.24% detection accuracy, surpassing current state-of-the-art models. This study offers a novel approach for constructing depression detection models, providing a foundation for future research and application.