Abstract

Info

Title:: Depression detection based on EEG temporal-domain features and brain lateralization difference representations

Author(s):: LI Zheng1; SONG Yu2; 1. Department of Neurology, Tianjin Fifth Central Hospital (Peking University Binhai Hospital), Tianjin 300450, China 2. School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China

Keywords:: Keywords: depression electroencephalogram deep learning spatio-temporal feature learning

Abstract:: Abstract: To address the challenges posed by the complex spatiotemporal coupling of electroencephalogram (EEG) signals, where traditional methods fail to effectively capture lateralization differences and electrode-specific information, which limits the accuracy and interpretability of depression diagnosis, this study proposes a spatiotemporal feature learning network based on MLP-Mixer. A pre-trained MLP-Mixer with shared weights is first adopted to learn temporal context information within each EEG channel. Subsequently, using channel-level temporal features as input, the MLP-Mixer learns differential features between electrode pairs in the left and right hemispheres to characterize lateralization differences and spatial correlations between electrodes. Finally, the model outputs binary classification results for depression diagnosis. Experimental results on the PRED+CT dataset demonstrate that the proposed model achieves an average classification accuracy of 96.14%. Further analysis indicates that dual-band α and β frequency features outperform features from other frequency bands in terms of model performance for depression detection. This finding is consistent with neurophysiological mechanisms, thereby validating the rationality of the models feature extraction mechanism.