Depression is a prevalent mental health disorder characterized by persistent sadness and a diminished interest in daily activities, early detection of depression facilitates timely intervention, mitigating its adverse effects. Electroencephalography (EEG) signals and eye movements are emerging as promising biomarkers for depression detection due to their non-invasive nature and cost-effectiveness. Nevertheless, existing studies suffer from methodological constraints, including low specificity, insufficient sample sizes, limited generalizability, and difficulties in large-scale replication, which collectively undermine their clinical utility. To address these challenges, we collected a large-scale depression dataset comprising EEG and eye movements from 1,060 individuals diagnosed with depression and 1,308 healthy controls. To efficiently leverage multimodal data for automatic depression detection, we propose the EEG-Eye Movements Model (E2Mo). E2Mo employs modality-specific encoders to extract discriminative multi-view features from each modality and incorporates a mixture-of-modality-experts architecture with multi pretraining tasks to achieve efficient and robust modality alignment and fusion. Our approach achieves a 70.06% balanced accuracy by leveraging multi-modal data, demonstrating the effectiveness of integrating EEG signals and eye movements for automatic depression detection.

Meditation has been shown to improve mental health by reducing stress and enhancing cognitive functions. To monitor meditation quality, Electroencephalogram (EEG) offers an objective alternative to self-evaluation questionnaires, which suffer from subjectivity and delays. However, previous EEG protocols often inflated classification performance due to temporal correlation and auditory interference. This study introduces a new data acquisition protocol to address these issues. We constructed a dataset from 32 subjects, each participating on three sessions in different days to eliminate temporal interference. During each session, subjects alternated between audio-guided meditation and silent unguided meditation. Using the Multi-view Spectral-Spatial-Temporal Masked Autoencoder (MV-SSTMA) model which was pre-trained on the Emotion EEG dataset SEED, the model achieved superior classification performance in F1 score, accuracy, precision, and recall. These findings offer valuable insights for improving EEG-based meditation monitoring in biomedical applications.