Fusing spatio-temporal information using supervised local low-rank correlation embedding for depression recognition

Electroencephalography (EEG) signals contain rich spatio-temporal information that reflects the brain's dynamic activity, making it widely used in depression recognition. However, effectively integrating this information to capture discriminative and complementary features remains a key challenge. To address this issue, we propose a novel Discriminative Local Low-Rank Correlation Embedding (DLLCE) to fuse spatio-temporal information of EEG. DLLCE integrates shared low-rank representation, local invariance, discriminative constraints, and enhanced correlation analysis into a unified framework. Specifically, the shared low-rank representation is used to capture the common structural patterns, while the correlation analysis aims to reduce redundancy among feature sets. In addition, the Laplacian regularization is applied to the shared representation to preserve the local geometric structure of the original data. To further enhance discriminative capability, a discriminant graph embedding term is incorporated to exploit label information. Experimental results on EEG datasets demonstrate that DLLCE achieves superior performance compared to existing methods. This work provides new insights into EEG-based mental health assessment and holds promise for early depression diagnosis and clinical decision support.