Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition

Electroencephalography (EEG) signals contain rich spatiotemporal information reflecting brain activity, making them valuable for analyzing cognitive, emotional, and neurological disorders. However, effectively integrating these two types of information to capture both discriminative and complementary features remains a significant challenge. To address this, we propose a Graph-Enhanced Dual Low-Rank Correlation Embedding (GEDLCE) method, which integrates spatiotemporal EEG features to improve depression recognition. GEDLCE enforces low-rank constraints at both feature and sample levels, enabling extraction of shared latent factors across multiple feature sets. To preserve the intrinsic geometric structure of the data, GEDLCE employs two graph Laplacian terms to model local relationships in the sample space. Furthermore, GEDLCE introduces a graph embedding term that utilizes label information to enhance its discriminative capability. In addition, GEDLCE incorporates an enhanced correlation analysis to exploit inter-view correlations while reducing intra-view redundancy. Finally, GEDLCE jointly optimizes low-rank representations, correlation constraints, and graph embedding within a unified framework. Experiments on EEG datasets show that GEDLCE effectively captures critical information, achieves superior performance in depression recognition, and shows promise for early diagnosis and disease monitoring.