STKG-TP: Depression recognition via spatial-temporal knowledge graph and trajectory-semantic cross-fusion with EEG signals

Abstract

EEG signals carry important neurocognitive information for depression recognition. However, existing EEG-based depression recognition research faces challenges in addressing semantic interpretability and improving model robustness. Consequently, in this paper, to overcome these challenges, we propose STKG-TP, a novel depression recognition model that integrates Spatiotemporal Knowledge Graphs (STKG) with trajectory-semantic cross-fusion. Specifically, we design an STKG module to learn brain region activation patterns associated with different depressive states and construct a spatiotemporal knowledge graph to enhance the model’s generalization and robustness. In addition, we introduce a Trajectory Prompting module that transforms EEG signal trajectories into a structured semantic library, enabling neurocognitive interpretability at the semantic level. Extensive experimental evaluations on three publicly available EEG datasets demonstrate the superior performance of STKG-TP in addressing these challenges. Compared with existing state-of-the-art depression recognition models, STKG-TP improves Accuracy by 1.13 %, 0.61 %, and 1.29 %, and Kappa score by 3.14 %, 1.88 %, and 2.75 %, respectively. The STKG-TP code is publicly available at: https://github.com/xuxuanya-love/STKG-TP.