基于动态有向时空图的EEG情绪识别方法OA

Electroencephalogram(EEG)emotion recognition holds significant applications value in human-computer interaction and diagnosis and treatment of emotional disorders.However,existing methods for characterizing brain emotional activities have limitations in fully exploring the dynamic and directed spatial-temporal features within EEG.To address the insufficient modeling of dynamic topological relationships among brain regions in EEG-based emotion recognition studies,this research proposes a Dynamic Directed Spatial-Temporal Graph Network.This innovative approach constructs dynamic directed spatial-temporal graphs to capture the dynamic changes in topological connections between EEG channels during emotion induction processes,thereby fully exploiting spatial-temporal features.Experiments evaluations on the SEED and SEED-Ⅳ public datasets demonstrate that the proposed method outperforming state-of-the-art models,indicating its significant advantages in EEG emotion recognition tasks.