Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study.

Electroencephalography (EEG) provides a non-invasive, portable, and cost-effective solution for emotion classification, but existing methods often struggle to capture spatial-spectral dependencies and multi-temporal scale dynamics inherent in EEG signals. This paper proposes a spectral-spatial attention multi-temporal scale network (SSA-MTSNet) to address these challenges. The SSA-MTSNet integrates 3 components: a spectral-spatial attention module, a multi-temporal scale spatio-temporal convolution module, and a long short-term memory (LSTM) module. First, the SSA-MTSNet preserves electrode topology and simultaneously enhances EEG signal frequencies and interactions among brain regions through attention mechanisms. Then, short- and long-term emotional cues are captured using multi-temporal scale convolution, followed by sequence modeling with LSTM. Evaluated on the SEED and SEED-IV datasets, the SSA-MTSNet achieves average accuracies of 98.34% and 91.79% respectively, and 95.13% and 95.30% on the valence and arousal classification tasks of DEAP, respectively. These results suggest that the proposed model achieves competitive performance across different experimental paradigms and emotion labeling strategies by jointly modeling complementary spectral, spatial, and temporal EEG information for emotion recognition.