RESUMEN
BACKGROUND: In the realm of brain-computer interfaces (BCI), identifying emotions from electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the intricacy of the signals, and the several channels that make up the signals. NEW METHODS: Using dual-stream structure scaling and multiple attention mechanisms (LDMGEEG), a lightweight network is provided to maximize the accuracy and performance of EEG-based emotion identification. Reducing the number of computational parameters while maintaining the current level of classification accuracy is the aim. This network employs a symmetric dual-stream architecture to assess separately time-domain and frequency-domain spatio-temporal maps constructed using differential entropy features of EEG signals as inputs. RESULT: The experimental results show that after significantly lowering the number of parameters, the model achieved the best possible performance in the field, with a 95.18â¯% accuracy on the SEED dataset. COMPARISON WITH EXISTING METHODS: Moreover, it reduced the number of parameters by 98â¯% when compared to existing models. CONCLUSION: The proposed method distinct channel-time/frequency-space multiple attention and post-attention methods enhance the model's ability to aggregate features and result in lightweight performance.