Key result
AB-DPCGRU model outperforms existing methods in EEG-based emotion recognition accuracy.
Why the study?
Most existing EEG emotion classification models extract a single temporal feature while ignoring crucial temporal dynamic information, constraining classification capability.
The proposed AB-DPCGRU model improves EEG-based emotion recognition accuracy compared to existing models.
May advance multi-feature EEG emotion models; leaves open validation in real-world clinical settings.
Studying brain activity and deciphering the information in electroencephalogram (EEG) signals has become an emerging research field, and substantial advances have been made in the EEG-based classification of emotions. However, using different EEG features and complementarity to discriminate other emotions is still challenging. Most existing models extract a single temporal feature from the EEG signal while ignoring the crucial temporal dynamic information, which, to a certain extent, constrains the classification capability of the model. To address this issue, we propose an Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model and validate it with the mixed experiment on the SEED and SEED-IV datasets. The experimental outcomes revealed that the accuracy of the model outperforms the existing state-of-the-art methods, which confirmed the superiority of our approach over currently popular emotion recognition models.
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Ye et al. (2023) studied Emotion recognition. Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model vs. Existing state-of-the-art methods was evaluated on Classification accuracy. The Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model outperformed existing state-of-the-art methods in EEG-based emotion recognition accuracy.
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