Emotion recognition based on convolutional gated recurrent units with attention

Abstract

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.

KEYWORDS:

• EEG
• emotion recognition
• attention mechanism
• depthwise parameterised convolutional
• gate recurrent neural unit

Acknowledgement

This work is supported by the National Natural Science Foundation of China under grant number 62272067; the Sichuan Science and Technology Program under Grant Nos. 2023NSFSC0499, 2023YFG0018; the LOST 2030 Brain Project No. 2022ZD0208500; the Scientific Research Foundation of Chengdu University of Information Technology under Grant Nos. KYQN202208, KYQN202206, and the 2011 Collaborative Innovation Center for Image and Geospatial Information of Sichuan Province.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China under grant number 62272067; the Sichuan Science and Technology Program under grant numbers 2023NSFSC0499, 2023YFG0018; the LOST 2030 Brain Project No. 2022ZD0208500; the Scientific Research Foundation of Chengdu University of Information Technology under grant number KYQN202208, KYQN202206, and the 2011 Collaborative Innovation Center for Image and Geospatial Information of Sichuan Province.