Multi-Channel EEG Emotion Recognition Based on Parallel Transformer and 3D-Convolutional Neural Network

Jie Sun (), Xuan Wang, Kun Zhao, Siyuan Hao and Tianyu Wang
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Jie Sun: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
Xuan Wang: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
Kun Zhao: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
Siyuan Hao: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
Tianyu Wang: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China

Mathematics, 2022, vol. 10, issue 17, 1-15

Abstract: Due to its covert and real-time properties, electroencephalography (EEG) has long been the medium of choice for emotion identification research. Currently, EEG-based emotion recognition focuses on exploiting temporal, spatial, and spatiotemporal EEG data for emotion recognition. Due to the lack of consideration of both spatial and temporal aspects of EEG data, the accuracy of EEG emotion detection algorithms employing solely spatial or temporal variables is low. In addition, approaches that use spatiotemporal properties of EEG for emotion recognition take temporal and spatial characteristics of EEG into account; however, these methods extract temporal and spatial information directly from EEG data. Since there is no reconstruction of the EEG data format, the temporal and spatial properties of the EEG data cannot be extracted efficiently. To address the aforementioned issues, this research proposes a multi-channel EEG emotion identification model based on the parallel transformer and three-dimensional convolutional neural networks (3D-CNN). First, parallel channel EEG data and position reconstruction EEG sequence data are created separately. The temporal and spatial characteristics of EEG are then retrieved using transformer and 3D-CNN models. Finally, the features of the two parallel modules are combined to form the final features for emotion recognition. On the DEAP, Dreamer, and SEED databases, the technique achieved greater accuracy in emotion recognition than other methods. It demonstrates the efficiency of the strategy described in this paper.

Keywords: EEG; transformer; 3D-CNN; feature fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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