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On-line Access: 2024-03-17

Received: 2023-11-17

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Frontiers of Information Technology & Electronic Engineering 

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VG-DOCoT: a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition


Author(s):  Yanping ZHU, Lei HUANG, Jixin CHEN, Shenyun WANG, Fayu WAN, Jianan CHEN

Affiliation(s):  Nanjing University of Information Science and Technology, School of Electronic and Information Engineering, Nanjing 210044, China

Corresponding email(s):  001520@nuist.edu.cn, 20211249221@nuist.edu.cn, 202212490689@nuist.edu.cn, wangsy2006@126.com, 002470@nuist.edu.cn, 202212490688@nuist.edu.cn

Key Words:  Emotion recognition; EEG; Depthwise over-parameterized convolutional (DO-Conv); Transformer; VAE-GAN


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Yanping ZHU, Lei HUANG, Jixin CHEN, Shenyun WANG, Fayu WAN, Jianan CHEN. VG-DOCoT: a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2300781

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Abstract: 
Human emotions are intricate psychological phenomena that reflect an individual's current physiological and psychological state. Emotions have a pronounced influence on human behavior, cognition, communication, and decision-making. However, current emotion recognition methods often suffer from suboptimal performance and limited scalability in practical applications. To solve this problem, a novel electroencephalogram (EEG) emotion recognition network named VG-DOCoT is proposed, which is based on depthwise over-parameterized convolutional (DO-Conv), Transformer, and VAE-GAN structures. Specifically, the differential entropy can be extracted from EEG signals to create mappings into the temporal, spatial, and frequency information in preprocessing. To enhance the training data, VAE-GAN is employed for data augmentation. A novel convolution module DO-Conv is used to replace the traditional convolution layer to improve the network. A Transformer structure is introduced into the network framework to reveal the global dependencies from EEG signals. Using the proposed model, a binary classification on the DEAP dataset is carried out, which achieves an accuracy of 92.52% for arousal and 92.27% for valence. Next, a ternary classification is conducted on the SEED dataset, which classifies neutral, positive, and negative emotions; an impressive average prediction accuracy of 93.77% is obtained. The proposed method significantly improves the accuracy for EEG-based emotion recognition.

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