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VG-DOCoT: a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition
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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, 1998, -1(-1): .
@article{title="VG-DOCoT: a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition",
author="Yanping ZHU, Lei HUANG, Jixin CHEN, Shenyun WANG, Fayu WAN, Jianan CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300781"
}
%0 Journal Article
%T VG-DOCoT: a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition
%A Yanping ZHU
%A Lei HUANG
%A Jixin CHEN
%A Shenyun WANG
%A Fayu WAN
%A Jianan CHEN
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300781
TY - JOUR
T1 - VG-DOCoT: a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition
A1 - Yanping ZHU
A1 - Lei HUANG
A1 - Jixin CHEN
A1 - Shenyun WANG
A1 - Fayu WAN
A1 - Jianan CHEN
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300781
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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