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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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VG-DOCoT:a novel DO-Conv and transformer framework via VAE-GAN technique for EEG emotion recognition
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 variational automatic encoder-generative adversarial network(VAE-GAN)structures.Specifically,the differential entropy(DE)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 SEED,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.

Emotion recognitionElectroencephalogram(EEG)Depthwise over-parameterized convolutional(DO-Conv)TransformerVariational automatic encoder-generative adversarial network(VAE-GAN)

Yanping ZHU、Lei HUANG、Jixin CHEN、Shenyun WANG、Fayu WAN、Jianan CHEN

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School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China

Emotion recognition Electroencephalogram(EEG) Depthwise over-parameterized convolutional(DO-Conv) Transformer Variational automatic encoder-generative adversarial network(VAE-GAN)

2024

10.1631/FITEE.2300781

信息与电子工程前沿(英文)
浙江大学

信息与电子工程前沿(英文)

CSTPCD
影响因子：0.371
ISSN：2095-9184
年,卷(期)：2024.25(11)