|MTLFuseNet: A novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning|
|Li, R(李睿); Ren, C(任超); Ge, Yiqing; Zhao, Qiqi; Yang, Yikun; Shi, Yuhan; Zhang, XW(张晓炜); Hu, B(胡斌)|
|Online publication date||2023-07
|Source Publication||KNOWLEDGE-BASED SYSTEMS
Impact Factor & Quartile|
|Abstract||How to extract discriminative latent feature representations from electroencephalography (EEG) signals and build a generalized model is a topic in EEG-based emotion recognition research. This study proposed a novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning, referred to as MTLFuseNet. MTLFuseNet learned spatio-temporal latent features of EEG in an unsupervised manner by a variational autoencoder (VAE) and learned the spatio-spectral features of EEG in a supervised manner by a graph convolutional network (GCN) and gated recurrent unit (GRU) network. Afterward, the two latent features were fused to form more complementary and discriminative spatio-temporal–spectral fusion features for EEG signal representation. In addition, MTLFuseNet was constructed based on multi-task learning. The focal loss was introduced to solve the problem of unbalanced sample classes in an emotional dataset, and the triplet-center loss was introduced to make the fused latent feature vectors more discriminative. Finally, a subject-independent leave-one-subject-out cross-validation strategy was used to validate extensively on two public datasets, DEAP and DREAMER. On the DEAP dataset, the average accuracies of valence and arousal are 71.33% and 73.28%, respectively. On the DREAMER dataset, the average accuracies of valence and arousal are 80.43% and 83.33%, respectively. The experimental results show that the proposed MTLFuseNet model achieves excellent recognition performance, outperforming the state-of-the-art methods. © 2023 Elsevier B.V.|
|Keyword||Biomedical signal processing
|WOS Research Area||Computer Science
|WOS Subject||Computer Science, Artificial Intelligence
|EI Accession Number||20232914417908
|EI Keywords||Emotion Recognition
|EI Classification Number||461.1 Biomedical Engineering
; 461.4 Ergonomics and Human Factors Engineering
; 461.6 Medicine and Pharmacology
; 716.1 Information Theory and Signal Processing
; 723.2 Data Processing and Image Processing
; 751.5 Speech
|Original Document Type||Journal article (JA)
|Corresponding Author||Ren, Chao; Hu, Bin|
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou; 730000, China
|First Author Affilication||School of Information Science and Engineering
|Corresponding Author Affilication||School of Information Science and Engineering
Li, Rui,Ren, Chao,Ge, Yiqing,et al. MTLFuseNet: A novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning[J].
Li, Rui.,Ren, Chao.,Ge, Yiqing.,Zhao, Qiqi.,Yang, Yikun.,...&Hu, Bin.(2023).MTLFuseNet: A novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning.KNOWLEDGE-BASED SYSTEMS,276.
Li, Rui,et al."MTLFuseNet: A novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning".KNOWLEDGE-BASED SYSTEMS 276(2023).
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