Subject-independent EEG emotion recognition with hybrid spatio-temporal GRU-Conv architecture

Recently, various deep learning frameworks have shown excellent performance in decoding electroencephalogram (EEG) signals, especially in human emotion recognition. However, most of them just focus on temporal features and ignore the features based on spatial dimensions. Traditional gated recurrent...

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Vydáno v:Medical & biological engineering & computing Ročník 61; číslo 1; s. 61 - 73
Hlavní autoři: Xu, Guixun, Guo, Wenhui, Wang, Yanjiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023
Springer Nature B.V
Témata:
Arousal
Artificial neural networks
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Applications
Databases, Factual
Deep learning
EEG
Electroencephalography
Emotion recognition
Emotions
Feature extraction
Human Physiology
Humans
hybrids
Imaging
Information processing
Machine learning
Neural networks
Neural Networks, Computer
Original Article
Radiology
Spatial data
Temporal variations
Time Factors
time series analysis
Multi-level feature fusion
EEG emotion recognition
Spatio-semporal information
Subject-independent
GRU-Conv
ISSN:0140-0118, 1741-0444, 1741-0444
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Popis
Shrnutí:Recently, various deep learning frameworks have shown excellent performance in decoding electroencephalogram (EEG) signals, especially in human emotion recognition. However, most of them just focus on temporal features and ignore the features based on spatial dimensions. Traditional gated recurrent unit (GRU) model performs well in processing time series data, and convolutional neural network (CNN) can obtain spatial characteristics from input data. Therefore, this paper introduces a hybrid GRU and CNN deep learning framework named GRU-Conv to fully leverage the advantages of both. Nevertheless, contrary to most previous GRU architectures, we retain the output information of all GRU units. So, the GRU-Conv model could extract crucial spatio-temporal features from EEG data. And more especially, the proposed model acquires the multi-dimensional features of multi-units after temporal processing in GRU and then uses CNN to extract spatial information from the temporal features. In this way, the EEG signals with different characteristics could be classified more accurately. Finally, the subject-independent experiment shows that our model has good performance on SEED and DEAP databases. The average accuracy of the former is 87.04%. The mean accuracy of the latter is 70.07% for arousal and 67.36% for valence. Graphical abstract
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ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-022-02686-x