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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Vydané v:	Medical & biological engineering & computing Ročník 61; číslo 1; s. 61 - 73
Hlavní autori:	Xu, Guixun, Guo, Wenhui, Wang, Yanjiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023 Springer Nature B.V
Predmet:	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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