An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the func...

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Vydáno v:	IEEE transactions on affective computing Ročník 13; číslo 3; s. 1528 - 1540
Hlavní autoři:	Du, Xiaobing, Ma, Cuixia, Zhang, Guanhua, Li, Jinyao, Lai, Yu-Kun, Zhao, Guozhen, Deng, Xiaoming, Liu, Yong-Jin, Wang, Hongan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	attention mechanism Brain modeling Channels Data models domain adaptation Domains Electrodes Electroencephalography Emotion recognition Emotions Feature extraction Frequency-domain analysis LSTM Machine learning multichannel EEG
ISSN:	1949-3045, 1949-3045
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Abstract	Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called AT tention-based LSTM with D omain D iscriminator (ATDD-LSTM), a model based on Long Short-Term Memory (LSTM) for emotion recognition that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.
AbstractList	Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the relation among different EEG electrodes and propose a deep learning method to automatically extract the spatial features that characterize the functional relation between EEG signals at different electrodes. Our proposed deep model is called AT tention-based LSTM with D omain D iscriminator (ATDD-LSTM), a model based on Long Short-Term Memory (LSTM) for emotion recognition that can characterize nonlinear relations among EEG signals of different electrodes. To achieve state-of-the-art emotion recognition performance, the architecture of ATDD-LSTM has two distinguishing characteristics: (1) By applying the attention mechanism to the feature vectors produced by LSTM, ATDD-LSTM automatically selects suitable EEG channels for emotion recognition, which makes the learned model concentrate on the emotion related channels in response to a given emotion; (2) To minimize the significant feature distribution shift between different sessions and/or subjects, ATDD-LSTM uses a domain discriminator to modify the data representation space and generate domain-invariant features. We evaluate the proposed ATDD-LSTM model on three public EEG emotional databases (DEAP, SEED and CMEED) for emotion recognition. The experimental results demonstrate that our ATDD-LSTM model achieves superior performance on subject-dependent (for the same subject), subject-independent (for different subjects) and cross-session (for the same subject) evaluation.
Author	Zhang, Guanhua Lai, Yu-Kun Wang, Hongan Zhao, Guozhen Li, Jinyao Liu, Yong-Jin Ma, Cuixia Du, Xiaobing Deng, Xiaoming
Author_xml	– sequence: 1 givenname: Xiaobing surname: Du fullname: Du, Xiaobing email: duxiaobing16@mails.ucas.ac.cn organization: Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Cuixia orcidid: 0000-0003-3999-7429 surname: Ma fullname: Ma, Cuixia email: cuixia@iscas.ac.cn organization: State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Guanhua surname: Zhang fullname: Zhang, Guanhua email: zgh17@mails.tsinghua.edu.cn organization: Department of Computer Science and Technology, BNRist, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, China – sequence: 4 givenname: Jinyao surname: Li fullname: Li, Jinyao email: lijinyao19@mails.ucas.ac.cn organization: Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Yu-Kun orcidid: 0000-0002-2094-5680 surname: Lai fullname: Lai, Yu-Kun email: laiy4@cardiff.ac.uk organization: School of Computer Science and Informatics, Cardiff University, Cardiff, Wales, U.K – sequence: 6 givenname: Guozhen orcidid: 0000-0003-4438-5320 surname: Zhao fullname: Zhao, Guozhen email: zhaogz@psych.ac.cn organization: CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China – sequence: 7 givenname: Xiaoming surname: Deng fullname: Deng, Xiaoming email: xiaoming@iscas.ac.cn organization: Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, China – sequence: 8 givenname: Yong-Jin orcidid: 0000-0001-5774-1916 surname: Liu fullname: Liu, Yong-Jin email: liuyongjin@tsinghua.edu.cn organization: Department of Computer Science and Technology, BNRist, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, China – sequence: 9 givenname: Hongan surname: Wang fullname: Wang, Hongan email: hongan@iscas.ac.cn organization: State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
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Snippet	Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this article, we study the...
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SubjectTerms	attention mechanism Brain modeling Channels Data models domain adaptation Domains Electrodes Electroencephalography Emotion recognition Emotions Feature extraction Frequency-domain analysis LSTM Machine learning multichannel EEG
Title	An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals
URI	https://ieeexplore.ieee.org/document/9154557 https://www.proquest.com/docview/2709156569
Volume	13
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