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An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals
Du, Xiaobing1,2; Ma, Cuixia2,3,4; Zhang, Guanhua5; Li, Jinyao1,2; Lai, Yu-Kun5; Zhao, Guozhen6,7; Deng, Xiaoming1,2; Liu, Yong-Jin5; Wang, Hongan2,3,4
First AuthorDu, Xiaobing
Contribution Rank6
Abstract

Most previous EEG-based emotion recognition methods studied hand-crafted EEG features extracted from different electrodes. In this paper, 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 ATtention-based LSTM with Domain Discriminator (ATDD-LSTM) 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.

2020
Language英语
DOI10.1109/TAFFC.2020.3013711
Source PublicationIEEE Transactions on Affective Computing
Subtype综述
Indexed BySCI
WoS QuartileQ1
QuartileQ1
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/39338
Collection中国科学院行为科学重点实验室
Corresponding AuthorMa, Cuixia; Liu, Yong-Jin
Affiliation1.Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing, China
3.State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
4.Beijing Key Laboratory of Human Computer Interactions, International Joint Laboratory of artificial intelligence and emotional interaction, Beijing 100190, China
5.BNRist, MOE-Key Laboratory of Pervasive Computing, the Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
6.CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
7.Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Du, Xiaobing,Ma, Cuixia,Zhang, Guanhua,et al. An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals[J]. IEEE Transactions on Affective Computing,2020.
APA Du, Xiaobing.,Ma, Cuixia.,Zhang, Guanhua.,Li, Jinyao.,Lai, Yu-Kun.,...&Wang, Hongan.(2020).An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals.IEEE Transactions on Affective Computing.
MLA Du, Xiaobing,et al."An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals".IEEE Transactions on Affective Computing (2020).
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