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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
第一作者Du, Xiaobing
心理所单位排序6
摘要

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.

关键词Electroencephalography Feature extraction Brain modeling Emotion recognition Electrodes Data models Frequency-domain analysis Emotion recognition multichannel EEG LSTM attention mechanism domain adaptation
2020
语种英语
DOI10.1109/TAFFC.2020.3013711
发表期刊IEEE Transactions on Affective Computing
ISSN1949-3045
卷号13期号:3页码:1528-1540
期刊论文类型综述
收录类别SCI
资助项目National Key Research and Development Program of China[2016YFB1001200] ; Natural Science Foundation of China[U1736220] ; Natural Science Foundation of China[61725204] ; Natural Science Foundation of China[61872346]
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词ASYMMETRY
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000849263500029
WOS分区Q1
Q分类Q1
资助机构National Key Research and Development Program of China ; Natural Science Foundation of China
引用统计
被引频次:79[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/39338
专题中国科学院行为科学重点实验室
通讯作者Ma, Cuixia; Liu, Yong-Jin
作者单位1.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
推荐引用方式
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,2020,13(3):1528-1540.
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,13(3),1528-1540.
MLA Du, Xiaobing,et al."An Efficient LSTM Network for Emotion Recognition from Multichannel EEG Signals".IEEE Transactions on Affective Computing 13.3(2020):1528-1540.
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