ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition

doi:10.1016/j.bspc.2023.105422

	ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition
	Fan, Cunhang1 ; Xie, Heng 1; Tao, Jianhua2 ; Li, Yongwei 4; Pei, Guanxiong 3; Li, Taihao 3; Lv, Zhao 1
发表期刊	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN	1746-8094
	2024
卷号	87 页码:9
通讯作者	Tao, Jianhua(jhtao@tsinghua.edu.cn) ; Li, Yongwei(yongwei.li@nlpr.ia.ac.cn) ; Lv, Zhao(kjlz@ahu.edu.cn)
摘要	Electroencephalography (EEG) emotion recognition is an important task for brain-computer interfaces. The time, frequency, and spatial domains of EEG signals have been widely studied. However, these methods often ignore the spatial and temporal correlations in dual modules, resulting in insufficient emotional representations. In this paper, a dual module EEG emotion recognition method based on an improved capsule network and residual Long-Short Term Memory (ResLSTM) is proposed. Using an improved capsule network as the spatial module is more advantageous in learning specific EEG spatial representations. The ResLSTM of the temporal module inherits the information flow from the upper spatial module and conducts complementary learning of the spatiotemporal dual module features through residual connections, thus obtaining more discriminative EEG features and ultimately boosting the classification capabilities of the model. The average accuracy of arousal, valence, and dominance on the DEAP dataset reached 98.06%, 97.94%, and 98.15%, respectively. The DREAMER dataset's average accuracy of arousal, valence, and dominance reached 94.97%, 94.71%, and 94.96%, respectively. The results of our experiments indicate that our method outperforms state-of-the-art approaches.
关键词	Electroencephalogram Emotion recognition Capsule network Residual Long-Short Term Memory
DOI	10.1016/j.bspc.2023.105422
关键词[WOS]	CLASSIFICATION ; DEEP
收录类别	SCI
语种	英语
资助项目	STI[2021ZD0201500] ; National Natural Science Foundation of China (NSFC)[61972437] ; National Natural Science Foundation of China (NSFC)[62201002] ; National Natural Science Foundation of China (NSFC)[62201571] ; Distinguished Youth Foundation of Anhui Scientific Committee[2208085J05] ; Special Fund for Key Program of Science and Technology of Anhui Province[202203a07020008] ; Open Fund of Key Laboratory of Flight Techniques and Flight Safety, CACC[FZ2022KF15] ; Open Research Projects of Zhejiang Lab[2021KH0 AB06] ; Open Projects Program of National Laboratory of Pattern Recognition[202200014]
项目资助者	STI ; National Natural Science Foundation of China (NSFC) ; Distinguished Youth Foundation of Anhui Scientific Committee ; Special Fund for Key Program of Science and Technology of Anhui Province ; Open Fund of Key Laboratory of Flight Techniques and Flight Safety, CACC ; Open Research Projects of Zhejiang Lab ; Open Projects Program of National Laboratory of Pattern Recognition
WOS研究方向	Engineering
WOS类目	Engineering, Biomedical
WOS记录号	WOS:001082097600001
出版者	ELSEVIER SCI LTD
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53000
专题	多模态人工智能系统全国重点实验室
通讯作者	Tao, Jianhua; Li, Yongwei; Lv, Zhao
作者单位	1.Anhui Univ, Sch Comp Sci & Technol, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China 2.Tsinghua Univ, Dept Automat, Beijing, Peoples R China 3.Zhejiang Lab, Artificial Intelligence Res Inst, Hangzhou 311121, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Fan, Cunhang,Xie, Heng,Tao, Jianhua,et al. ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2024,87:9.
APA	Fan, Cunhang.,Xie, Heng.,Tao, Jianhua.,Li, Yongwei.,Pei, Guanxiong.,...&Lv, Zhao.(2024).ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,87,9.
MLA	Fan, Cunhang,et al."ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 87(2024):9.