ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition
文献类型：期刊论文
| 作者 | Fan, Cunhang3 ; Xie, Heng3; Tao, Jianhua4; Li, Yongwei2; Pei, Guanxiong1; Li, Taihao1; Lv, Zhao3
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| 刊名 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL
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| 出版日期 | 2024 |
| 卷号 | 87页码:9 |
| ISSN号 | 1746-8094 |
| 关键词 | Electroencephalogram Emotion recognition Capsule network Residual Long-Short Term Memory |
| DOI | 10.1016/j.bspc.2023.105422 |
| 通讯作者 | Tao, Jianhua(jhtao@tsinghua.edu.cn) ; Li, Yongwei(yongwei.li@nlpr.ia.ac.cn) ; Lv, Zhao(kjlz@ahu.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | CLASSIFICATION ; DEEP |
| 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] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001082097600001 |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52999]  |
| 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.Zhejiang Lab, Artificial Intelligence Res Inst, Hangzhou 311121, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Anhui Univ, Sch Comp Sci & Technol, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China 4.Tsinghua Univ, Dept Automat, Beijing, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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