Residential College | false |
Status | 已發表Published |
ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement | |
Zehui Wang1; Chuangquan Chen1; Junhua Li1,2; Feng Wan3; Yu Sun4,5; Hongtao Wang1 | |
2023-01-16 | |
Source Publication | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
ISSN | 1534-4320 |
Volume | 31Pages:991-1000 |
Abstract | A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 det-ection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300. |
Keyword | Attention Brain-computer Interfaces (Bcis) Capsule Network P300 |
DOI | 10.1109/TNSRE.2023.3237319 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Engineering ; Rehabilitation |
WOS Subject | Engineering, Biomedical ; Rehabilitation |
WOS ID | WOS:000965423500001 |
Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141 |
Scopus ID | 2-s2.0-85147279004 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING INSTITUTE OF COLLABORATIVE INNOVATION |
Corresponding Author | Yu Sun; Hongtao Wang |
Affiliation | 1.Wuyi University,Faculty of Intelligent Manufacturing,Jiangmen,529020,China 2.University of Essex,School of Computer Science and Electronic Engineering,Colchester,CO4 3SQ,United Kingdom 3.Institute of Collaborative Innovation,University of Macau,Faculty of Science and Engineering,Centre for Cognitive and Brain Sciences,Department of Electrical and Computer Engineering,Macao 4.Zhejiang University,Key Laboratory for Biomedical Engineering,Ministry of Education of China,Department of Biomedical Engineering,Hangzhou,310027,China 5.Zhejiang University School of Medicine,Sir Run Run Shaw Hospital,Department of Neurology,Hangzhou,310020,China |
Recommended Citation GB/T 7714 | Zehui Wang,Chuangquan Chen,Junhua Li,et al. ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31, 991-1000. |
APA | Zehui Wang., Chuangquan Chen., Junhua Li., Feng Wan., Yu Sun., & Hongtao Wang (2023). ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 991-1000. |
MLA | Zehui Wang,et al."ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement".IEEE Transactions on Neural Systems and Rehabilitation Engineering 31(2023):991-1000. |
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