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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 PublicationIEEE Transactions on Neural Systems and Rehabilitation Engineering
ISSN1534-4320
Volume31Pages: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.

KeywordAttention Brain-computer Interfaces (Bcis) Capsule Network P300
DOI10.1109/TNSRE.2023.3237319
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Rehabilitation
WOS SubjectEngineering, Biomedical ; Rehabilitation
WOS IDWOS:000965423500001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141
Scopus ID2-s2.0-85147279004
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
INSTITUTE OF COLLABORATIVE INNOVATION
Corresponding AuthorYu Sun; Hongtao Wang
Affiliation1.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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