SMMTM: Motor imagery EEG decoding algorithm using a hybrid multi-branch separable convolutional self-attention temporal convolutional network

Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI sig...

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Bibliographic Details
Published in:PloS one Vol. 20; no. 10; p. e0333805
Main Authors: Cao, DianGuo, Yu, ZhenYuan, Wang, Jinqiang, Wu, Yuqiang
Format: Journal Article
Language:English
Published: United States Public Library of Science 23.10.2025
Public Library of Science (PLoS)
Subjects:
Accuracy
Algorithms
Analysis
Biochips
Brain
Brain research
Brain-Computer Interfaces
Classification
Computational linguistics
Computer applications
Convolution
Datasets
Decoding
Deep learning
Electroencephalography - methods
Human-computer interface
Humans
Imagery
Imagination - physiology
Implants
Language processing
Mental task performance
Natural language interfaces
Neural networks
Neural Networks, Computer
Neurology
Prostheses
Rehabilitation
Smart buildings
Technology application
Temporal variations
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Motor imagery (MI) is a brain-computer interface (BCI) technology with the potential to change human life in the future. MI signals have been widely applied in various BCI applications, including neurorehabilitation, smart home control, and prosthetic control. However, the limited accuracy of MI signals decoding remains a significant barrier to the broader growth of the BCI applications. In this study, we propose the SMMTM model, which combines spatiotemporal convolution (SC), multi-branch separable convolution (MSC), multi-head self-attention (MSA), temporal convolution network (TCN), and multimodal feature fusion (MFF). Specifically, we use the SC module to capture both temporal and spatial features. We design a MSC to capture temporal features at multiple scales. In addition, MSA is designed to extract valuable global features with long-term dependence. The TCN is employed to capture higher-level temporal features. The MFF consists of feature fusion and decision fusion, using the features output from the SMMTM to improve robustness. The SMMTM was evaluated on the public benchmark BCI Comparison IV 2a and 2b datasets, the results showed that the within-subject classification accuracies for the datasets were 84.96% and 89.26% respectively, with kappa values of 0.797 and 0.756. The cross-subject classification accuracy for the 2a dataset was 69.21%, with a kappa value of 0.584. These results indicate that the SMMTM significantly enhances decoding performance, providing a strong foundation for advancing practical BCI implementations.
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ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0333805