DBTSP-net: A temporal-spatial parallel network with optuna optimization for subject-specific motor imagery EEG decoding and visualization
The accuracy and stability of decoding EEG-based motor imagery (MI-EEG) is critical for achieving effective human-machine interaction and promoting motor function recovery in patients with severe motor dysfunction. In this paper, we propose a novel dual-branch temporal–spatial parallel hybrid classi...
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| Published in: | Neurocomputing (Amsterdam) Vol. 660; p. 131858 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
07.01.2026
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| ISSN: | 0925-2312 |
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| Abstract | The accuracy and stability of decoding EEG-based motor imagery (MI-EEG) is critical for achieving effective human-machine interaction and promoting motor function recovery in patients with severe motor dysfunction. In this paper, we propose a novel dual-branch temporal–spatial parallel hybrid classification network named DBTSP-Net. In addition, we introduce an adaptive weighted feature fusion method to decode MI-EEG signals on the basis of the Optuna optimization algorithm. Nine subjects were recruited to participate in the MI-EEG decoding experiment. We evaluated the classification performance of both conventional and state-of-the-art MI-EEG models using the public BCI Competition IV 2a and 2b datasets. The experimental results demonstrated that the classification performance of DBTSP-Net surpassed that of the other baseline methods, attaining average classification accuracies of 79.61 % ± 14.43 and 86.21 % ± 12.17, respectively, with corresponding kappa values of 0.7856 and 0.7189, respectively. We further conducted ablation experiments to verify the rationality of the design of each module. Additionally, EEG topological maps and t-distributed stochastic neighbor embedding (t-SNE) were utilized for feature visualization. The decoding accuracy of MI-EEG signals was increased, and a solid theoretical foundation for the future practical application of MI-BCI systems in motion control and neural rehabilitation training was obtained. The code has been released at https://github.com/xinchenPhD/DBTSPNet. |
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| AbstractList | The accuracy and stability of decoding EEG-based motor imagery (MI-EEG) is critical for achieving effective human-machine interaction and promoting motor function recovery in patients with severe motor dysfunction. In this paper, we propose a novel dual-branch temporal–spatial parallel hybrid classification network named DBTSP-Net. In addition, we introduce an adaptive weighted feature fusion method to decode MI-EEG signals on the basis of the Optuna optimization algorithm. Nine subjects were recruited to participate in the MI-EEG decoding experiment. We evaluated the classification performance of both conventional and state-of-the-art MI-EEG models using the public BCI Competition IV 2a and 2b datasets. The experimental results demonstrated that the classification performance of DBTSP-Net surpassed that of the other baseline methods, attaining average classification accuracies of 79.61 % ± 14.43 and 86.21 % ± 12.17, respectively, with corresponding kappa values of 0.7856 and 0.7189, respectively. We further conducted ablation experiments to verify the rationality of the design of each module. Additionally, EEG topological maps and t-distributed stochastic neighbor embedding (t-SNE) were utilized for feature visualization. The decoding accuracy of MI-EEG signals was increased, and a solid theoretical foundation for the future practical application of MI-BCI systems in motion control and neural rehabilitation training was obtained. The code has been released at https://github.com/xinchenPhD/DBTSPNet. |
| ArticleNumber | 131858 |
| Author | Chen, Xin Du, Mingyu Lin, Hongze Cai, Shibo Bao, Guanjun Wu, Xinyu Yu, Longjie Wei, Wei |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0009-0004-8879-4219 surname: Chen fullname: Chen, Xin organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China – sequence: 2 givenname: Longjie orcidid: 0009-0003-6772-0109 surname: Yu fullname: Yu, Longjie organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China – sequence: 3 givenname: Hongze orcidid: 0009-0002-9555-4948 surname: Lin fullname: Lin, Hongze organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China – sequence: 4 givenname: Mingyu surname: Du fullname: Du, Mingyu organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China – sequence: 5 givenname: Wei orcidid: 0000-0001-8408-3381 surname: Wei fullname: Wei, Wei organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China – sequence: 6 givenname: Xinyu surname: Wu fullname: Wu, Xinyu organization: Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China – sequence: 7 givenname: Guanjun surname: Bao fullname: Bao, Guanjun organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China – sequence: 8 givenname: Shibo surname: Cai fullname: Cai, Shibo email: ccc@zjut.edu.cn organization: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China |
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| Keywords | Motor imagery (MI) Brain-computer interface (BCI) Temporal–Spatial parallel network Transformer Electroencephalogram (EEG) Adaptive weighted feature fusion |
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