Classification of Five Control/Non-control States to Enhance Asynchronous Brain-Computer Interfaces
Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theo...
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| Vydané v: | WRC Symposium on Advanced Robotics and Automation (Online) s. 51 - 54 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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IEEE
23.08.2024
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| ISSN: | 2835-3358 |
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| Abstract | Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theory-based decision fusion (DST-DF) method integrating decisions from two asynchronous algorithms for improved classification accuracy. An individualized space-frequency based complex network (ISF-OCN) algorithm and an Irregular-resampling auto-spectral analysis (IRASA) algorithm were used to extract control/non-control features for classification. The DST-DF method constructed the basic probability assignment (BPA) functions for them and assigned weights considering their varied importance in decision making. The weights were optimized using a single objective Bayesian optimization method, and were assigned to the decisions of ISF-OCN and IRASA for D-S fusion. The experimental results of 10 subjects showed that the proposed method obtained higher accuracy (93.92%) by integrating the ISF-OCN (78.87%) and IRASA (91.20%) algorithms. |
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| AbstractList | Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theory-based decision fusion (DST-DF) method integrating decisions from two asynchronous algorithms for improved classification accuracy. An individualized space-frequency based complex network (ISF-OCN) algorithm and an Irregular-resampling auto-spectral analysis (IRASA) algorithm were used to extract control/non-control features for classification. The DST-DF method constructed the basic probability assignment (BPA) functions for them and assigned weights considering their varied importance in decision making. The weights were optimized using a single objective Bayesian optimization method, and were assigned to the decisions of ISF-OCN and IRASA for D-S fusion. The experimental results of 10 subjects showed that the proposed method obtained higher accuracy (93.92%) by integrating the ISF-OCN (78.87%) and IRASA (91.20%) algorithms. |
| Author | Zhang, Qian Li, Jiaxin Wang, Hantao Zhao, Jing Liu, Xueshuo |
| Author_xml | – sequence: 1 givenname: Xueshuo surname: Liu fullname: Liu, Xueshuo email: 1120126113@qq.com organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004 – sequence: 2 givenname: Qian surname: Zhang fullname: Zhang, Qian email: zhangqian0321@163.com organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004 – sequence: 3 givenname: Jiaxin surname: Li fullname: Li, Jiaxin email: ljx0508lim@163.com organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004 – sequence: 4 givenname: Hantao surname: Wang fullname: Wang, Hantao email: 1758400205@qq.com organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004 – sequence: 5 givenname: Jing surname: Zhao fullname: Zhao, Jing email: zhaoj0104@ysu.edu.cn organization: Yanshan University,Department of Electrical Engineering,Qinhuangdao,China,066004 |
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| Snippet | Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown... |
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| SubjectTerms | Accuracy Brain-computer interfaces Complex networks Decision making Electroencephalography Feature extraction Optimization methods |
| Title | Classification of Five Control/Non-control States to Enhance Asynchronous Brain-Computer Interfaces |
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