An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios
SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alte...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10750850/ |
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| author | Junsong Wang Yuntian Cui Hongxin Zhang Haolin Wu Chen Yang |
| author_facet | Junsong Wang Yuntian Cui Hongxin Zhang Haolin Wu Chen Yang |
| author_sort | Junsong Wang |
| collection | DOAJ |
| description | SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability. |
| format | Article |
| id | doaj-art-397ef6d504f846e7a53bf1724dc7119f |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-397ef6d504f846e7a53bf1724dc7119f2024-11-26T00:00:03ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102024-01-01324120413010.1109/TNSRE.2024.349672710750850An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability ScenariosJunsong Wang0https://orcid.org/0009-0005-6884-6189Yuntian Cui1https://orcid.org/0009-0009-8827-5044Hongxin Zhang2https://orcid.org/0000-0003-1865-9519Haolin Wu3https://orcid.org/0009-0006-8472-8489Chen Yang4https://orcid.org/0000-0002-0454-6647School of Electronic Engineering, Beijing University of Post and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Post and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Post and Telecommunications, Beijing, ChinaDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Post and Telecommunications, Beijing, ChinaSSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.https://ieeexplore.ieee.org/document/10750850/Brain-computer interface (BCI)steady-state visual evoked potential (SSVEP)maximum a posteriori (MAP)asynchronousmutual information rate (MIR) |
| spellingShingle | Junsong Wang Yuntian Cui Hongxin Zhang Haolin Wu Chen Yang An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios IEEE Transactions on Neural Systems and Rehabilitation Engineering Brain-computer interface (BCI) steady-state visual evoked potential (SSVEP) maximum a posteriori (MAP) asynchronous mutual information rate (MIR) |
| title | An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios |
| title_full | An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios |
| title_fullStr | An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios |
| title_full_unstemmed | An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios |
| title_short | An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios |
| title_sort | asynchronous training free ssvep bci detection algorithm for non equal prior probability scenarios |
| topic | Brain-computer interface (BCI) steady-state visual evoked potential (SSVEP) maximum a posteriori (MAP) asynchronous mutual information rate (MIR) |
| url | https://ieeexplore.ieee.org/document/10750850/ |
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