A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition
Abstract Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers’ mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature sub...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-86234-1 |
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| _version_ | 1832594827215110144 |
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| author | Hanying Guo Siying Chen Yongjiang Zhou Ting Xu Yuhao Zhang Hongliang Ding |
| author_facet | Hanying Guo Siying Chen Yongjiang Zhou Ting Xu Yuhao Zhang Hongliang Ding |
| author_sort | Hanying Guo |
| collection | DOAJ |
| description | Abstract Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers’ mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method’s stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention. |
| format | Article |
| id | doaj-art-5212e17c635a4ca5973dc86400cf4de5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-5212e17c635a4ca5973dc86400cf4de52025-01-19T12:19:02ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-86234-1A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognitionHanying Guo0Siying Chen1Yongjiang Zhou2Ting Xu3Yuhao Zhang4Hongliang Ding5School of Automobile and Transportation, Xihua UniversitySchool of Automobile and Transportation, Xihua UniversitySchool of Automobile and Transportation, Xihua UniversitySchool of Automobile and Transportation, Xihua UniversityCollege of Traffic and Transportation, Chongqing Jiaotong UniversityCollege of Smart City and Transportation, Southwest Jiaotong UniversityAbstract Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers’ mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method’s stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.https://doi.org/10.1038/s41598-025-86234-1 |
| spellingShingle | Hanying Guo Siying Chen Yongjiang Zhou Ting Xu Yuhao Zhang Hongliang Ding A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition Scientific Reports |
| title | A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition |
| title_full | A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition |
| title_fullStr | A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition |
| title_full_unstemmed | A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition |
| title_short | A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition |
| title_sort | hybrid critical channels and optimal feature subset selection framework for eeg fatigue recognition |
| url | https://doi.org/10.1038/s41598-025-86234-1 |
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