Research on auditory and olfactory regulation methods for abnormal driver emotions based on EEG signals
In sudden and dangerous traffic situations, drivers are susceptible to abnormal emotional states, such as tension and anger, which can significantly increase safety risks while driving. Electroencephalography (EEG) signals, being an objective measure of emotional states, offer valuable insights for...
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Published in | Frontiers in human neuroscience Vol. 19; p. 1615346 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Switzerland
Frontiers Media S.A
16.06.2025
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Online Access | Get full text |
ISSN | 1662-5161 1662-5161 |
DOI | 10.3389/fnhum.2025.1615346 |
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Abstract | In sudden and dangerous traffic situations, drivers are susceptible to abnormal emotional states, such as tension and anger, which can significantly increase safety risks while driving. Electroencephalography (EEG) signals, being an objective measure of emotional states, offer valuable insights for identifying and regulating these emotions.
This study collected EEG data from 54 drivers in a simulated driving environment, resulting in a total of 1,260 samples, and developed a recognition model for abnormal emotions-specifically tension and anger-based on the EEG signals. Time-frequency domain features, including mean, variance, skewness, kurtosis, root mean square, and power spectral density, were extracted and analyzed using classification algorithms such as Back Propagation Neural Networks (BPNN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), enabling precise identification of varying levels of tension and anger. Furthermore, the study assessed the effects of music, fragrance, and their combined application on alleviating these abnormal emotional states.
Results indicated that music, fragrance, and their combination were related to a reduction in stress and anger across different severity levels, with subjective assessments correlating well with the objective EEG data. Notably, music regulation was found to be most effective for mild and moderate tension, reducing tension levels by 63.33% and 68.75%, respectively, whereas fragrance was more efficacious in high tension situations, achieving a 43% reduction. For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. Additionally, an analysis of time-domain features utilizing Hjorth parameters revealed that the application of a single fragrance was most effective for alleviating tension, while a singular music regulation strategy demonstrated superior performance in calming anger.
The reliability of both the abnormal emotion recognition model and the emotion regulation assessment system was validated through the study. These findings contribute valuable scientific evidence for the management of drivers' emotions and suggest promising avenues for optimizing personalized emotional regulation strategies in the future. |
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AbstractList | In sudden and dangerous traffic situations, drivers are susceptible to abnormal emotional states, such as tension and anger, which can significantly increase safety risks while driving. Electroencephalography (EEG) signals, being an objective measure of emotional states, offer valuable insights for identifying and regulating these emotions.IntroductionIn sudden and dangerous traffic situations, drivers are susceptible to abnormal emotional states, such as tension and anger, which can significantly increase safety risks while driving. Electroencephalography (EEG) signals, being an objective measure of emotional states, offer valuable insights for identifying and regulating these emotions.This study collected EEG data from 54 drivers in a simulated driving environment, resulting in a total of 1,260 samples, and developed a recognition model for abnormal emotions-specifically tension and anger-based on the EEG signals. Time-frequency domain features, including mean, variance, skewness, kurtosis, root mean square, and power spectral density, were extracted and analyzed using classification algorithms such as Back Propagation Neural Networks (BPNN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), enabling precise identification of varying levels of tension and anger. Furthermore, the study assessed the effects of music, fragrance, and their combined application on alleviating these abnormal emotional states.MethodsThis study collected EEG data from 54 drivers in a simulated driving environment, resulting in a total of 1,260 samples, and developed a recognition model for abnormal emotions-specifically tension and anger-based on the EEG signals. Time-frequency domain features, including mean, variance, skewness, kurtosis, root mean square, and power spectral density, were extracted and analyzed using classification algorithms such as Back Propagation Neural Networks (BPNN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), enabling precise identification of varying levels of tension and anger. Furthermore, the study assessed the effects of music, fragrance, and their combined application on alleviating these abnormal emotional states.Results indicated that music, fragrance, and their combination were related to a reduction in stress and anger across different severity levels, with subjective assessments correlating well with the objective EEG data. Notably, music regulation was found to be most effective for mild and moderate tension, reducing tension levels by 63.33% and 68.75%, respectively, whereas fragrance was more efficacious in high tension situations, achieving a 43% reduction. For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. Additionally, an analysis of time-domain features utilizing Hjorth parameters revealed that the application of a single fragrance was most effective for alleviating tension, while a singular music regulation strategy demonstrated superior performance in calming anger.ResultsResults indicated that music, fragrance, and their combination were related to a reduction in stress and anger across different severity levels, with subjective assessments correlating well with the objective EEG data. Notably, music regulation was found to be most effective for mild and moderate tension, reducing tension levels by 63.33% and 68.75%, respectively, whereas fragrance was more efficacious in high tension situations, achieving a 43% reduction. For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. Additionally, an analysis of time-domain features utilizing Hjorth parameters revealed that the application of a single fragrance was most effective for alleviating tension, while a singular music regulation strategy demonstrated superior performance in calming anger.The reliability of both the abnormal emotion recognition model and the emotion regulation assessment system was validated through the study. These findings contribute valuable scientific evidence for the management of drivers' emotions and suggest promising avenues for optimizing personalized emotional regulation strategies in the future.DiscussionThe reliability of both the abnormal emotion recognition model and the emotion regulation assessment system was validated through the study. These findings contribute valuable scientific evidence for the management of drivers' emotions and suggest promising avenues for optimizing personalized emotional regulation strategies in the future. In sudden and dangerous traffic situations, drivers are susceptible to abnormal emotional states, such as tension and anger, which can significantly increase safety risks while driving. Electroencephalography (EEG) signals, being an objective measure of emotional states, offer valuable insights for identifying and regulating these emotions. This study collected EEG data from 54 drivers in a simulated driving environment, resulting in a total of 1,260 samples, and developed a recognition model for abnormal emotions-specifically tension and anger-based on the EEG signals. Time-frequency domain features, including mean, variance, skewness, kurtosis, root mean square, and power spectral density, were extracted and analyzed using classification algorithms such as Back Propagation Neural Networks (BPNN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), enabling precise identification of varying levels of tension and anger. Furthermore, the study assessed the effects of music, fragrance, and their combined application on alleviating these abnormal emotional states. Results indicated that music, fragrance, and their combination were related to a reduction in stress and anger across different severity levels, with subjective assessments correlating well with the objective EEG data. Notably, music regulation was found to be most effective for mild and moderate tension, reducing tension levels by 63.33% and 68.75%, respectively, whereas fragrance was more efficacious in high tension situations, achieving a 43% reduction. For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. Additionally, an analysis of time-domain features utilizing Hjorth parameters revealed that the application of a single fragrance was most effective for alleviating tension, while a singular music regulation strategy demonstrated superior performance in calming anger. The reliability of both the abnormal emotion recognition model and the emotion regulation assessment system was validated through the study. These findings contribute valuable scientific evidence for the management of drivers' emotions and suggest promising avenues for optimizing personalized emotional regulation strategies in the future. IntroductionIn sudden and dangerous traffic situations, drivers are susceptible to abnormal emotional states, such as tension and anger, which can significantly increase safety risks while driving. Electroencephalography (EEG) signals, being an objective measure of emotional states, offer valuable insights for identifying and regulating these emotions.MethodsThis study collected EEG data from 54 drivers in a simulated driving environment, resulting in a total of 1,260 samples, and developed a recognition model for abnormal emotions—specifically tension and anger—based on the EEG signals. Time-frequency domain features, including mean, variance, skewness, kurtosis, root mean square, and power spectral density, were extracted and analyzed using classification algorithms such as Back Propagation Neural Networks (BPNN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), enabling precise identification of varying levels of tension and anger. Furthermore, the study assessed the effects of music, fragrance, and their combined application on alleviating these abnormal emotional states.ResultsResults indicated that music, fragrance, and their combination were related to a reduction in stress and anger across different severity levels, with subjective assessments correlating well with the objective EEG data. Notably, music regulation was found to be most effective for mild and moderate tension, reducing tension levels by 63.33% and 68.75%, respectively, whereas fragrance was more efficacious in high tension situations, achieving a 43% reduction. For anger, fragrance regulation proved more beneficial for mild and moderate anger (reducing anger by 66.67 and 73.75%, respectively), while music regulation was most effective in mitigating high anger levels, resulting in a 58% reduction. Additionally, an analysis of time-domain features utilizing Hjorth parameters revealed that the application of a single fragrance was most effective for alleviating tension, while a singular music regulation strategy demonstrated superior performance in calming anger.DiscussionThe reliability of both the abnormal emotion recognition model and the emotion regulation assessment system was validated through the study. These findings contribute valuable scientific evidence for the management of drivers’ emotions and suggest promising avenues for optimizing personalized emotional regulation strategies in the future. |
Author | Wu, Yingzhang Tang, Bangbei Yue, Qizong Li, Yilun Li, Yan |
AuthorAffiliation | 1 School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences , Chongqing , China 4 School of Music and Dance, Henan Institute of Science and Technology , Xinxiang , China 3 School of Vehicle and Mobility, Tsinghua University , Chongqing , China 2 Department of Physiology, Army Medical University , Chongqing , China 5 China Music Mental Health Institute, Southwest University , Chongqing , China |
AuthorAffiliation_xml | – name: 4 School of Music and Dance, Henan Institute of Science and Technology , Xinxiang , China – name: 5 China Music Mental Health Institute, Southwest University , Chongqing , China – name: 2 Department of Physiology, Army Medical University , Chongqing , China – name: 3 School of Vehicle and Mobility, Tsinghua University , Chongqing , China – name: 1 School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences , Chongqing , China |
Author_xml | – sequence: 1 givenname: Bangbei surname: Tang fullname: Tang, Bangbei – sequence: 2 givenname: Yan surname: Li fullname: Li, Yan – sequence: 3 givenname: Yingzhang surname: Wu fullname: Wu, Yingzhang – sequence: 4 givenname: Yilun surname: Li fullname: Li, Yilun – sequence: 5 givenname: Qizong surname: Yue fullname: Yue, Qizong |
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Cites_doi | 10.3390/sci5020023 10.3389/fpsyg.2022.919695 10.1016/j.aap.2020.105516 10.1038/s41598-024-78156-1 10.1063/1.5139383 10.1109/JSEN.2023.3339727 10.1109/ACCESS.2019.2934018 10.3390/brainsci14070721 10.1108/09590550910941535 10.3390/ijerph15040611 10.1109/IJCNN.2009.5178854 10.1177/030573561773462 10.1016/j.neucli.2016.07.002 10.22034/IJHCUM.2024.04.05 10.1016/j.eswa.2021.114952 10.15666/aeer/1704_90839095 10.5117/9789087283209 10.1016/j.heliyon.2024.e38941 10.1109/IES59143.2023.10242538 10.1155/2021/5497081 10.1016/j.aap.2020.105923 10.1016/j.neuroimage.2019.06.046 10.1016/j.bspc.2022.103966 10.1016/j.measurement.2022.111738 10.20982/tqmp.05.2.p068 10.1080/01441647.2022.2100943 10.1109/TITS.2018.2868499 10.1007/s12144-022-03259-9 10.1016/j.bspc.2021.102648 10.3389/fpsyg.2022.1014202 10.1016/j.ijinfomgt.2023.102715 10.1037/bul0000096 10.1016/j.tust.2024.105912 10.3389/fnins.2018.00162 10.1109/TIV.2022.3195635 10.1073/pnas.2308859121 10.20959/wjpr20244-31747 10.1016/j.eswa.2015.10.049 10.3233/FAIA220517 10.3389/fpsyg.2024.1391204 10.3390/app13010100 10.1371/journal.pone.0067347 10.1016/j.trf.2022.01.010 10.1016/j.scitotenv.2023.168785 10.4103/nah.nah_5_24 10.1109/TIV.2023.3339673 |
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Keywords | emotional regulation music intervention brain driving emotions fragrance intervention |
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Title | Research on auditory and olfactory regulation methods for abnormal driver emotions based on EEG signals |
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