Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study
Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Neuroergonomics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnrgo.2022.864938/full |
| _version_ | 1817977246093148160 |
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| author | Takahiko Ogihara Kensuke Tanioka Tomoyuki Hiroyasu Satoru Hiwa |
| author_facet | Takahiko Ogihara Kensuke Tanioka Tomoyuki Hiroyasu Satoru Hiwa |
| author_sort | Takahiko Ogihara |
| collection | DOAJ |
| description | Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving. |
| first_indexed | 2024-04-13T22:13:32Z |
| format | Article |
| id | doaj.art-8bfb3dcdeb5b4db6a9a41ca69e1cea83 |
| institution | Directory Open Access Journal |
| issn | 2673-6195 |
| language | English |
| last_indexed | 2024-04-13T22:13:32Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroergonomics |
| spelling | doaj.art-8bfb3dcdeb5b4db6a9a41ca69e1cea832022-12-22T02:27:37ZengFrontiers Media S.A.Frontiers in Neuroergonomics2673-61952022-07-01310.3389/fnrgo.2022.864938864938Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot StudyTakahiko Ogihara0Kensuke Tanioka1Tomoyuki Hiroyasu2Satoru Hiwa3Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, JapanDepartment of Biomedical Sciences and Informatics, Doshisha University, Kyoto, JapanDepartment of Biomedical Sciences and Informatics, Doshisha University, Kyoto, JapanDepartment of Biomedical Sciences and Informatics, Doshisha University, Kyoto, JapanDistracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.https://www.frontiersin.org/articles/10.3389/fnrgo.2022.864938/fulldistracted drivingfunctional connectivityfunctional near-infrared spectroscopyfunctional brain imagingmind wandering |
| spellingShingle | Takahiko Ogihara Kensuke Tanioka Tomoyuki Hiroyasu Satoru Hiwa Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study Frontiers in Neuroergonomics distracted driving functional connectivity functional near-infrared spectroscopy functional brain imaging mind wandering |
| title | Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study |
| title_full | Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study |
| title_fullStr | Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study |
| title_full_unstemmed | Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study |
| title_short | Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study |
| title_sort | predicting the degree of distracted driving based on fnirs functional connectivity a pilot study |
| topic | distracted driving functional connectivity functional near-infrared spectroscopy functional brain imaging mind wandering |
| url | https://www.frontiersin.org/articles/10.3389/fnrgo.2022.864938/full |
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