Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study
Driving fatigue is a common experience for most drivers and can reduce human cognition and judgment abilities. Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to...
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Veröffentlicht in: | IEEE transactions on neural systems and rehabilitation engineering 2023, Vol.31, p.4895-4906 |
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description | Driving fatigue is a common experience for most drivers and can reduce human cognition and judgment abilities. Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasing-increasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy. |
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Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasing-increasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy.</description><identifier>ISSN: 1534-4320</identifier><identifier>ISSN: 1558-0210</identifier><identifier>EISSN: 1558-0210</identifier><identifier>DOI: 10.1109/TNSRE.2023.3339768</identifier><identifier>PMID: 38060360</identifier><identifier>CODEN: ITNSB3</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accidents ; Automobile Driving ; Behavioral sciences ; brain network ; Cluster analysis ; Clustering ; Cognition ; Comparative analysis ; Driver fatigue ; EEG ; Electroencephalography ; Electroencephalography - methods ; Fatigue ; Fatigue - diagnosis ; fatigue self-regulation (FSR) ; Humans ; Indexes ; machine learning ; Neural networks ; Reaction Time - physiology ; Reaction time task ; Self-Control ; Task analysis ; Trends</subject><ispartof>IEEE transactions on neural systems and rehabilitation engineering, 2023, Vol.31, p.4895-4906</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-d811cf2befca2f0a61d174820627c51ecefcdb63bb5a4cc3836e312e884878833</citedby><cites>FETCH-LOGICAL-c462t-d811cf2befca2f0a61d174820627c51ecefcdb63bb5a4cc3836e312e884878833</cites><orcidid>0000-0002-8199-6000 ; 0000-0003-1496-1745 ; 0000-0003-1957-471X ; 0009-0004-0556-8241 ; 0000-0002-6666-8586</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,2096,4010,27900,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38060360$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Gang</creatorcontrib><creatorcontrib>Wang, Jie</creatorcontrib><creatorcontrib>Xu, Wanxiu</creatorcontrib><creatorcontrib>Wu, Kuijun</creatorcontrib><creatorcontrib>Liu, Yufeng</creatorcontrib><creatorcontrib>Bezerianos, Anastasios</creatorcontrib><creatorcontrib>Sun, Yu</creatorcontrib><title>Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study</title><title>IEEE transactions on neural systems and rehabilitation engineering</title><addtitle>TNSRE</addtitle><addtitle>IEEE Trans Neural Syst Rehabil Eng</addtitle><description>Driving fatigue is a common experience for most drivers and can reduce human cognition and judgment abilities. Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasing-increasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy.</description><subject>Accidents</subject><subject>Automobile Driving</subject><subject>Behavioral sciences</subject><subject>brain network</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Cognition</subject><subject>Comparative analysis</subject><subject>Driver fatigue</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Fatigue</subject><subject>Fatigue - diagnosis</subject><subject>fatigue self-regulation (FSR)</subject><subject>Humans</subject><subject>Indexes</subject><subject>machine learning</subject><subject>Neural networks</subject><subject>Reaction Time - physiology</subject><subject>Reaction time task</subject><subject>Self-Control</subject><subject>Task analysis</subject><subject>Trends</subject><issn>1534-4320</issn><issn>1558-0210</issn><issn>1558-0210</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNpdkU1r3DAQhkVpaT7aP1BKMfSSizczki1rewubTRoIacim1wpZGrtebCuV7cD--2g_GkoPQmLmmYcRL2OfEGaIMD9_vFs9LGccuJgJIeaFVG_YMea5SoEjvN2-RZZmgsMROxmGNQAWMi_esyOhQIKQcMx-rait0geqp9aMje-T-9_U-y6ePll2FGpyyeUUmr5O7oNvfb8tXEW0nii5DM1z7HxLLiK8vE4Wvu_JjrE4bpLVOLnNB_auMu1AHw_3Kft5tXxcfE9vf1zfLC5uU5tJPqZOIdqKl1RZwyswEh0WmeIgeWFzJBsbrpSiLHOTWSuUkCSQk1KZKpQS4pTd7L3Om7V-Ck1nwkZ70-hdwYdamzA2tiWN1nICstw4yBREp7OuAJQcKSdho-ts73oK_s9Ew6i7ZrDUtqYnPw2az4HPM1mgjOjX_9C1n0Iff7qjkEd_ESm-p2zwwxCoel0QQW-T1Lsk9TZJfUgyDn05qKeyI_c68je6CHzeAw0R_WMUmZIoxAulFaGW</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Li, Gang</creator><creator>Wang, Jie</creator><creator>Xu, Wanxiu</creator><creator>Wu, Kuijun</creator><creator>Liu, Yufeng</creator><creator>Bezerianos, Anastasios</creator><creator>Sun, Yu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8199-6000</orcidid><orcidid>https://orcid.org/0000-0003-1496-1745</orcidid><orcidid>https://orcid.org/0000-0003-1957-471X</orcidid><orcidid>https://orcid.org/0009-0004-0556-8241</orcidid><orcidid>https://orcid.org/0000-0002-6666-8586</orcidid></search><sort><creationdate>2023</creationdate><title>Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study</title><author>Li, Gang ; 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Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasing-increasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>38060360</pmid><doi>10.1109/TNSRE.2023.3339768</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-8199-6000</orcidid><orcidid>https://orcid.org/0000-0003-1496-1745</orcidid><orcidid>https://orcid.org/0000-0003-1957-471X</orcidid><orcidid>https://orcid.org/0009-0004-0556-8241</orcidid><orcidid>https://orcid.org/0000-0002-6666-8586</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accidents Automobile Driving Behavioral sciences brain network Cluster analysis Clustering Cognition Comparative analysis Driver fatigue EEG Electroencephalography Electroencephalography - methods Fatigue Fatigue - diagnosis fatigue self-regulation (FSR) Humans Indexes machine learning Neural networks Reaction Time - physiology Reaction time task Self-Control Task analysis Trends |
title | Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study |
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