{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:20:28Z","timestamp":1772173228414,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,8,26]],"date-time":"2016-08-26T00:00:00Z","timestamp":1472169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Driving anger, called \u201croad rage\u201d, has become increasingly common nowadays, affecting road safety. A few researches focused on how to identify driving anger, however, there is still a gap in driving anger grading, especially in real traffic environment, which is beneficial to take corresponding intervening measures according to different anger intensity. This study proposes a method for discriminating driving anger states with different intensity based on Electroencephalogram (EEG) spectral features. First, thirty drivers were recruited to conduct on-road experiments on a busy route in Wuhan, China where anger could be inducted by various road events, e.g., vehicles weaving\/cutting in line, jaywalking\/cyclist crossing, traffic congestion and waiting red light if they want to complete the experiments ahead of basic time for extra paid. Subsequently, significance analysis was used to select relative energy spectrum of \u03b2 band (\u03b2%) and relative energy spectrum of \u03b8 band (\u03b8%) for discriminating the different driving anger states. Finally, according to receiver operating characteristic (ROC) curve analysis, the optimal thresholds (best cut-off points) of \u03b2% and \u03b8% for identifying none anger state (i.e., neutral) were determined to be 0.2183 \u2264 \u03b8% &lt; 1, 0 &lt; \u03b2% &lt; 0.2586; low anger state is 0.1539 \u2264 \u03b8% &lt; 0.2183, 0.2586 \u2264 \u03b2% &lt; 0.3269; moderate anger state is 0.1216 \u2264 \u03b8% &lt; 0.1539, 0.3269 \u2264 \u03b2% &lt; 0.3674; high anger state is 0 &lt; \u03b8% &lt; 0.1216, 0.3674 \u2264 \u03b2% &lt; 1. Moreover, the discrimination performances of verification indicate that, the overall accuracy (Acc) of the optimal thresholds of \u03b2% for discriminating the four driving anger states is 80.21%, while 75.20% for that of \u03b8%. The results can provide theoretical foundation for developing driving anger detection or warning devices based on the relevant optimal thresholds.<\/jats:p>","DOI":"10.3390\/info7030052","type":"journal-article","created":{"date-parts":[[2016,8,26]],"date-time":"2016-08-26T09:58:48Z","timestamp":1472205528000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimal Threshold Determination for Discriminating Driving Anger Intensity Based on EEG Wavelet Features and ROC Curve Analysis"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2791-5169","authenticated-orcid":false,"given":"Ping","family":"Wan","sequence":"first","affiliation":[{"name":"Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"},{"name":"Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China"},{"name":"Intelligent Human-Machine Systems Laboratory, Northeastern University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaozhong","family":"Wu","sequence":"additional","affiliation":[{"name":"Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"},{"name":"Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingzi","family":"Lin","sequence":"additional","affiliation":[{"name":"Intelligent Human-Machine Systems Laboratory, Northeastern University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Ma","sequence":"additional","affiliation":[{"name":"Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"},{"name":"Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,26]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"Review on the study of motorists\u2019 driving anger","volume":"20","author":"Wu","year":"2010","journal-title":"China Saf. 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