標題: The effects of different fatigue levels on brain-behavior relationships in driving
作者: Huang, Kuan-Chih
Chuang, Chun-Hsiang
Wang, Yu-kai
Hsieh, Chi-Yuan
King, Jung-Tai
Lin, Chin-Teng
電機工程學系
腦科學研究中心
Department of Electrical and Computer Engineering
Brain Research Center
關鍵字: brain dynamics;electroencephalograms;fatigue;longitudinal assessment
公開日期: 1-一月-1970
摘要: Background In the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain-behavior relationships. Methods A longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model. Results Results showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high-fatigue (high-risk) group. Additionally, the alpha power of the occipital regions showed an inverted U-shaped change. Conclusion Our results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators.
URI: http://dx.doi.org/10.1002/brb3.1379
http://hdl.handle.net/11536/153096
ISSN: 2162-3279
DOI: 10.1002/brb3.1379
期刊: BRAIN AND BEHAVIOR
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