標題: Task-related EEG and HRV entropy factors under different real-world fatigue scenarios
作者: Lin, Chin-Teng
Nascimben, Mauro
King, Jung-Tai
Wang, Yu-Kai
腦科學研究中心
Brain Research Center
關鍵字: Human performance;Entropy analysis;Alertness prediction;EEG;HRV;Psychomotor vigilance task
公開日期: 15-Oct-2018
摘要: We classified the alertness levels of 17 subjects in different experimental sessions in a six-month longitudinal study based on a daily sampling system and related alertness to performance on a psychomotor vigilance task (PVT). As to our best knowledge, this is the first EEG-based longitudinal study for real-world fatigue. Alertness and PVT performance showed a monotonically increasing relationship. Moreover, we identified two measures in the entropy domain from electroencephalography (EEG) and heart rate variability (HRV) signals that were able to identify the extreme classes of PVT performers. Wiener entropy on selected leads from the frontal-parietal axis was able to discriminate the group of best performers. Sample entropy from the HRV signal was able to identify the worst performers. This joint EEG-HRV quantification provides complementary indexes to indicate more reliable human performance. (C) 2018 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.neucom.2018.05.043
http://hdl.handle.net/11536/147879
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.05.043
期刊: NEUROCOMPUTING
Volume: 311
起始頁: 24
結束頁: 31
Appears in Collections:Articles