Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

• Published in: PloS one Vol. 12; no. 12; p. e0188756
• Main Authors: Min, Jianliang, Wang, Ping, Hu, Jianfeng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.12.2017; Public Library of Science (PLoS)
• Subjects: Autoregressive models; Autoregressive processes; Biology and Life Sciences; Brain research; Classification; Classifiers; Collaboration; Comparative analysis; Computer and Information Sciences; Computer simulation; Diagnosis; Driver fatigue; Driving ability; Ecosystems; EEG; Electroencephalography; Entropy; Experiments; Fatigue; Health aspects; Information technology; Medicine and Health Sciences; Methods; Motor vehicle drivers; Neural networks; Physical Sciences; Physiology; Research and Analysis Methods; Signal processing; Studies; Time series; Traffic accidents & safety; Wavelet transforms
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0188756

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.