An EEG-based system utilizing multiple entropy fusion analysis from four channel regions detected driver fatigue with an accuracy of 98.3%, sensitivity of 98.3%, and specificity of 98.2%.
A multiple entropy fusion method using EEG data achieved high accuracy (98.3%) in detecting driver fatigue in a simulated environment.
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.
Min et al. (Fri,) conducted a other in Driver fatigue (n=12). Multiple entropy fusion analysis vs. Autoregressive (AR) modeling was evaluated on Accuracy of driver fatigue detection. An EEG-based system utilizing multiple entropy fusion analysis from four channel regions detected driver fatigue with an accuracy of 98.3%, sensitivity of 98.3%, and specificity of 98.2%.