Driver Fatigue Detection Through Chaotic Entropy Analysis of Cortical Sources Obtained From Scalp EEG Signals

In this paper, the focus is on the analysis of a scalp electroencephalography (EEG) database of human subjects using the electrophysiological source imaging or source localization and the classification of normal and sleep-deprived states. The EEG collection was carried out while the subjects were driving in simulated condition in a laboratory, where the fatigue level propagates through 11 different stages of fatigue, to achieve the sleep deprivation of a total period of 36 h. Standardized low-resolution brain electromagnetic tomography (sLORETA) algorithm has been used here for estimating the source activations on the surface of the neo-cortex. sLORETA transforms the surface or scalp EEG data to the corresponding corticular dipole sources at each voxel on a simulated neo-cortex. For the characterization of the underlying neural patterns, approximate and sample entropies in voxels nearest to specific electrodes for different subjects and varying fatigue levels have been computed. Approximate entropy, sample entropy, and modified sample entropy are used here as the measures of complexity, similarity, and regularity in the sources. As a further investigation, these measures computed over all the stages are used to train a support vector machine, which classifies the measured values between alert and extremely fatigued states. As a result, several observations on the nature of change of the chaotic entropies are provided, and up to 86% classification accuracy is obtained.