  Ear EEG based driver fatigue monitoring systems have the potential to provide
a seamless, efficient, and feasibly deployable alternative to existing scalp
EEG based systems, which are often cumbersome and impractical. However, the
feasibility of detecting the relevant delta, theta, alpha, and beta band EEG
activity through the ear EEG is yet to be investigated. Through measurements of
scalp and ear EEG on ten subjects during a simulated, monotonous driving
experiment, this study provides statistical analysis of characteristic ear EEG
changes that are associated with the transition from alert to mentally fatigued
states, and subsequent testing of a machine learning based automatic fatigue
detection model. Novel numerical evidence is provided to support the
feasibility of detection of mental fatigue with ear EEG that is in agreement
with widely reported scalp EEG findings. This study paves the way for the
development of ultra-wearable and readily deployable hearables based driver
fatigue monitoring systems.
