Assessment of mental fatigue during car driving by using high resolution EEG activity and neurophysiologic indices

Abstract

Driving tasks are vulnerable to the effects of sleep deprivation and mental fatigue, diminishing driver's ability to respond effectively to unusual or emergent situations. Physiological and brain activity analysis could help to understand how to provide useful feedback and alert signals to the drivers for avoiding car accidents. In this study we analyze the insurgence of mental fatigue or drowsiness during car driving in a simulated environment by using high resolution EEG techniques as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). Results suggest that it is possible to introduce a EEG-based cerebral workload index that it is sensitive to the mental efforts of the driver during drive tasks of different levels of difficulty. Workload index was based on the estimation of increase of EEG power spectra in the theta band over prefrontal areas and the simultaneous decrease of EEG power spectra over parietal areas in alpha band during difficult drive conditions. Such index could be used in a future to assess on-line the mental state of the driver during the drive task.

Brain computer interfacing: Applications and challenges

“…Various types of measures have contributed in determining the driver's cognitive state [64][65][66]. Uses of EEG signals for fatigue detection have been widely studied in [67], while [68] has discussed the utilization of workload index to assess the driver's mental state. Several models for distinguishing distracted drivers have been examined in [69].…”

Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of Political Candidates

“…Also, tendencies toward worry, thought to be approach-motivated in the sense of being linked to problem solving, have been linked to relatively greater left frontal EEG activity [84]. Thus, the emerging consensus appears to be that frontal EEG asymmetry primarily reflects levels of approach motivation (left hemisphere) versus avoidance motivation (right hemisphere), as also testified by previous studies [85–88]. …”

Blink rate variability during resting and reading sessions

“…In order to estimate scale exponents a range of scales have been selected. The frequency range is proportional to the analyzed scale l. We set the range of scales as l = {4, 5,6,7,8,10,11,13,16,19,23,27, 32}. The maximum scale is chosen in a way to be smaller than at least half of the shortest BRV's length among all participants and across all stages.…”