Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data

Abstract

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions.

Investigation of brain functional connectivity in patients with mild cognitive impairment: A functional near‐infrared spectroscopy (fNIRS) study

“…Five caps (two for short channel PDs and LEDs, and three for long channel PDs) were connected to form an fNIRS probe (Figure A). The detailed system design can be found in our previous studies . The fNIRS probe was firmly attached on the subjects' foreheads using a medical double‐layer tape such that the center cap was placed at exactly 15 mm below the Fpz position of EEG 10‐20 standard system (Figure A).…”

The correlation between visual fatigue and duration of viewing as assessed by brain monitoring

“…Besides absolute power spectral density (A‐PSD) of all electrodes, the relative power spectral density (R‐PSD) was also calculated. The R‐PSD, which was supposed to reduce session/subject variability, represented by the spectral ratios which could be defined as where R j , b is the R‐PSD of senor j, P j , b is the average A‐PSD of band b in sensor j, b∈{δ, θ, α, β, γ}.…”

“…Besides absolute power spectral density (A-PSD) of all electrodes, the relative power spectral density (R-PSD) was also calculated. The R-PSD, which was supposed to reduce session/subject variability, 27 represented by the spectral ratios which could be defined as…”

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

“…There are several causes of traffic accidents such as the operator's consciousness, use of the phone, drug or alcohol use, fatigue which leads to drowsiness and loss of concentration. So far, several techniques for detecting drowsiness have been studied [3][4][5][6][7][8][9][10][11][12][13][14][15][16], which can be divided into three basic directions as follows:…”