The effects of different fatigue levels on brain–behavior relationships in driving

Abstract

BackgroundIn the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain–behavior relationships.MethodsA longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model.ResultsResults showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high‐fatigue (high‐risk) group. Additionally, the alpha power of the occipital regions showed an inverted U‐shaped change.ConclusionOur results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators.

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks

“…The driving experiment was conducted in the early afternoon (13:00-15:00) after lunch, when the circadian rhythm of drowsiness is at its peak [29]. In addition, the VR-highway scene was monotonous, and the task demand was low to induce drowsiness [11], [30], [31]. Under such conditions, the subjects had difficulty regulating attention and performance, which resulted in long RTs [32].…”

“…The noises associated with eye movements and muscle activities were removed manually, followed by 250-Hz downsampling, a highpass filter (1 Hz) and a low-pass filter (50 Hz). The EEG signals were later segmented into a 6-second baseline signal, which began 6 seconds before event onset [11], [29]- [31]. To eliminate inter-subject differences, the measured RTs and EEG dynamics were normalized by the trails with 10% fastest RTs in every single subject [11], [12].…”

“…The human brain is dominated by four major lobes, the frontal, parietal, temporal and occipital lobes. Each lobe is responsible for distinct cognitive functions [9], [31]- [34]. Electrodes are placed around the scalp to acquire brain information, and a 32-channel EEG cap was used in this study.…”

“…According to previous studies, the brain dynamics in the theta and alpha bands are positively correlated with the attentional demands or levels of arousal [31], [38]. In particular, researchers have mainly focused on the changes in the frontal and posterior cingulate cortex (parietal and occipital lobes) [36]- [38].…”

EEG-Beats: Automated analysis of heart rate variability (HVR) from EEG-EKG

“…This study uses the NCTU RWN_VDE data collection from a longitudinal experiment conducted at National Chiao Tung University (NCTU) during the 2014-15 school year to assess the effects of fatigue and stress on performance (Lin et al, 2016) (Lin et al, 2018) (Huang et al, 2019). Each of the 17 study subjects wore an Actigraph activity monitor for the duration of the study and completed several subjective assessments of stress, fatigue, and sleep quality on a daily basis.…”