Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study

Abstract

Driving fatigue is a common experience for most drivers and can reduce human cognition and judgment abilities. Previous studies have exhibited a phenomenon of the nonmonotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasingincreasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics

“…[ 36 ], our study aimed to assess mental fatigue, only focusing on experimental factors and thus excluding non-fatigue-related operations. From this standpoint, by exclusively estimating significant connections that exhibit an increasing or decreasing trend over the entire duration, we can avoid incorporating fatigue characteristics that could be internally regulated [ 11 ].…”

“…It should be noted that although self-perceived assessment usually fails to overcome subjective perception concerning the individuality of the subjects [ 8 ], utilizing physiological measures can provide insights into the actual impact of mental fatigue on cognitive functioning, independently of individuals’ subjective perceptions [ 9 ]. In fact, cortical variability in states of fatigue is well-documented across multiple studies, demonstrating significant differences in how individuals’ brain activity changes in response to fatigue [ 10 , 11 ]. In this regard, Lim et al [ 12 ] found that individuals exhibit considerable variability in specific EEG brain waves, presenting differentiations in power during prolonged cognitive tasks, with some showing rapid onset of fatigue indicators and others maintaining stable EEG patterns over time.…”

“…In fact, Tompson et al [ 28 ] conducted a review on the temporal dynamics of dynamic brain networks, noting that variations in both the strength and adaptability of evolving brain networks play a role in shaping individual disparities in executive function, attention, working memory, and learning abilities. In another study, Li et al [ 11 ], employed a long-term driving EEG task, reporting significantly increased patterns in functional connectivity in theta, alpha, beta, and gamma bands, suggesting a compensation effect to attenuate the effects of driving fatigue.…”