Positive correlation between drowsiness and prefrontal activation during a simulated speed-control driving task

Abstract

The present study aimed to examine the relationship between drowsiness and prefrontal activation during simulated driving tasks using a wireless portable near-infrared spectroscopy device. Participants drove from start to goal along default routes with either intentional control of their driving speed (speed-control group) or not (speed-free group). Drowsiness level was assessed using a five-item Likert-type questionnaire. The behavioral data indicated longer driving time in the speed-control group than in the speed-free group, whereas no difference in the number of errors was found between the two groups. Importantly, the speed-control group showed a significant positive correlation between the drowsiness score and left prefrontal activation, whereas the speed-free group did not. The results suggest that drowsy individuals may show increased prefrontal activation as compensatory efforts to maintain the desired level of performance in tasks that require deliberate control of behaviors. Furthermore, the present study shows that near-infrared spectroscopy may provide us with a possibility to examine the state of drowsiness during daily-life operations.

Near-infrared spectroscopy as a tool for driving research

“…Finally, NIRS machines can vary in the number of optode-receiver pairs, which allow for simultaneous coverage of different brain areas, from simple single or two-channel set-ups (e.g., Liu, 2014), allowing very unrestricted, hands free/wireless monitoring, to more recent 48 or 64-channel imaging systems which can give a picture of the entire brain surface (e.g., Yoshino et al, 2013a, b).…”

“…(Scholkmann et al, 2014). Because NIRS data measured by CW systems is a relative value, and hence cannot be averaged directly across channels and participants, 2 many studies further calculate z-scores using the mean value and the standard deviation of hemoglobin changes during a baseline period (e.g., Liu, 2014;Matsuda and Hiraki, 2006). Finally, group-averaged data in each condition/group is obtained for statistical analysis.…”

“…Many daily operations, however, involve earlier stages of fatigue, in which a driver may show distinctive activation patterns. Liu (2014) examined the relationship between prefrontal activation and daily mental state of drowsiness in a simulated speed-control driving task using a wireless two-channel NIRS device (PocketNIRS; DynaSense Inc., Japan). Participants drove from start to goal along default route-maps either without speed limit (speed-free group) or with a speed limit of 30-40 km/h (speed-control group).…”

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

“…They concluded that tDCS, in comparison with the sham condition, failed to counteract cortical-fatigue-related changes, whereas tDCS combined with induction of cortical fatigue shifted the inter-hemispheric oxygen level in the post-training resting state. In an interesting study by Liu et al (2014) [110], a positive correlation between drowsy state and prefrontal activation was found by experimenting with a simulated environment in which a speed-control driving task was performed. The level of drowsiness was evaluated through a five-item Likert-type questionnaire.…”

A Methodological Review of fNIRS in Driving Research: Relevance to the Future of Autonomous Vehicles

“…The majority of studies included here were conducted in a simulator setting, with only nine occurring in an on-road environment (Harada et al, 2007;Shimizu T. et al, 2011;Yoshino et al, 2013a,b;Inoue et al, 2014;Orino et al, 2015Orino et al, , 2017Liu et al, 2017;Le et al, 2018). However, as described above, the quality of the simulators (e.g., fidelity of the visual environment, amount of visual field encompassed, realism of the simulator to a real automobile) varied between low fidelity desktop computer setups (Shang et al, 2007;Li et al, 2009Li et al, , 2018Tomioka et al, 2009;Liu, 2014;Khan and Hong, 2015;Pradhan et al, 2015;Unni et al, 2015;Ahn et al, 2016;Horrey et al, 2017;Nguyen et al, 2017;Xu L. et al, 2017;Hidalgo-Munoz et al, 2019;Khan et al, 2019;Lin et al, 2019;Tanveer et al, 2019) and more immersive simulated environments (Nakano et al, 2013;Oka et al, 2015;FakhrHosseini et al, 2015;Foy et al, 2016;Foy and Chapman, 2018;Huve et al, 2018Huve et al, , 2019Sturman and Wiggins, 2019;Yamamoto et al, 2019) including large-scale simulators that comprise a real car mock-up along with a wide field of vision (Tsunashima and Yanagisawa, 2009;Shimizu et al, 2009;Orino et al, 2015;…”