Deep in thought while driving: An EEG study on drivers’ cognitive distraction

Almahasneh, Hossam; Chooi, Weng-Tink; Kamel, Nidal; Malik, Aamir Saeed

doi:10.1016/j.trf.2014.08.001

Cited by 89 publications

(47 citation statements)

References 25 publications

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“…The findings represent that the force-feedback could correlate with higher ability in decision making and ultimately increase the capability of controlling the vehicle properly at the time of hazard encounter. It was initially expected to get the consistent results with Almahasneh et al (2014) findings, however, the topographical map result ( Figure 3) indicates that the difference between baseline and both cases is caused by more activity in corresponding brain region of the right frontal hemisphere near reaching the curves. Since most of cognitive activities occur at the frontal lobe (Lin et al, 2011) the findings are aligned with the role of frontal lobe in decision making and attention (Burgess, Alderman, Volle, Benoit, & Gilbert, 2009).…”

Section: Analysis Of Cognitive Statessupporting

confidence: 54%

The Effect of Whole-Body Haptic Feedback on Driver’s Perception in Negotiating a Curve

Pakdamanian

Kim

2018

Proceedings of the Human Factors and Ergonomics Society Annual

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It remains uncertain regarding the safety of driving in autonomous vehicles that, after a long, passive control and inattention to the driving situation, how the drivers will be effectively informed to take-over the control in emergency. In particular, the active role of vehicle force feedback on the driver's risk perception on curves has not been fully explored. To investigate it, the current paper examined the driver's cognitive and visual responses to the whole-body haptic feedback during curve negotiations. The effects of force feedback on drivers' responses on curves were investigated in a high-fidelity driving simulator while measuring EEG and visual gaze over ten participants. The preliminary analyses of the first two participants revealed that pupil diameter and fixation time on the curves were significantly longer when the driver received whole-body feedback, compared to none. The findings suggest that whole-body feedback can be used as an effective "advance notification" of hazards.

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Section: Analysis Of Cognitive Statessupporting

confidence: 54%

The Effect of Whole-Body Haptic Feedback on Driver’s Perception in Negotiating a Curve

Pakdamanian

Kim

2018

Proceedings of the Human Factors and Ergonomics Society Annual

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“…The driving duration was considered prolonged when it took at least 120 minutes [20], although other researchers set 150-180 minutes as the prolonged driving threshold [16,21]. The route chosen for this simulation was a loop track comprising of freeways and city streets, as provided by the driving simulation software used, City Car Driving Simulator v.1.4, which is commonly used in driving simulation studies (e.g., Almahasneh, et al [22]). The traffic density was set at 80% and the suggested driving speed was in the range of 40 to 60 km/hour.…”

Section: Tasks Designmentioning

confidence: 99%

An Analysis of EEG Changes during Prolonged Simulated Driving for the Assessment of Driver Fatigue

Zuraida¹,

Iridiastadi²,

Sutalaksana³

et al. 2019

J. Eng. Technol. Sci.

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Fatigue during driving is the main contributing factor to road accidents. It is influenced by time on task (TOT) and time of day (TOD). Recent electroencephalogram (EEG) research on fatigue assessment has shown a promising result in explaining the fatigue phenomenon. However, different findings exist regarding the best EEG parameters related to fatigue. This study examined EEG changes according to the effect of TOT and TOD and determined the best parameters to distinguish fatigue status. To generate driver fatigue, prolonged driving in the morning and at night in a simulator was conducted. The EEG signal was collected from 28 male participants at frontal and occipital areas. The EEG power (brainwave) was determined from the first and last 5 minutes of the driving task and after a break of 30 minutes. The results of this study showed a general tendency of EEG power changing throughout the driving sessions. However, changes related to fatigue were only found for the night sessions, as confirmed by θ power and the subjective fatigue measurement result. This study showed that TOT (as a factor that induces fatigue) was explained by θ from the frontal area, whereas TOD was differentiated by α, θ, θ/β, (θ+α)/β and (θ+α)/(β+α).

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“…Numerous studies conducted via driving simulators, test tracks, and naturalistic driving research have sought to quantify and clarify the risks of distracted driving due to visual and manual secondary tasks (e.g., Dingus et al, 2016;Fitch et al, 2013;Guo et al, 2017;Klauer et al, 2014;Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006;Olson, Hanowski, Hickman, & Bocanegra, 2009;Victor et al, 2015). However, existing research into cognitive disengagement relative to automobile use and risk (e.g., Almahasneh, Chooi, Kamel, & Malik, 2014;Angell, Perez, & Soccolich, 2014;Briggs, Hole, & Land, 2011;Owens et al, 2018;Strayer et al, 2013;Strayer et al, 2014;Wang, Reimer, Dobres, & Mehler, 2014) appears to be less conclusive. Current discrepancies are likely due to different data sources and study methodologies.…”

Section: Discussionmentioning

confidence: 99%

The prevalence of cognitive disengagement in automobile crashes

Wotring

Dingus

Atwood

et al. 2020

Applied Cognitive Psychology

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Summary The current study analysed crashes identified in a large‐scale naturalistic driving database to assess the prevalence of cognitive disengagement (i.e., purely cognitive distraction and mind wandering/microsleep) or episodes wherein the driver did not look away from the roadway during secondary task completion or wherein another clearly observable contributing crash factor was not present, and the driver's reaction to the crash showed symptoms of cognitive disengagement. The study found that <1% (95% CI [0.45, 1.66]) of higher severity crashes had a potential contributing crash factor of mind wandering/microsleep; ~1.5% (95% CI [0.83, 2.32]) had a potential contributing factor of purely cognitive distraction. The results suggest a relatively low prevalence of cognitive disengagement among automotive crashes compared with visual/manual secondary tasks. However, the current methodology is limited in that it does not account for instances where cognitive disengagement, in combination with other factors, may have led to crashes, potentially leading to conservative estimates.

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Deep in thought while driving: An EEG study on drivers’ cognitive distraction

Cited by 89 publications

References 25 publications

The Effect of Whole-Body Haptic Feedback on Driver’s Perception in Negotiating a Curve

The Effect of Whole-Body Haptic Feedback on Driver’s Perception in Negotiating a Curve

An Analysis of EEG Changes during Prolonged Simulated Driving for the Assessment of Driver Fatigue

The prevalence of cognitive disengagement in automobile crashes

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