Driving performance and EEG fluctuations during on-the-road driving following sleep deprivation

Perrier, Joy; Jongen, Stefan; Vuurman, E.F.P.M.; Bocca, Marie‐Laure; Ramaekers, J.G.; Vermeeren, Annemiek

doi:10.1016/j.biopsycho.2016.09.010

Cited by 55 publications

(46 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results showed an alpha-beta band decrement and theta-delta increment in line with fatigue onset, and this was consistent for all of the participants. The result is slightly different from those achieved by Jagannath and Balasubramanian (2014) and Perrier et al (2016), whose results showed an increment in the alpha-theta band for fatigue. However, the result is in accordance with Cajochen et al (1995), who stated that there is an increment in the theta-delta band as an indication of fatigue, and Otmani et al (2005), who found a decrement in the beta band.…”

Section: Hydrodynamic Performancecontrasting

confidence: 99%

“…On the other hand, the ratio of the EEG parameters (θ/β, θ/(α+β), (θ+α)/β, and (θ+α)/(α+β)) were all significantly affected by driving duration, with the after-driving period having a higher ratio than the before-driving period. This shows that the alpha, beta, and theta bands have a major effect in terms of their ability to indicate fatigue onset, which is in line with the results of previous studies (Otmani et al, 2005;Cajochen et al, 1995;Jagannath & Balasubramanian, 2014;Perrier et al, 2016).…”

Section: Hydrodynamic Performancesupporting

confidence: 92%

“…The measurement points were six frontal channels (F3, F4, F7, F8, AF3, and AF4) out of a total of 16 channels (as shown in Figure 2). This brain measurement followed previous studies (Otmani et al, 2005;Jagannath & Balasubramanian, 2014;Perrier et al, 2016), which stated that the frontal part of the brain controls cognitive skill and is affected by fatigue.…”

Section: Methodsmentioning

confidence: 69%

“…Electroencephalogram (EEG) has been accepted as the 'gold standard' of fatigue measurement in much of the prior literature (Jap et al, 2011;Jagannath & Balasubramanian, 2014;Perrier et al, 2016). Previous studies have suggested that prolonged driving duration had varied effects on EEG fluctuations, consisting of disappearance of the alpha band with increments in theta and delta (Cajochen et al, 1995), increments in the alpha and theta bands (Jagannath & Balasubramanian, 2014;Perrier et al, 2016), and a decrement in the beta band (Otmani et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have suggested that prolonged driving duration had varied effects on EEG fluctuations, consisting of disappearance of the alpha band with increments in theta and delta (Cajochen et al, 1995), increments in the alpha and theta bands (Jagannath & Balasubramanian, 2014;Perrier et al, 2016), and a decrement in the beta band (Otmani et al, 2005). Furthermore, Jap et al (2011) studied the effects of the monotony of train driving for 30 minutes and compared several parameters of EEG; θ/β, θ/(α+β), (θ+α)/β, and (θ+α)/(α+β).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Effect Of Driving Duration On Eeg Fluctuations

Puspasari¹,

Iridiastadi

Sutalaksana

et al. 2017

IJTech

View full text Add to dashboard Cite

Road accidents are a major issue in Indonesia, and their number increases every year. Based on previous studies, mental fatigue is one of the biggest factors leading to road accidents and is majorly affected by mental workload. Driving duration is one of the factors that triggers mental fatigue. The prior literature cites electroencephalogram (EEG) measurement as the gold standard for measuring fatigue. However, there has been only limited study to examine the EEG indicators that are affected by driving duration, and the prior research still contains disagreements regarding the best EEG parameter for use in measuring fatigue. Therefore, this study aimed to evaluate the effect of driving duration on EEG fluctuation and determine the best EEG parameter related to fatigue. Seven participants were asked to spend three hours driving in a medium-fidelity simulator. A one-way ANOVA and correlation analysis were performed to measure the effect of driving duration on the EEG indicators and determine the correlation of the indicators. A Receiver Operating Characteristics (ROC) curve was also utilized to determine the variable with the greatest correlation with the subjective sleepiness indices. The results showed that at the end of three hours' driving, there was an increment in delta and theta activities, followed by a decrement in alpha and beta activities. In addition, the correlation of all bands was significant, with positive results for the alpha-beta and theta-delta bands, and a negative result in relation to each other. Furthermore, the results from the ROC curve revealed the Relative Power Ratio (RPR) of theta, the RPR of alpha, and the ratio of θ/α+β to be the best indicators among others, demonstrating a high degree of accuracy (above 85%).

show abstract

Section: Hydrodynamic Performancecontrasting

confidence: 99%