EEG signal analysis for the assessment and quantification of driver’s fatigue

Kar, S.; Bhagat, Mayank; Routray, Aurobinda

doi:10.1016/j.trf.2010.06.006

Cited by 244 publications

(133 citation statements)

References 27 publications

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“…The insensitivity of the temporal region to most of these parameters may be because of its irrelevance for vision cognition. Furthermore, the SE significantly increased in the posterior temporal, parietal, and occipital areas, which are consistent with the results from a study by Kar [4]. SE is a measure of flatness of the energy spectrum, and the physical interpretation for this may be understood in the terms of neural activity.…”

Section: Discussionsupporting

confidence: 90%

“…Its general symptoms are non-specific: generally it manifests in the form of drowsiness, weakness, dizziness or queasiness. Fatigue can lead to distractibility and provokes lapses in information processing [4]. In other words, when one persists in continuing the current work as normal, efficiency and performance can be reduced during fatigue [5].…”

Section: Introductionmentioning

confidence: 99%

“…However, subjective evaluation is easily affected by individual difference. Objective evaluation includes measures based on reaction time, ocular parameters, and physiological variables like the Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) or Electrocardiogram (ECG) [4].…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalogram assessment of mental fatigue in visual search

Fan

Zhou

Liu

et al. 2015

BME

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Abstract. Mental fatigue is considered to be a contributing factor responsible for numerous road accidents and various medical conditions and the efficiency and performance could be impaired during fatigue. Hence, determining how to evaluate mental fatigue is very important. In the present study, ten subjects performed a long-term visual search task with electroencephalogram recorded, and self-assessment and reaction time (RT) were combined to verify if mental fatigue had been induced and were also used as confirmatory tests for the proposed measures. The changes in relative energy in four wavebands į ș Į and ȕ), four ratio formulas [(D + T)/E, D/E, (D + T)/(D + E), and T/E], and Shannon's entropy (SE) were compared and analyzed between the beginning and end of the task. The results showed that a significant increase occurred in alpha activity in the frontal, central, posterior temporal, parietal, and occipital lobes, and a dip occurred in the beta activity in the pre-frontal, inferior frontal, posterior temporal, and occipital lobes. The ratio formulas clearly increased in all of these brain regions except the temporal region, where only D/E changed obviously after finishing the 60-min visual search task. SE significantly increased in the posterior temporal, parietal, and occipital lobes. These results demonstrate some potential indicators for mental fatigue detection and evaluation, which can be applied in the future development of countermeasures to fatigue.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Electroencephalogram assessment of mental fatigue in visual search

Fan

Zhou

Liu

et al. 2015

BME

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show abstract

“…There are four kinds of wave in the human brain, delta wave, theta wave, alpha wave and beta wave. The delta wave and theta wave increase while the alpha wave and beta wave decrease when the driver changes from a state of consciousness to a state of fatigue [13]. Based on the relationship between the change of band signal and degree of driver fatigue, detection algorithm is able to use the real-time EEG signals to make accurate judgments on the degree of driver fatigue.…”

Section: Eeg Detectionmentioning

confidence: 99%

“…Based on the relationship between the change of band signal and degree of driver fatigue, detection algorithm is able to use the real-time EEG signals to make accurate judgments on the degree of driver fatigue. EEG signals is the most important and reliable index for fatigue detection [13].…”

Section: Eeg Detectionmentioning

confidence: 99%

A Review on Fatigue Driving Detection

Shi

Wang

2017

ITM Web Conf.

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Abstract:The socialization of automobile development has brought great convenience to people's travel. However, the rapid increase in the number of vehicles has also caused a series of problems. The increase in traffic accidents has brought great social casualties and economic losses. Fatigue driving, which is an important factor in the traffic accident, has aroused people's attention. This paper reviews all kinds of fatigue driving detection methods at present; compares various fatigue driving detection methods in terms of accuracy, real-time and cost; analyses the advantages and disadvantages of various methods; introduces the application of fatigue detection system in automobile; summarizes the current deficiencies and future development trends in the field of fatigue driving detection. The future research of this field will be more to the data fusion, computer vision and deep learning.

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Different brain activation patterns in the prefrontal area between self‐paced and high‐speed driving tasks

Hirano

Kimura

Yano

et al. 2022

Journal of Biophotonics

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The purpose of this study was to investigate the effects on prefrontal cortex brain activity when participants attempted to stop a car accurately at a stop line when driving at different speeds using functional near‐infrared spectroscopy (fNIRS). Twenty healthy subjects with driving experience drove their own cars for a distance of 60 m five times each at their own pace or as fast as possible. The variation in the distance between the stop line and the car was not significantly different between the self‐paced and high‐speed tasks. However, oxygenated hemoglobin concentration in the prefrontal cortex was significantly higher in the high‐speed task than in the self‐paced task. These findings suggest that driving at high speed requires more divided attention than driving at self‐paced speed, even though the participants were able to stop the car at the same distance from the target. This study shows the advantages and usefulness of fNIRS .

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EEG signal analysis for the assessment and quantification of driver’s fatigue

Cited by 244 publications

References 27 publications

Electroencephalogram assessment of mental fatigue in visual search

Electroencephalogram assessment of mental fatigue in visual search

A Review on Fatigue Driving Detection

Different brain activation patterns in the prefrontal area between self‐paced and high‐speed driving tasks

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