Academic journal article Iranian Journal of Public Health

Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving

Academic journal article Iranian Journal of Public Health

Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving

Article excerpt

Introduction

Fatigue is a transitional state between awake and sleep which manifests itself as lack of alertness and deteriorated mental or physical performance and often associated with drowsiness. Driver fatigue is one of the major causes of accidents and casualties in roads. Road accidents due to fatigue are often much more severe than other crashes, since the driver reaction time increases (1).

Driver fatigue is deemed to account for up to 40% of road accidents (2). It is conjectured that 10-30% of road deaths are related to driver fatigue (3). Many studies have suggested that mental fatigue induces deterioration in cognitive abilities. Mental fatigue causes reactions become prolonged, more fluctuable, and more error tending (4, 5). Impairments in perceptual and cognitive functions after extended wakefulness are responsible for performance deteriorations. Grandjean defined fatigue as a state with decreased efficiency and lack of general willingness to work (6, 7).

There are different techniques and methodologies for mental fatigue measurement. These can be classified as subjective, psychological, performance and physiological methods. In subjective methods, standard questionnaires such as F-VAS and Karolinska sleepiness scale have been employed (8-13). Moreover, the use of behavioral and psychological techniques in mental fatigue investigation has been adopted in several preceding studies (14-17). Among these studies, a set of video recordings of facial expressions, mannerisms and personality traits questionnaires were common methodologies with high reliability and validity (17). In addition, some fatigue studies on driving simulator exploited performance features such as steering wheel angle and lane departure (18-20). Some other researchers have focused on driver's physiological changes, such as the measurement of eye activity, heart rate, skin electrical potential and specially EEG activity as a means to detect cognitive states (21, 22). Although several physiological indices available for assessing the alertness level, EEG signal may be one of the safest and most predictive, (23-25), since it immediately reflects brain activity. Driving involves several tasks such as motion, reasoning, visual and auditory processing, decision-making, perception and cognition. Driving is also under the influence of emotion, anxiety and many other psychological factors (24). All physical and mental activities associated with driving are reflected in EEG signals. The brain electrical activity rhythms are classified according to frequency bands including delta, theta, alpha and beta waves (26). Alpha rhythm has the frequency range from 8-13 Hz, which occurs during wakefulness, especially in the occipital cortex area of the brain. It typically appears during eye closure and reduced when eyes open and attenuates severely during attention tasks (27).

In previous researches, EEG measurements have been used to detect performance variations. It is known that increase in delta power during internal processing is associated with mental task performance (28). Other researchers have reported that an increase in EEG theta power depends on decreased performance in monotonous tasks (29). Although some definite trends were observed in the delta, theta and alpha power frequency bands during fatigue, the results of different studies may be influenced by inter-individual and intra-individual variations in EEG data (30).

Changes in EEG power spectra are associated with fluctuations in the alertness state (31). Monitoring physiological signals while driving can provide the possibility to detect and warn fatigue (32). Most investigations revealed that changes in delta and theta activity are related to the transition to fatigue. Therefore, EEG monitoring during driving may be a promising variable for using in fatigue countermeasure devices (24).

It has been suggested that during driving at night, delta band varies significantly with the degree of fatigue (33). …

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