Electroencephalography Activity Associated with Driver Fatigue: Implications for a Fatigue Countermeasure Device

Abstract

This paper reviews the association between electroencephalography (EEG) activity and driver fatigue. The current literature shows substantial evidence of changes in brain wave activity, such as simultaneous changes in slow-wave activity (e.g., delta and theta activity) as well as alpha activity during driver fatigue. It is apparent from the literature review that EEG is a promising neurophysiological indicator of driver fatigue and has the potential to be incorporated into the development of a fatigue countermeasure device. The findings from this review are discussed in the light of directions for future fatigue research studies.

The psychophysiological determinants of fatigue

“…As suggested above, many fatigue monitoring devices target physiological or performance decrement measures of fatigue (Lal and Craig, 2001), and in doing so, ignore the substantial psychological component of fatigue. Research has identified a need for improvement in fatigue self-assessment strategies (Fletcher et al, 2005), and there is a developing recognition that self-assessment of fatigue could be a possible fatigue countermeasure strategy.…”

The roles of cortical oscillations in sustained attention

“…For example, frontomedial theta power has been linked with both attentional fatigue [30] and enhanced attention task performance [11]. Similarly, alpha power reflects reduced attention when localised to posterior regions [8], but reflects improved attention when averaged across the scalp [31].…”

“…This approach allows estimation of the contribution of individual frequencies to the analysed signal ( Figure IB). In the case of cognitive electrophysiological research, frequencies are divided into spectral bands with distinct functional associations: delta (1-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13)(14), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (>30 Hz) ( Figure IC).…”

Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

“…Daubechies 10 is adopted as the mother wavelet. After eight-octave wavelet packet decomposition, the EEG components of the following 4 frequency bands are obtained: δ (0.5-3.5 Hz), θ (4-7 Hz), α (8)(9)(10)(11)(12), and β (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Then relative wavelet packet energy indices in four frequency bands, wavelet packet entropy, the ratio indices β/α, θ /α and (α+θ )/β for all EEG data segments are calculated, and wavelet packet features of 88 dimensions in every session are obtained.…”

“…driving, flying and tracking over extended periods) were related to the increase of the 0.1 Hz component in the heart rate variability (HRV) [5][6][7][8] . Although numerous physiological indicators are available to describe an individual's mental fatigue state, the EEG signals have been shown to be the most promising, predictive and reliable [9,10] . The EEG is widely regarded as the physiological "gold standard" for the assessment of mental fatigue.…”