Using EEG spectral components to assess algorithms for detecting fatigue

Fatigue is a constant occupational hazard for drivers and it greatly reduces efficiency and performance when one persists in continuing the current activity. Studies have investigated various physiological associations with fatigue to try to identify fatigue indicators. The current study assessed the four electroencephalography (EEG) activities, delta (δ), theta (θ), alpha (α) and beta (β), during a monotonous driving session in 52 subjects (36 males and 16 females). Performance of four algorithms, which were: algorithm (i) (θ + α)/β, algorithm (ii) α/β, algorithm (iii) (θ + α)/(α + β), and algorithm (iv) θ/β, were also assessed as possible indicators for fatigue detection. Results showed stable delta and theta activities over time, a slight decrease of alpha activity, and a significant decrease of beta activity (p < 0.05). All four algorithms showed an increase in the ratio of slow wave to fast wave EEG activities over time. Algorithm (i) (θ + α)/β showed a larger increase. The results have implications for detecting fatigue. Impact on industry: The results of this research have the implication for detecting fatigue and can be used for future development of fatigue countermeasure devices.