An attempt to predict drowsiness by Bayesian estimation

In this study, EEG (EEG-MPF, EEG-α/β), heart rate variability (RRV3), tracking error and subjective rating of fatigue (drowsiness) while performing a simulated driving task were measured. The relation between these measurements and drowsiness was analyzed. As a result, EEG-MPF tended to decrease with the increase of drowsiness. It tended that EEG-α/β, RRV3 and tracking error increased with the increase of drowsiness. Then, a method for predicting drowsiness by applying Bayesian estimation to physiological measurements was proposed. Bayesian estimation carries out a statistical inference using some kind of evidences or observations and calculating the probability that a hypothesis is true. An attempt was made to predicting drowsiness by applying Bayesian estimation to psychological parameters such as EEG-MPF, EEG-α/β, RRV3. As a result, it was suggested that the proposed method can predict the symptom of decreased consciousness (drowsiness).