Abstract
The large number of automobile accidents due to driver drowsiness is a critical concern of many countries. To solve this problem, numerous methods of countermeasure have been proposed. However, the results were unsatisfactory due to inadequate accuracy of drowsiness detection. In this study, we introduce a new approach, a combination of EEG and NIRS, to detect driver drowsiness. EEG, EOG, ECG and NIRS signals have been measured during a simulated driving task, in which subjects underwent both awake and drowsy states. The blinking rate, eye closure, heart rate, alpha and beta band power were used to identify subject’s condition. Statistical tests were performed on EEG and NIRS signals to find the most informative parameters. Fisher’s linear discriminant analysis method was employed to classify awake and drowsy states. Time series analysis was used to predict drowsiness. The oxy-hemoglobin concentration change and the beta band power in the frontal lobe were found to differ the most between the two states. In addition, these two parameters correspond well to an awake to drowsy state transition. A sharp increase of the oxy-hemoglobin concentration change, together with a dramatic decrease of the beta band power, happened several seconds before the first eye closure.
Highlights
The large number of automobile accidents due to driver drowsiness is a critical concern of many countries
The first two approaches are highly affected by external environments such as vehicle model and traffic condition, while the last one solely depends on subject condition; it shows a higher capability of detecting driver drowsiness
We used (1) frequency domain analysis to investigate the physiological signals in each state, (2) Fisher’s linear discriminant analysis (FLDA) to classify the awake and drowsy states, and (3) time series analysis to predict driver drowsiness
Summary
The large number of automobile accidents due to driver drowsiness is a critical concern of many countries. EEG, EOG, ECG and NIRS signals have been measured during a simulated driving task, in which subjects underwent both awake and drowsy states. The oxyhemoglobin concentration change and the beta band power in the frontal lobe were found to differ the most between the two states These two parameters correspond well to an awake to drowsy state transition. Early identification of the microsleep episode could be helpful to detect the onset of driver drowsiness and prevent automobile accidents. Due to the popularity of the EEG signal and its analysis methods, many works have been done to investigate drowsiness detection. We used (1) frequency domain analysis to investigate the physiological signals in each state, (2) Fisher’s linear discriminant analysis (FLDA) to classify the awake and drowsy states, and (3) time series analysis to predict driver drowsiness
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