Abstract
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15–20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.
Highlights
Fatigue-related performance decrements such as lapses in attention and slowed reaction time could lead to catastrophic incidents in occupations ranging from ship navigators to airplane pilots, railroad engineers, truck and auto drivers, and nuclear plant monitors
This study aims to extend previous studies to design, develop and test a truly On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System that can continuously monitor EEG dynamics, predict fatigue-related lapses based on EEG signals, arouse the fatigued users by delivering arousing signals, and assess the efficacy of the arousing signal based on EEG spectra
This study used the alpha power fluctuations to monitor cognitive lapses because (1) a recent study showed that the alpha augmentation was sensitive to the transition from full alertness to mediate drowsiness, while the theta augmentation was more sensitive to the transition from mediate to deep drowsiness (Chuang et al, 2012); (2) the empirical results of this study showed that the augmentation of alpha-band power changes was greater than that of the theta-band power (Figure 2)
Summary
Fatigue-related performance decrements such as lapses in attention and slowed reaction time could lead to catastrophic incidents in occupations ranging from ship navigators to airplane pilots, railroad engineers, truck and auto drivers, and nuclear plant monitors. An earlier detection of driving fatigue is a crucial issue for preventing catastrophic incidents. In order to detect the driving fatigue, several approaches have been proposed in scientific literature. Bergasa et al (2006) used a real-time image-acquisition system to monitor drivers’ visual behaviors that revealed a drivers’ alertness level. D’Orazio et al (2007) proposed a neural classifier to recognize the eye activities from images without being constrained to head rotation or partially occluded eyes. Lin et al (2009) performed an event-related, lane-keeping driving task in an immersive virtual-reality environment. The results showed that the reaction time (RT), defined as the time interval between the onset of the simulated car deviation www.frontiersin.org
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