This study examined the momentary effects of microsleeps on the driving performance in drivers who are at particular risk for drowsy driving because of OSAS. We tested the hypothesis that drowsy drivers would show measurable changes in driving performance during microsleep compared to matched non-microsleep segments. The results of this study showed significant differences in driver control that were related to both the occurrence and duration of microsleeps. Drivers showed lower speeds during microsleep episodes, indicating that drowsy drivers exert less control over the accelerator pedal during microsleeps by failing to continue to depress it as needed to maintain the recommended speed. Changes such as momentary vehicle slowing are not likely to have much effect on a vacant rural road, but might produce interactions that alter the traffic flow in highly congested areas, when sleepy drivers are returning home during peak traffic periods. Risser et al (2000)
found greater speed variability in drowsy drivers with OSAS compared to controls over an entire simulator drive, but did not assess momentary changes related to microsleeps. They also noted that lane position variability correlated with the frequency and duration of EEG-defined “attention lapses”.
Although there was no effect for SDSWA due to microsleeps, drivers with OSAS showed greater variability in maintaining lane position during microsleep episodes compared to non-microsleep episodes. SE increased during microsleep episodes and with successive drive segments, a finding not observed with the other lateral measures. Unlike the other steering measures, SE is related to moment to moment predictability and will increase as drivers make more error corrective maneuvers.
The finding that entropy increased with each drive segment indicates that sleepy drivers with microsleep episodes showed worse vehicle control the longer they drove. The finding of lower minimum TLC on curves, calculated using the method of van Winsum and Godthelp (1996)
, suggests that changes in road geometry may lead to a greater crash risk. Minimum TLCs were longer on straight segments, probably due to invariant road geometry and lack of external challenges. These findings suggest that drivers can maintain a relatively high TLC on straight segments during microsleep episodes of differing durations even if they may not be controlling the vehicle as actively.
Use of a fixed-base driving simulator in this study allowed us to make safe observations of sleepy driver behavior with a high degree of experimental control. A drawback of this approach is that feedback to the driver in any simulator differs from that in a moving vehicle. Drivers in actual road conditions get more tactile and vibratory feedback from the steering wheel, seat and vehicle frame, as well as vestibular feedback, all of which provide potential cues for drivers to exert control over the vehicle.
There are also challenges in using EEG to study sleepy driving. Because movement and muscle artifacts may hinder the EEG interpretation, this study analyzed only artifact-free EEG data. Microsleeps occurring during these segments would be missed. Although we used expert visual inspection of the EEG to identify microsleeps according to generally accepted criteria, this technique is inherently subjective. Several studies have used “quantitative” EEG methods to identify driver sleepiness (de Waard & Brookhuis, 1991
; Eoh et al., 2005
; Kecklund & Akerstedt, 1993
; Horne & Reyner, 1996
; Lal & Craig, 2002
). Alpha and theta power (usually expressed as the relative power of alpha + theta/beta), and the frequency of alpha and theta bursts typically increase during prolonged driving, and are associated with poor driving performance. As these techniques typically average EEG activity over several seconds (up to one minute), detection of brief microsleep episodes, as studied here, would not be possible. Eoh et al (2005)
showed that the numbers of short (1 second) alpha bursts and driving incidents increased with driving duration. However, instead of finding bursts occurring at the time of an incident, they noted a drop in alpha + theta/beta power in the seconds after incidents compared to the preceding 10 seconds. Although the current study did not include spectral analysis of EEG data, decline in alpha + theta/beta power probably reflects post-event alerting, while microsleep episodes reflect both pre- and post-event EEG changes. Risser et al (2000)
found that “attention lapses”, comprising EEG episodes of increased alpha or theta activity lasting more than 3 seconds (which differs from the conventional definition of microsleeps) correlated with lane position variability and crash frequency. The best techniques for identifying impending driver sleepiness by EEG are targets for future research.
A variety of physiological measures have been proposed for identifying and alerting drowsy drivers. One of the most investigated is PERCLOS (or PERcent CLOSure), a measure of drowsiness associated with slow eye closures (Grace et al., 1998
; Hayami et al., 2002
; Pilutti & Ulsoy, 1997
). However, PERCLOS does not identify drivers with “blank stares”, whose eyes remain open while they are drowsy. EEG changes, including microsleep episodes, may provide complementary evidence of impending sleep in these drowsy drivers. Paul et al (2005b)
showed that drivers had greater variation in steering and lane position during microsleep episodes when compared to the periods before and after a microsleep. Lal and Craig (2002)
identified early signs of sleepiness in a driving simulator task using EEG that was later proposed for fatigue-detection countermeasure systems (Lal et al., 2003
Combining EEG and PERCLOS data may permit the design of an onboard system that could alert sleepy drivers to unsafe situations before lid closure occurs. While the current study did not directly study this issue, EOG recordings (made to exclude potential artifacts during EEG recordings) showed that eye blinks often continued during microsleeps, indicating that the eyes were at least partially open. In fact, eye closure characteristically leads to an increase in alpha (Niedermeyer, 2005
), rather than the decrease that was used as the primary criterion for determining the presence of a microsleep. Further studies are needed to establish the extent to which EEG and eye closure information provide complementary information.
This study used a within subject design and was specifically aimed at evaluating driver performance related to microsleeps in an enriched population of drowsy drivers. It did not address the relationship between overall performance and EEG changes, or correlate findings with subjective measures of sleepiness such as the ESS. The costs and benefits of EEG monitoring of drivers, and how the findings discriminate between groups of safe and unsafe drivers with varying degrees of sleepiness or sleep disorders are topics for future studies.
In conclusion, drowsy drivers with OSAS show deterioration in simulated driving performance during EEG-verified microsleeps. The degree of deterioration correlated with microsleep duration and was worse when microsleeps occurred on curved road segments. Identifying how microsleep episodes influence driving behavior may prove to be relevant to the design and implementation of countermeasures, such as drowsy driver detection and alerting systems.