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Drowsy driving is the main reason for sleep-related crashes. We have observed that an alpha wave attenuation-disappearance phenomenon and a typical alpha blocking phenomenon commonly exist in the eye closure events during daytime simulated driving experiments. These two alpha-related phenomena prove to respectively represent two different sleepiness levels: the sleep onset and the relaxed wakefulness...
Attenuation of alpha wave is considered as the most valid marker of sleep onset during sleep, but this has received little attention during driving. Interestingly, from our simulated driving experiments, a new alpha wave's attenuation-disappearance phenomenon was observed to frequently appear in eye closure events (ECEs), with an obvious split point, which divides ECE into alpha attenuation phase...
Slow eye movement (SEM) is reported as a reliable indicator of sleep onset period (SOP) in sleep researches, but its characteristics and functions for detecting driving fatigue have not been fully studied. Through visual observations on ten subjects' experimental data, we found that SEMs tend to occur during eye closure events (ECEs). SEMs accompanied with alpha wave's attenuation during simulated...
This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning approaches, k-Nearest Neighbor (kNN), support vector machine (SVM), and discriminative graph regularized extreme...
This study aims at using electrooculographic (EOG) features, mainly slow eye movements (SEM), to estimate the human vigilance changes during a monotonous task. In particular, SEMs are first automatically detected by a method based on discrete wavelet transform, then linear dynamic system is used to find the trajectory of vigilance changes according to the SEM proportion. The performance of this system...
Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become...
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