An innovative EEG-based approach to drowsiness detection.

摘要

Sleep researchers rely on the EEG in the classification of various sleep stages, however, drowsiness and sleep onset produces much less distinguishable changes in the EEG waveform. The primary focus of this research effort has been the development of a signal processing methodology that reliably tracks the transition from normal alertness to extreme sleepiness in an individual from a single-channel measurement of the electroencephalogram (EEG). Extreme sleepiness refers to the state during which sleep is perceived as difficult to resist, the individual struggles against sleep, performance lapses occur, and sleep eventually ensues.; The results of this research represents a new and highly innovative approach to drowsiness detection. This research has resulted in the discovery of an entirely new range of frequencies in the EEG signal that correlate with states of consciousness from alertness through extreme sleepiness and various stages of sleep. These new signals have a much high frequency than the traditional EEG bands and these new frequencies were previously considered broadband noise and as such, were typically filtered out of the EEG signal. In fact, laboratory tests and data analysis conducted in this work has established for the first time that the high frequency range of the EEG signal contains useful information for the drowsiness tracking application. In the course of this research, we have been able to explore some of the characteristics of these new frequencies and compare them to the behavior of the standard frequency bands before moving on to the design and implementation of a tracking algorithm.; In addition to discovering an entirely new range of useful frequencies in the EEG signal, a method has been given which, through effective signal analysis and processing, can allow these frequencies to be used directly in a drowsiness tracking and detection system. In fact, the algorithm developed in this work is constructed exclusively from those frequencies that are routinely eliminated from typical EEG records.