The results of this research represented a new and highly innovative approach to drowsiness detection.  The main objective of this research was to correlate the signal characteristics from a single channel of EEG data with extreme sleepiness.

This research resulted in the discovery of information in 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.  The information contained in this new range of frequencies, much higher than the traditional EEG frequency bands, was previously ignored and considered broadband noise and as such, was typically eliminated from the EEG signal by filtering.  In fact, laboratory tests and data analysis conducted in this work established for the first time that the high frequency range of the EEG signal contained useful information for the drowsiness tracking application.  In the course of this research, we were able to explore some of the characteristics of these new frequencies and compare them to the behavior of the standard frequency bands before discussing 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 had been given which, through effective signal analysis and processing, allowed these frequencies to be used directly in a drowsiness tracking and detection system.  In fact, the algorithm developed in this work was constructed exclusively from those frequencies that were routinely eliminated from typical EEG records.

We had also verified, within the scope of the experimental study, that the behavioral failures due to extreme sleepiness appear to be caused by the initial stages of sleep as we would expect and not by some other mechanism.

Finally, the laboratory experiments conducted as a part of the research verified some of the correlations between energy in the traditional frequency bands and extreme drowsiness.  For this experiment and under these testing conditions, we determined that among the traditional frequency bands, increases in delta and theta activity were the most reliable EEG correlates/indicators with test failures.  In addition to these frequencies, a disappearance of alpha activity and reduction of beta energy also showed correlations with test failures but with less consistency than the delta and theta bands.