  In this study we investigate a textural processing method of
electroencephalography (EEG) signal as an indicator to estimate the driver's
vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty
of the solution proposed relies on employing the one-dimensional Local Binary
Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data.
From the resulting feature vector, the classification is done according to
three vigilance classes: awake, tired and drowsy. The claim is that the class
transitions can be detected by describing the variations of the micro-patterns'
occurrences along the EEG signal. The 1D-LBP is able to describe them by
detecting mutual variations of the signal temporarily "close" as a short
bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to
significant performance improvement. Moreover, capturing the class transitions
from the EEG signal is effective, although the overall performance is not yet
good enough to develop a BCI for assessing the driver's vigilance in real
environments.
