Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface
This addresses driver safety by detecting drowsiness, but it is incremental as it applies an existing textural method to EEG data.
The study tackled driver vigilance monitoring by using a one-dimensional Local Binary Pattern algorithm on EEG signals to classify states as awake, tired, or drowsy, achieving significant performance improvement but noting overall performance is insufficient for real-world BCI applications.
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.