Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Electroencephalography signal processing based on textural features for
  monitoring the driver's state by a Brain-Computer Interface   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.

6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio