论文标题
脑电图信号处理基于纹理特征，用于通过脑部计算机界面监视驾驶员的状态
Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface
论文作者
论文摘要
在这项研究中，我们研究了一种脑电图（EEG）信号的质地处理方法，以估算假设的脑部计算机界面（BCI）系统中驾驶员警惕性的指标。提出的解决方案的新颖性依赖于采用一维局部二元模式（1D-LBP）算法来从预处理的EEG数据中提取特征。从最终的功能向量中，分类是根据三个警惕课程进行的：清醒，疲倦和昏昏欲睡。声称可以通过描述沿EEG信号的微图案出现的变化来检测类转变。 1d-lbp能够通过检测信号的相互变化暂时“关闭”作为短比特代码来描述它们。我们的分析得出的结论是，1D磅的采用率已导致了显着的绩效提高。此外，捕获从脑电图信号的班级过渡是有效的，尽管总体表现还不够出色，无法开发BCI来评估驾驶员在实际环境中的警惕。
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.
