Background

Mental fatigue is one of the most causes of road accidents. Identification of biological tools and methods such as electroencephalogram (EEG) are invaluable to detect them at early stage in hazard situations.

Methods

In this paper, an expert automatic method based on brain region connectivity for detecting fatigue is proposed. The recorded general data during driving in both fatigue (the last five minutes) and alert (at the beginning of driving) states are used in analyzing the method. In this process, the EEG data during continuous driving in one to two hours are noted. The new feature of Gaussian Copula Mutual Information (GCMI) based on wavelet coefficients is calculated to detect brain region connectivity. Classification for each subject is then done through selected optimal features using the support vector machine (SVM) with linear kernel.

Results

The designed technique can classify trials with 98.1% accuracy. The most significant contributions to the selected features are the wavelet coefficients details 1−2 (corresponding to the Beta and Gamma frequency bands) in the central and temporal regions. In this paper, a new algorithm for channel selection is introduced that has been able to achieve 97.2% efficiency by selecting eight channels from 30 recorded channels.

Conclusion

The obtained results from the classification are compared with other methods, and it is proved that the proposed method accuracy is higher from others at a significant level. The technique is completely automatic, while the calculation load could be reduced remarkably through selecting the optimal channels implementing in real-time systems.

中文翻译：

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通过基于小波的连通性从脑电图自动检测驾驶员疲劳

背景

精神疲劳是造成道路交通事故的最主要原因之一。鉴定生物工具和方法（例如脑电图（EEG））对于在危险情况下及早发现它们具有重要意义。

方法

提出了一种基于脑区域连通性的疲劳检测专家自动方法。在分析该方法时，使用了在疲劳（最后五分钟）和警报（在驾驶开始时）状态下行驶过程中记录的常规数据。在此过程中，记录了在一到两个小时内连续行驶期间的EEG数据。计算基于小波系数的高斯Copula互信息（GCMI）的新功能，以检测大脑区域的连通性。然后，使用带有线性核的支持向量机（SVM）通过选择的最佳特征对每个主题进行分类。

结果

设计的技术可以以98.1％的准确度对试验进行分类。对所选特征的最重要贡献是中央和时间区域的小波系数细节1-2（对应于Beta和Gamma频带）。本文介绍了一种新的频道选择算法，该算法通过从30个记录的频道中选择8个频道，可以达到97.2％的效率。

结论

从分类中获得的结果与其他方法进行了比较，证明了该方法的准确性在相当大的程度上高于其他方法。该技术是完全自动的，而通过选择实时系统中实现的最佳通道可以显着降低计算量。