Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

疲劳驾驶是引发交通事故的主要原因之一。人脑是一个复杂的结构，其功能可以通过脑电图（EEG）来评估。利用脑电图进行自动驾驶员疲劳检测可降低相关交通事故的发生概率。因此，设计适当的特征提取技术并选择有效的分类方法可以被认为是有效驾驶员疲劳检测的关键部分。因此，本研究设计了一种基于脑电图的驾驶员疲劳检测智能系统。所提出的框架包括一个新的特征生成网络，该网络是通过使用纹理描述符实现的，用于疲劳检测。所提出的方案包含预处理、特征生成、信息特征选择和浅层分类器阶段的分类。在预处理中，结合使用了离散余弦变换和快速傅立叶变换。此外，结合使用基于动态中心的二元模式和多阈值三元模式来创建新的特征生成网络。为了提高检测性能，我们利用离散小波变换作为池化方法，其中基于功能性脑网络的特征描述疲劳与脑网络组织之间的关系。在特征选择阶段，提出了一种混合三层特征选择方法，并在分类阶段使用基准分类器来证明该方法的优点。在实验中，所提出的框架在使用脑电图信号进行疲劳检测时实现了 97.29% 的分类准确率。这一结果表明，所提出的框架可以有效地用于驾驶员疲劳检测。