EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function

Frontiers in Neurorobotics

Hongmiao Zhang¹  Ying Lin¹  Bin Chen²  Chushan Wang²  Rihui Li³  Yingchun Zhang⁴  Yuliang Ma⁵  Ziwu Ren⁵ 

[1] Robotics Institute, Hangzhou Dianzi University, Hangzhou, China;;College of Automation, Intelligent Control &Department of Biomedical Engineering, University of Houston, Houston, TX, United States;Guangdong Provincial Work Injury Rehabilitation Hospital, Guangzhou, China;Robotics and Microsystems Center, Soochow University, Suzhou, China;

关键词: driving fatigue detection;    electroencephalography;    principal component analysis;    radial basis function;    neural network;    classification;   

DOI  :  10.3389/fnbot.2021.618408

来源: DOAJ

【 摘 要 】

Electroencephalography (EEG)-based driving fatigue detection has gained increasing attention recently due to the non-invasive, low-cost, and potable nature of the EEG technology, but it is still challenging to extract informative features from noisy EEG signals for driving fatigue detection. Radial basis function (RBF) neural network has drawn lots of attention as a promising classifier due to its linear-in-the-parameters network structure, strong non-linear approximation ability, and desired generalization property. The RBF network performance heavily relies on network parameters such as the number of the hidden nodes, number of the center vectors, width, and output weights. However, global optimization methods that directly optimize all the network parameters often result in high evaluation cost and slow convergence. To enhance the accuracy and efficiency of EEG-based driving fatigue detection model, this study aims to develop a two-level learning hierarchy RBF network (RBF-TLLH) which allows for global optimization of the key network parameters. Experimental EEG data were collected, at both fatigue and alert states, from six healthy participants in a simulated driving environment. Principal component analysis was first utilized to extract features from EEG signals, and the proposed RBF-TLLH was then employed for driving status (fatigue vs. alert) classification. The results demonstrated that the proposed RBF-TLLH approach achieved a better classification performance (mean accuracy: 92.71%; area under the receiver operating curve: 0.9199) compared to other widely used artificial neural networks. Moreover, only three core parameters need to be determined using the training datasets in the proposed RBF-TLLH classifier, which increases its reliability and applicability. The findings demonstrate that the proposed RBF-TLLH approach can be used as a promising framework for reliable EEG-based driving fatigue detection.

【 授权许可】

Unknown