February 11, 2021Open Access

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

Structured PICO

Population

EEG datasets for driving fatigue detection

Intervention

Two-Level Learning Hierarchy Radial Basis Function (RBF-TLLH) classifier

Comparator

Other widely used artificial neural networks

Outcome

Classification performance (accuracy and area under the receiver operating curve)

The proposed RBF-TLLH approach demonstrates high accuracy and reliability for EEG-based driving fatigue detection.

Abstract

. 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.

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