Multiple robust approaches for EEG-based driving fatigue detection and classification

• Published in: Array (New York) Vol. 19; p. 100320
• Main Authors: Prabhakar, Sunil Kumar, Won, Dong-Ok
• Format: Journal Article
• Language: English
• Published: Elsevier 01.09.2023
• Subjects: ANFIS; ELM; Hilbert transform; MDS; SVM; SVR
• ISSN: 2590-0056; 2590-0056
• DOI: 10.1016/j.array.2023.100320

Electroencephalography (EEG) signals are used to evaluate the activities of the brain. For the accidents occurring on the road, one of the primary reasons is driver fatigueness and it can be easily identified by the EEG. In this work, five efficient and robust approaches for the EEG-based driving fatigue detection and classification are proposed. In the first proposed strategy, the concept of Multi-Dimensional Scaling (MDS) and Singular Value Decomposition (SVD) are merged and then the Fuzzy C Means based Support Vector Regression (FCM-SVR) classification module is utilized to get the output. In the second proposed strategy, the Marginal Fisher Analysis (MFA) is implemented and the concepts of conditional feature mapping and cross domain transfer learning are implemented and classified with machine learning classifiers. In the third proposed strategy, the concepts of Flexible Analytic Wavelet Transform (FAWT) and Tunable Q Wavelet Transform (TQWT) are implemented and merged and then it is classified with Extreme Learning Machine (ELM), Kernel ELM and Adaptive Neuro Fuzzy Inference System (ANFIS) classifiers. In the fourth proposed strategy, the concepts of Correntropy spectral density and Lyapunov exponent with Rosenstein algorithm is implemented and then the multi distance signal level difference is computed followed by the calculation of the Geodesic minimum distance to the Riemannian means and finally tangent space mapping is implemented to it before feeding it to classification. In the fifth or final proposed strategy, the Hilbert Huang Transform (HHT) is implemented and then the Hilbert marginal spectrum is computed. Then using the Blackhole optimization algorithm, the features are selected and finally it is classified with Cascade Adaboost classifier. The proposed techniques are applied on publicly available EEG datasets and the best result of 99.13% is obtained when the proposed Correntropy spectral density and Lyapunov exponent with Rosenstein algorithm is implemented with the multi distance signal level difference followed by the calculation of the Geodesic minimum distance to the Riemannian means and finally tangent space mapping is implemented with Support Vector Machine (SVM) classifier.