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Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity

Ahmadi Amirmasoud, Bazregarzadeh Hanieh, Kazemi Kamran

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 1

316--332

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.

Vision-based solutions for driver assistance

Nowosielski A.

Journal of Theoretical and Applied Computer Science

2014

Vol. 8, nr 4

35--44

The article presents a review on vision-based solutions for driver assistance. These solutions support the driver to keep safe travel conditions. They use diverse sensing modalities for the recognition of the environment around the vehicle. Upon detection a critical safety situation they supply the driver with the warning. Four assistance systems have been addressed: TSR - Traffic Sign Recognition, CAV - Collision Avoidance, LDW - Lane Departure Warning, and driver fatigue detection. Their structure and some existing approaches are presented. Furthermore, a solution for lane detection and another one for a driver fatigue detection are proposed in the article. They are prepared as the combination of existing image processing algorithms with the aim of presentation the ease of own limited solution creation. For the real-world and diverse working scenarios they would require a great deal of improvements.