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A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection

Turker Tuncer1, Sengul Dogan1, Fatih Ertam1

  • 1Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.

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Summary

This study introduces an intelligent system using electroencephalogram (EEG) signals for automated driver fatigue detection. The novel framework achieved 97.29% accuracy, significantly improving traffic safety by identifying driver drowsiness.

Keywords:
Driver fatigue detectionElectroencephalogram (EEG)Textural feature extractionTexture transformation

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Driver fatigue is a primary cause of traffic accidents, necessitating effective detection methods.
  • Electroencephalogram (EEG) signals offer a viable means to assess brain function and detect fatigue.
  • Automated driver fatigue detection systems are crucial for enhancing road safety.

Purpose of the Study:

  • To develop an intelligent system for driver fatigue detection using EEG signals.
  • To propose a novel feature extraction and classification framework for improved fatigue detection accuracy.
  • To evaluate the effectiveness of the proposed system in real-world driving scenarios.

Main Methods:

  • The proposed framework involves pre-processing using Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT).
  • A new feature generation network utilizing texture descriptors, Dynamic Center Based Binary Pattern (DCB) and Multi Threshold Ternary Pattern (MTT) was developed.
  • Discrete Wavelet Transform (DWT) was employed for pooling and extracting functional brain network-based features, followed by a hybrid three-layered feature selection and shallow classifier evaluation.

Main Results:

  • The developed EEG-based system achieved a high classification accuracy of 97.29% for driver fatigue detection.
  • The novel feature generation network and selection method demonstrated superior performance compared to benchmark classifiers.
  • The functional brain network-based features effectively captured the relationship between fatigue and brain network organization.

Conclusions:

  • The proposed intelligent system is highly effective for automated driver fatigue detection using EEG signals.
  • The integration of texture descriptors, advanced feature extraction techniques, and robust classification methods enhances detection accuracy.
  • This research contributes to the development of advanced driver assistance systems for improved road safety.