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Related Experiment Video

Updated: Jan 18, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
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A Novel Hybrid Approach for Drowsiness Detection Using EEG Scalograms to Overcome Inter-Subject Variability.

Aymen Zayed1,2,3, Nidhameddine Belhadj4, Khaled Ben Khalifa2,5

  • 1Service d'électronique et de Microélectronique, University of Mons, 7000 Mons, Belgium.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
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This summary is machine-generated.

A new hybrid approach using convolutional neural networks (CNNs) and support vector machines (SVMs) effectively detects drowsiness from electroencephalography (EEG) signals. This method significantly improves accuracy and reduces variability for enhanced workplace safety.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Occupational Safety

Background:

  • Drowsiness is a major risk factor for accidents in various industries.
  • Electroencephalography (EEG) offers direct brain activity measurement for drowsiness detection.
  • EEG signal non-stationarity and inter-subject variability pose challenges for accurate detection.

Purpose of the Study:

  • To develop a robust drowsiness detection method using EEG signals.
  • To address challenges of EEG signal variability and improve detection accuracy.
  • To compare a novel hybrid CNN-SVM approach with existing methods.

Main Methods:

  • A hybrid framework combining Convolutional Neural Networks (CNNs) for feature extraction and Support Vector Machines (SVMs) for classification.
Keywords:
CNNEEGdeep learningdrowsinessscalogramtransfer learning

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  • Utilized Continuous Wavelet Transform (CWT) to generate 2D EEG scalograms for CNN feature extraction.
  • Compared the proposed CNN-SVM model with 1D CNNs and transfer learning models (VGG16, ResNet50) on the DROZY dataset.

    • Main Results:

    • The hybrid CNN-SVM model achieved a high accuracy of 98.33% in drowsiness detection.
    • The proposed method significantly outperformed 1D CNNs and transfer learning models.
    • The approach demonstrated effectiveness in minimizing inter-subject variability.

    Conclusions:

    • The hybrid CNN-SVM approach offers a robust and accurate solution for EEG-based drowsiness detection.
    • This method has significant potential for enhancing safety in high-risk occupational settings.
    • The use of 2D EEG scalograms with CNNs is a promising direction for future research.