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QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG

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

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|November 8, 2024
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A novel explainable feature engineering (XFE) model using Directed Lobish (DLob) effectively identifies stress from electroencephalography (EEG) signals. This approach achieves high accuracy in classifying stress versus control states in a large dataset.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Electroencephalography (EEG) is a cost-effective method for brain data collection.
  • EEG signal processing is vital for neuroscience and machine learning (ML).
  • Automated stress detection from EEG is an emerging area of research.

Purpose of the Study:

  • To introduce a new EEG stress dataset.
  • To propose an explainable feature engineering (XFE) model for automatic stress detection.
  • To utilize Directed Lobish (DLob) symbolic language for explainable results.

Main Methods:

  • Collected a new EEG stress dataset from 310 participants (stress and control classes).
  • Developed an XFE model involving channel transformation, QuadTPat feature generation, CWNCA feature selection, DLob for explainability, and tkNN classification.
  • Generated DLob strings for interpretable stress detection results.

Main Results:

  • The XFE model achieved 92.95% accuracy with 10-fold cross-validation.
  • Leave-one-subject-out (LOSO) cross-validation yielded 73.63% accuracy.
  • The QuadTPat-based XFE model demonstrated strong performance in EEG signal classification.

Conclusions:

  • The proposed QuadTPat-based XFE model is effective for EEG signal classification.
  • The XFE model integrated with DLob provides a valuable tool for explainable artificial intelligence (XAI).
  • This research contributes to advancing automated stress detection using interpretable ML techniques.