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Related Experiment Video

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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A computational eye state classification model using EEG signal based on data mining techniques: comparative

Subhash Mondal1,2, Amitava Nag3

  • 1Computer Science & Engineering (AI & ML), Dayananda Sagar University, Bengaluru, Karnataka, India. ph22cse1001@cit.ac.in.

Physical and Engineering Sciences in Medicine
|August 5, 2025
PubMed
Summary
This summary is machine-generated.

This study accurately classifies eye states using Electroencephalogram (EEG) signals and machine learning. The k-Nearest Neighbours model achieved over 97% accuracy, demonstrating potential for real-time Brain-Computer Interface applications.

Keywords:
Classification modelCommon spatial patternElectroencephalogram (EEG)Eye state detectionk-Nearest neighbor (KNN)k-mean accuracy

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Artificial Intelligence (AI) shows promise in non-invasive healthcare diagnostics using bio-signals.
  • Electroencephalogram (EEG) signals are crucial for understanding brain activity.
  • Brain-Computer Interfaces (BCIs) leverage bio-signals for human-computer interaction.

Purpose of the Study:

  • To classify eye states (open/closed) using EEG signals.
  • To evaluate the effectiveness of various machine learning models for this classification task.
  • To explore the impact of signal processing and feature extraction techniques on classification accuracy.

Main Methods:

  • Utilized a publicly available dataset of 14,980 EEG signal instances.
  • Applied preprocessing steps: Z-score outlier removal, SMOTETomek for class imbalance, and bandpass filtering.
  • Performed feature selection using independent t-tests and feature extraction with Common Spatial Pattern (CSP).
  • Evaluated fourteen classical machine learning models via tenfold cross-validation.

Main Results:

  • Several classifiers achieved >90% accuracy.
  • k-Nearest Neighbours (k-NN) yielded the highest accuracy (97.92% with CSP, 97.75% without CSP).
  • CSP enhanced Multi-Layer Perceptron (95.30%) and Support Vector Machine (93.93%) performance.

Conclusions:

  • Integrating statistical validation, signal processing, and ML enables accurate EEG-based eye state classification.
  • The findings support practical applications in real-time BCIs.
  • This approach offers a lightweight solution for healthcare wearable devices.