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EEG signal classification based on SVM with improved squirrel search algorithm.

Miao Shi1, Chao Wang1, Xian-Zhe Li2

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Summary
This summary is machine-generated.

This study introduces an improved squirrel search algorithm (ISSA) to optimize support vector machine (SVM) parameters for classifying electroencephalography (EEG) signals, achieving 85.9% accuracy.

Keywords:
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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Electroencephalography (EEG) signals offer valuable physiological insights.
  • Accurate classification of EEG signals is crucial for research and diagnostics.
  • Existing methods for EEG signal analysis and classification require optimization.

Purpose of the Study:

  • To propose a novel pattern recognition method for EEG signal classification.
  • To enhance the performance of Support Vector Machine (SVM) through parameter optimization.
  • To introduce an Improved Squirrel Search Algorithm (ISSA) for optimizing SVM parameters.

Main Methods:

  • EEG signals were preprocessed, and time-domain features were extracted.
  • An Improved Squirrel Search Algorithm (ISSA), incorporating chaos and reverse learning, was developed.
  • The ISSA was used to optimize SVM parameters, creating the ISSA-SVM model.

Main Results:

  • The ISSA demonstrated improved exploration ability and convergence speed in benchmark tests.
  • The ISSA-SVM model achieved an average classification accuracy of 85.9% for EEG signals.
  • This accuracy represents a 2-5% improvement over existing SVM parameter optimization models.

Conclusions:

  • The ISSA-SVM model provides an effective approach for EEG signal classification.
  • The proposed optimization algorithm significantly enhances SVM performance in this domain.
  • This method holds promise for advancing physiological information extraction from EEG data.