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An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification.

Bhuvaneshwari M1, Grace Mary Kanaga E1, Kumudha Raimond1

  • 1Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, TN, India.

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Summary

This study introduces an optimized ensemble learning model for brain-computer interfaces using Harris Hawk Optimization and Boruta Feature Selection. The approach significantly improves electroencephalogram signal classification accuracy for assistive technologies.

Keywords:
Harris Hawk optimizationSteady state visually evoked potentialbaggingboostingbrain computer interfaceensemble modelstacking

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Area of Science:

  • Neuroscience
  • Computer Science
  • Machine Learning

Background:

  • Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) are crucial for healthcare solutions, particularly for individuals with paralysis.
  • While ensemble learning enhances classifier performance, selecting optimal subsets is time-consuming.

Purpose of the Study:

  • To develop an efficient multi-classifier model for EEG signal classification.
  • To address the challenge of optimal classifier subset selection in ensemble learning.

Main Methods:

  • Utilized the Harris Hawk Optimization algorithm for selecting the best classifier subset.
  • Employed the Boruta Feature Selection algorithm to identify prominent EEG signal features.
  • Integrated selected features into an optimized multi-classifier ensemble model.

Main Results:

  • Achieved high accuracies with ensemble techniques: Stacking (96.1%), Bagging (98.7%), Boosting (91.91%), and Voting (99.01%).
  • Demonstrated superior performance in multi-class classification problems.
  • Validated with sensitivity, specificity, and F1-Score metrics.

Conclusions:

  • The proposed Harris Hawk Optimization-based ensemble model offers a highly accurate and efficient solution for EEG signal classification.
  • This advancement holds significant potential for improving BCIs and assistive technologies for individuals with paralysis.