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Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

Julio Ortega1, Javier Asensio-Cubero2, John Q Gan3

  • 1Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain. jortega@ugr.es.

Biomedical Engineering Online
|July 26, 2016
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Summary

This study introduces evolutionary multiobjective feature selection for brain-computer interfaces (BCI) using electroencephalographic (EEG) signals. The proposed methods improve classification performance while significantly reducing computational features.

Keywords:
Brain-computer interfaces (BCI)EEG classificationFeature selectionImagery tasks classificationMultiobjective optimizationMultiresolution analysis (MRA)

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-computer interface (BCI) applications using electroencephalographic (EEG) signals face challenges with high-dimensional data and limited training samples, leading to the curse of dimensionality.
  • Multiresolution analysis (MRA) is valuable for EEG signal analysis but often increases data dimensionality, necessitating feature selection or reduction for effective BCI.

Purpose of the Study:

  • To investigate and propose novel feature selection methods within MRA-based frameworks for BCI applications.
  • To address the curse of dimensionality in EEG signal classification for improved BCI performance.

Main Methods:

  • Development of several wrapper approaches for evolutionary multiobjective feature selection tailored for MRA-based BCI.
  • Evaluation of proposed methods against baseline approaches, including those using sparse feature representation and no feature selection.

Main Results:

  • Statistical analysis using Kolmogorov-Smirnoff and Kruskal-Wallis tests demonstrated advantages of the proposed feature selection approaches.
  • The evolutionary multiobjective feature selection methods achieved classification performances comparable or superior to baseline MRA approaches.
  • A significant reduction in the number of computed features was observed with the proposed methods.

Conclusions:

  • The proposed evolutionary multiobjective feature selection techniques effectively enhance MRA-based BCI systems.
  • These methods offer a viable solution to the curse of dimensionality in EEG signal processing for BCI.
  • The study highlights the potential for improved BCI performance and computational efficiency through advanced feature selection.