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Multi optimized SVM classifiers for motor imagery left and right hand movement identification

  • Kamel MebarkiaEmail author
  • Aicha Reffad
Scientific Paper
  • 39 Downloads

Abstract

EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG signals. Brain task identification based on EEG signals is very difficult task and is still challenging researchers. In this paper, the motor imagery of left and right hand actions are identified using new features which are fed to a set of optimized SVM classifiers. Multi classifiers based classification showed having high faculty to improve the classification accuracy when using different kind or diversified features. Features selection was performed by genetic algorithm optimization. In single optimized SVM classifier, a mean classification accuracy of 89.8% was reached. To further improve the rate of classification, three SVMs classifiers have been suggested and optimized in order to find suitable features for each classifier. The three SVMs classifiers were optimized and achieved a performance mean of 94.11%. The achieved performance is a significant improvement comparing to the existing methods which does not exceed 81% while using the same database. Here, combining multi classifiers with selecting suitable features by optimization can be a good alternative for BCI applications.

Keywords

Electroencephalography EEG signals Motor imagery BCI (MI) Features extraction BCI system SVM classifier Optimization 

Notes

Acknowledgements

The authors would like to acknowledge the Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology, for providing the dataset online (https://www.bbci.de/competition/iii).

Compliance with ethical standards

Conflict of interest

No conflicts of interest are declared by the authors.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.LIS Laboratory, Electronics Department, Faculty of TechnologySétif 1 UniversitySétifAlgeria
  2. 2.LAS Laboratory, Electrotechnics Department, Faculty of TechnologySétif 1 UniversitySétifAlgeria

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