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A Classification of Motor Imagery Brain Signals Using Least Square Support Vector Machine and Chaotic Particles Swarm Optimization

  • Arwa N. Al-EdailyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 876)

Abstract

Motor Imagery signal is one of the brain signals that generated in the brain during moving status or when imagine a movement. It is one of the famous and most challenging research area in Brain Computer Interaction field. So, exploring a new combination of algorithms yields in improving this area more. In this paper, we try to find an effective classification method to classify Motor Imagery signals into two classes ‘left hand’ and ‘right hand’ movements with high accuracy. In this work we used Least Square Support Vector Machine classifier after choose its optimal parameters using Chaotic Particle Swarm Optimization search algorithm. The proposed algorithm has been tested on Graz data set III (Motor Imagery signals). The results indicate that the proposed approach produced good classification algorithm with high performance and accuracy up to 90%. The results show that it is a competitive classification method compared with other studies.

Keywords

BCI EEG Motor imagery LS-SVM PSO 

Notes

Acknowledgments

We deeply thank Dr. Hafida Benhidour for her valuable comments and guidance during this research. We would like also to thank Mrs. Reza ahmadzadeh for the victorized particle swarm optimization implementation version that available online.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Computer Science and Engineering DepartmentUniversity of HailHailSaudi Arabia

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