A novel swarm based feature selection algorithm in multifunction myoelectric control

  • Authors:

  • R. N. Khushaba;A. Alsukker;A. Al-Ani;A. Al-Jumaily;A. Y. Zomaya

  • Affiliations:

  • (Correspd. Tel.: +612 9514 3140. E-mail: rkhushab@eng.uts.edu.au (R.N. Khushaba)) Mechatronics and Intelligent Systems Group, Faculty of Engineering, University of Technology, Sydney, P.O. Box 123 ...;Mechatronics and Intelligent Systems Group, Faculty of Engineering, University of Technology, Sydney, P.O. Box 123, Broadway 2007, Sydney - NSW, Australia;Mechatronics and Intelligent Systems Group, Faculty of Engineering, University of Technology, Sydney, P.O. Box 123, Broadway 2007, Sydney - NSW, Australia;Mechatronics and Intelligent Systems Group, Faculty of Engineering, University of Technology, Sydney, P.O. Box 123, Broadway 2007, Sydney - NSW, Australia;School of Information Technologies, Building J12, University of Sydney, NSW 2006, Australia

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 2009

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Abstract

Accurate and computationally efficient myoelectric control strategies have been the focus of a great deal of research in recent years. Although many attempts exist in literature to develop such strategies, deficiencies still exist. One of the major challenges in myoelectric control is finding an optimal feature set that can best discriminate between classes. However, since the myoelectric signal is recorded using multi channels, the feature vector size can become very large. Hence a dimensionality reduction method is needed to identify an informative, yet small size feature set. This paper presents a new feature selection method based on modifying the Particle Swarm Optimization (PSO) algorithm with the inclusion of Mutual Information (MI) measure. The new method, called BPSOMI, is a mixture of filter and wrapper approaches of feature selection. In order to prove its efficiency, the proposed method is tested against other dimensionality reduction techniques proving powerful classification accuracy.