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Computation 2019, 7(1), 12; https://doi.org/10.3390/computation7010012

EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization

1
Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia
2
Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka 76100, Malaysia
*
Authors to whom correspondence should be addressed.
Received: 24 January 2019 / Revised: 15 February 2019 / Accepted: 15 February 2019 / Published: 22 February 2019
(This article belongs to the Section Computational Engineering)
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Abstract

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature selection is an effective process for eliminating redundant and irrelevant features. In this paper, we propose a new personal best (Pbest) guide binary particle swarm optimization (PBPSO) to solve the feature selection problem for EMG signal classification. First, the discrete wavelet transform (DWT) decomposes the signal into multiresolution coefficients. The features are then extracted from each coefficient to form the feature vector. After which pbest-guide binary particle swarm optimization (PBPSO) is used to evaluate the most informative features from the original feature set. In order to measure the effectiveness of PBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA), modified binary tree growth algorithm (MBTGA), and binary differential evolution (BDE) were used for performance comparison. Our experimental results show the superiority of PBPSO over other methods, especially in feature reduction; where it can reduce more than 90% of features while keeping a very high classification accuracy. Hence, PBPSO is more appropriate for application in clinical and rehabilitation applications. View Full-Text
Keywords: feature selection; classification; electromyography; binary particle swarm optimization; genetic algorithm; binary differential evolution; discrete wavelet transform feature selection; classification; electromyography; binary particle swarm optimization; genetic algorithm; binary differential evolution; discrete wavelet transform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Too, J.; Abdullah, A.R.; Mohd Saad, N.; Tee, W. EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization. Computation 2019, 7, 12.

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