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

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.

Voltage Variation Signals Source Identification and Diagnosis Method

“…From the literature, the 2 nd and 5 th repetitions will be used for the testing set, then the remaining will be applied as training set. The classification accuracy and precision [30], [31] will be presented as the performance evaluation. The classification accuracy is calculated as follows:…”

Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification

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A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

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“…In the process of fitness evaluation, the dataset was randomly partitioned into 80% for the training set and 20% for the testing set [4]. Furthermore, in order to measure the effectiveness of the proposed CBPSO-MIWS, four recent and popular feature selection methods include BPSO [13,22], genetic algorithm (GA) [28], binary gravitational search algorithm (BGSA) [29] and competitive binary grey wolf optimizer (CBGWO) [12] were used in comparison. GA is an evolutionary algorithm that utilizes the selection, crossover and mutation operators to evolve solutions.…”