EEG Channel Selection using Differential Evolution Algorithm and Particle Swarm Optimization for Classification of Odorant-Stimulated Records

Abstract

A significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant-unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbor), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.

Keywords

• DEA
• EEG Channel Selection
• evolutionary computing
• PSO
• swarm intelligence

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