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Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization

K. G., Li and Mohd Ibrahim , Shapiai and Asrul, Adam and Zuwairie, Ibrahim (2017) Feature Scaling For EEG Human Concentration Using Particle Swarm Optimization. In: International Conference on Information Technology and Electrical Engineering (ICITEE), 5 - 6 October 2016 , Yogyakarta, Indonesia. . ISBN 978-150904139-8

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Abstract

Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state becomes a challenging task due to the nature of EEG signals is a non-stationary. In the past research, various combinations of features have been employed. However, to improve the classification performance, determining the importance of each employed feature is crucially needed. In this study, feature scaling method is introduced to assign different weights for important features. Four different features are investigated in frequency domain using wavelet transform (WT). Then, particle swarm optimization (PSO) is used to scale the features while extreme learning machine (ELM) is used to classify between concentration and non-concentration states. The recorded EEG signals from Neurosky Mindwave are used to evaluate the performance of the proposed technique. The final results indicate that the proposed technique offers higher performance accuracy as compared to the methods without feature scaling.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Electroencephalogram (EEG); Wavelet transform (WT); Extreme Learning Machine (ELM); Particle Swarm Optimization (PSO); Human Concentration.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 21 Mar 2018 07:39
Last Modified: 21 Mar 2018 07:39
URI: http://umpir.ump.edu.my/id/eprint/18259
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