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Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals

Badaruddin, Muhammad and Mohd Falfazli, Mat Jusof and Mohd Ibrahim, Shapiai and Asrul, Adam and Zulkifli, Md. Yusof and Kamil Zakwan, Mohd Azmi and Nor Hidayati, Abdul Aziz and Zuwairie, Ibrahim and Norrima, Mokhtar (2018) Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals. In: 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS2018), 8-10 May 2018 , Kuala Lumpur, Malaysia. pp. 1-6.. ISBN 978-1-5386-6539-8

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Abstract

Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this paper, another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection problem. It is found that the BSKF algorithm performed slightly better than the AMSKF algorithm.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: EEG, feature selection, peak, simulated Kalman filter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Prof. Madya Dr. Zuwairi Ibrahim
Date Deposited: 12 Sep 2018 08:43
Last Modified: 12 Sep 2018 08:43
URI: http://umpir.ump.edu.my/id/eprint/21377
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