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

• Main Authors: Badaruddin, Muhammad, Mohd Falfazli, Mat Jusof, Mohd Ibrahim, Shapiai, Asrul, Adam, Zulkifli, Md. Yusof, Kamil Zakwan, Mohd Azmi, Nor Hidayati, Abdul Aziz, Zuwairie, Ibrahim, Norrima, Mokhtar
• Format: Conference or Workshop Item
• Language: English
• Published: IEEE 2018
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/21377/1/Feature%20Selection%20using%20Binary%20Simulated%20Kalman%20Filter%20for%20Peak%20Classification1.pdf; http://umpir.ump.edu.my/id/eprint/21377/

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 pap...