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

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

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...

Full description

Bibliographic Details
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

Internet

http://umpir.ump.edu.my/id/eprint/21377/1/Feature%20Selection%20using%20Binary%20Simulated%20Kalman%20Filter%20for%20Peak%20Classification1.pdf

Similar Items