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

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

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to...

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Bibliographic Details
Main Authors: Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Marizan, Mubin, Ismail, Saad
Format: Article
Language:English
Published: Springer 2016
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/14661/
http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf

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