Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications

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Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications

The optimization of peak detection algorithms for electroencephalogram (EEG) signal analysis is an ongoing project; previously existing algorithms have been used with different models to detect EEG peaks in various applications. However, none of the existing techniques perform adequately in eye even...

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Bibliographic Details
Main Authors:	Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Cumming, Paul, Marizan, Mubin
Format:	Article
Language:	English English
Published:	Springer India 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/17480/ http://umpir.ump.edu.my/id/eprint/17480/1/Improving%20EEG%20signal%20peak%20detection%20using%20feature%20weight%20learning%20of%20a%20neural%20network%20with%20random%20weights%20for%20eye%20event-related%20applications.pdf http://umpir.ump.edu.my/id/eprint/17480/3/Improving%20EEG%20signal%20peak%20detection%20using%20feature%20weight%20learning%20of%20a%20neural%20network%20with%20random%20weights%20for%20eye%20event-related%20applications%201.pdf

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