Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications-Reference-Cited by-同舟云学术

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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

Published:2017-03-23 Issue:5 Volume:42 Page:641-653
ISSN:0256-2499
Container-title:Sādhanā
language:en
Short-container-title:Sādhanā

Author:

Adam Asrul,Ibrahim Zuwairie,Mokhtar Norrima,Shapiai Mohd Ibrahim,Cumming Paul,Mubin Marizan

Funder

High Impact Research Fund

MyPhD scholarship

Research University Grant

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://link.springer.com/content/pdf/10.1007/s12046-017-0633-9.pdf

Reference33 articles.

1. Acir N, Oztura I, Kuntalp M, Baklan B and Guzelis C 2005 Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks. IEEE Trans. Biomed. Eng. 52: 30–40. doi:10.1109/TBME.2004.839630

2. Elgendi M, Norton I, Brearley M, Abbott D and Schuurmans D 2013 Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions. PLoS One 8: e76585. doi: 10.1371/journal.pone.0076585

3. Adam A, Shapiai M I, Mohd Tumari M Z, Mohamad M S and Mubin M 2014 Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization. Sci. World J. 2014: Article ID 973063. doi: 10.1155/2014/973063 .

4. Fürbassa F, Hartmann M M, Halford J J, Koren J, Herta J,Gruber A, Baumgartner C and Kluge T 2015 Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology. Clin. Neurophysiol. 45: 203–213

5. Zander T O, Gaertner M, Kothe C and Vilimek R 2011 Combining eye gaze input with a brain–computer interface for touchless human–computer interaction. Int. J. Hum. Comput. Interact. 27(1): 38–51

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