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Idendifying Eye Movements using Neural Networks for Human Computer Interaction

by Hema.c.r, Ramkumar.s, Paulraj.m.p
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 8
Year of Publication: 2014
Authors: Hema.c.r, Ramkumar.s, Paulraj.m.p
10.5120/18397-9658

Hema.c.r, Ramkumar.s, Paulraj.m.p . Idendifying Eye Movements using Neural Networks for Human Computer Interaction. International Journal of Computer Applications. 105, 8 ( November 2014), 18-26. DOI=10.5120/18397-9658

@article{ 10.5120/18397-9658,
author = { Hema.c.r, Ramkumar.s, Paulraj.m.p },
title = { Idendifying Eye Movements using Neural Networks for Human Computer Interaction },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number8/18397-9658/ },
doi = { 10.5120/18397-9658 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:10.422971+05:30
%A Hema.c.r
%A Ramkumar.s
%A Paulraj.m.p
%T Idendifying Eye Movements using Neural Networks for Human Computer Interaction
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 8
%P 18-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrooculography based bio signals have been used and applied as a control signal in several Human Computer Interactions. EOG is a technique of recording corneal- retinal potential associated with eye movement. An HCI captures and decodes EOG signals and transforms human eye movement into actions. This paper proposes algorithms for identifying eleven eye movement signals acquired from twenty subjects using static and dynamic networks. Convolution technique is used to extract the features. These features are trained and tested with two neural networks, namely time delay neural network and feed forward neural network. The results obtained are compared with Singular Value Decomposition features for same networks. Classification accuracies varied from 90. 99% and 90. 10% for convolution features and 90. 88% and 89. 92% for SVD features using time delay neural network and feed forward neural network respectively. From the results it is observed that Convolution features using Time Delay Neural Network has better classification rates in comparison with SVD features.

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Index Terms

Computer Science
Information Sciences

Keywords

Electrooculography Human Computer Interaction Convolution Features Singular Value Decomposition Feed Forward Neural Network Time Delay Neural Network Multi Layer Perceptron Fast Fourier Transform.

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