Automated eye tracking system calibration using artificial neural networks

There are no files associated with this record.

Title Automated eye tracking system calibration using artificial neural networks
Author Coughlin, Michael; Cutmore, Timothy; Hine, Trevor John
Journal Name Computer Methods and Programs in Biomedicine
Year Published 2004
Place of publication Ireland
Publisher Elsevier Ireland Ltd
Abstract The electro-oculogram (EOG) continues to be widely used to record eye movements especially in clinical settings. However, an efficient and accurate means of converting these recordings into eye position is lacking. An artificial neural network (ANN) that maps two-dimensional (2D) eye movement recordings into 2D eye positions can enhance the utility of such recordings. Multi-layer perceptrons (MLPs) with non-linear activation functions and trained with back propagation proved to be capable of calibrating simulated EOG data to a mean accuracy of 0.33°. Linear perceptrons (LPs) were only nearly half as accurate. For five subjects, the mean accuracy provided by the MLPs was 1.09° of visual angle (°) for EOG data, and 0.98° for an infrared eye tracker. MLPs enabled calibration of 2D saccadic EOG to an accuracy not significantly different from that obtained with the infrared tracker. Using initial weights trained on another person reduced MLP training time, reaching convergence in as little as 20 iterations.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.elsevier.com/wps/find/journaldescription.cws_home/505960/description#description
Copyright Statement Copyright 2004 Elsevier : Reproduced in accordance with the copyright policy of the publisher : This journal is available online - use hypertext links.
Volume 76
Page from 207
Page to 220
ISSN 0169-2607
Date Accessioned 2005-03-09
Date Available 2007-03-18T21:37:55Z
Language en_AU
Research Centre Griffith Health Institute; Centre for Wireless Monitoring and Applications; Behavioural Basis of Health
Faculty Griffith Health Faculty
Subject Neurocognitive Patterns and Neural Networks
URI http://hdl.handle.net/10072/5562
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

Brief Record

Griffith University copyright notice