Literature DB >> 17946887

Electrooculogram based system for computer control using a multiple feature classification model.

Armen R Kherlopian1, Joseph P Gerrein, Minerva Yue, Kristina E Kim, Ji Won Kim, Madhav Sukumaran, Paul Sajda.   

Abstract

This paper discusses the creation of a system for computer-aided communication through automated analysis and processing of electrooculogram signals. In situations of disease or trauma, there may be an inability to communicate with others through standard means such as speech or typing. Eye movement tends to be one of the last remaining active muscle capabilities for people with neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) also known as Lou Gehrig's disease. Thus, there is a need for eye movement based systems to enable communication. To meet this need, the Telepathix system was designed to accept eye movement commands denoted by looking to the left, looking to the right, and looking straight ahead to navigate a virtual keyboard. Using a ternary virtual keyboard layout and a multiple feature classification model, a typing speed of 6 letters per minute was achieved.

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Year:  2006        PMID: 17946887     DOI: 10.1109/IEMBS.2006.260851

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

Review 1.  EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface.

Authors:  Sarah M Hosni; Howida A Shedeed; Mai S Mabrouk; Mohamed F Tolba
Journal:  Neuroinformatics       Date:  2019-07

2.  Sensory system for implementing a human-computer interface based on electrooculography.

Authors:  Rafael Barea; Luciano Boquete; Jose Manuel Rodriguez-Ascariz; Sergio Ortega; Elena López
Journal:  Sensors (Basel)       Date:  2010-12-29       Impact factor: 3.576

3.  Removing the Interdependency between Horizontal and Vertical Eye-Movement Components in Electrooculograms.

Authors:  Won-Du Chang; Ho-Seung Cha; Chang-Hwan Im
Journal:  Sensors (Basel)       Date:  2016-02-14       Impact factor: 3.576

4.  Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification.

Authors:  Jayro Martínez-Cerveró; Majid Khalili Ardali; Andres Jaramillo-Gonzalez; Shizhe Wu; Alessandro Tonin; Niels Birbaumer; Ujwal Chaudhary
Journal:  Sensors (Basel)       Date:  2020-04-25       Impact factor: 3.576

  4 in total

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