On the use of electrooculogram for efficient human computer interfaces

A. B. Usakli, S. Gurkan, F. Aloise, G. Vecchiato, F. Babiloni

Research output: Contribution to journalArticle

Abstract

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as clean-up could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes.

Original languageEnglish
Article number135629
JournalComputational Intelligence and Neuroscience
Volume2010
DOIs
Publication statusPublished - 2010

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Electrooculography
Human-computer Interface
Interfaces (computer)
Communication
Electroencephalography
Aptitude
Amyotrophic Lateral Sclerosis
Cost Efficiency
Quality of Life
Extremities
Hand
Efficiency
Costs
Costs and Cost Analysis
Lateral
Equipment and Supplies
Experiments
Alternatives
Experiment

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)
  • Neuroscience(all)

Cite this

On the use of electrooculogram for efficient human computer interfaces. / Usakli, A. B.; Gurkan, S.; Aloise, F.; Vecchiato, G.; Babiloni, F.

In: Computational Intelligence and Neuroscience, Vol. 2010, 135629, 2010.

Research output: Contribution to journalArticle

Usakli, A. B. ; Gurkan, S. ; Aloise, F. ; Vecchiato, G. ; Babiloni, F. / On the use of electrooculogram for efficient human computer interfaces. In: Computational Intelligence and Neuroscience. 2010 ; Vol. 2010.
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