Ingenieria Electronica

Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2010, Article ID 135629, 5 pages doi:10.1155/2010/135629

Research Article On the Use of Electrooculogram for Efficient Human Computer Interfaces
A. B. Usakli,1, 2 S. Gurkan,2 F. Aloise,1 G. Vecchiato,1 and F. Babiloni1
1 IRCCS 2 Department

Fondazione Santa Lucia, Via Ardeatina, 306, 00179, Rome, Italy ofTechnical Sciences, The NCO Academy, 10100 Balikesir, Turkey

Correspondence should be addressed to A. B. Usakli, ausakli@yahoo.com Received 13 June 2009; Accepted 28 July 2009 Academic Editor: Fabrizio De Vico Fallani Copyright © 2010 A. B. Usakli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited. 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 otherillnesses 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 thanP300-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.

1. Introduction
An efficient alternative channel for communication without speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis orother illnesses that prevent correct limb and facial muscular responses. In this respect, the area of study related to the Human Computer Interaction and Brain Computer Interface (BCI) is very important in hopes of improving the medium term quality of the life for such patients. In eye movements, a potential across the cornea and retina exists, and it is source of electrooculogram (EOG). EOG canbe modeled by a dipole [1], and these systems can be used in medical systems. There are several EOG-based HCI studies in literature. A wheelchair controlled with eye movements is developed for the disabled and elderly people. Eye movement signals and sensor signals are combined, and both direction and acceleration are controlled [2]. Using horizontal and vertical eye movements and two and threeblinking signals a movable robot is controlled [3]. Because the EOG signals are slightly different for the each subject, a dynamical threshold algorithm is developed [4]. In this approach, the initial threshold is compared with the dynamic range; the threshold value is renewed after each

difference. According to this threshold the output signal is made 1 or 0 and afterwards it is processed. EOG, EEGand electromyogram (EMG) signals are classified in real time, and movable robots are controlled by using artificial neural network classifier [5, 6]. Investigating possibility of usage of the EOG for HCI, a relation between sight angle and EOG is determined [7]. The human-machine interface which provides control of machines for disabled people is called the Man Machine Interface (MMI). Generally, ifthe control is computerbased, it is called the Human Computer Interface (HCI) instead of MMI. If the assistive system is based on electroencephalogram (EEG), it is called BCI, and its applications are increasing for severely disabled people. BCI is a direct communication pathway between the brain and an external device. The BCI systems translate brain activity into electrical signals that...