Classification of EOG for Human Computer Interface

This paper uses Electro-ocular gram (EOG) to track the angular gaze of the eyes in order to determine a mathematical expression that is user independent.

Electrodes were placed on the subject’s face to gather signals. A test environment was set-up that gave the subjects “targets” to look at.

These targets were placed around the subject, in a sphere shape (see figure below). The subject was then directed to look at specified targets and the data was compiled.

After some filtering and linear regression magic, a model with high probability was constructed.

The findings show that the greater the angle, the less accurate the system. Peripheral vision was not well accounted for using this method.

Also, this method did not account for torso and other “non-eye” movement. Disabilities and “lazy-eye” were also not accounted for.


This paper appears in:
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
Date of Conference: 2002
Author(s): Kumar, D. 
Poole, E. 
Volume: 1
Page(s): 64 – 67 vol.1

About Frank Sposaro

Frank was the initial student to start the mobile lab with Dr. Tyson. After working on the first project, iFall, he and Dr. Tyson designed the Mobile Programming course as FSU. The course is used as a training base to recruit new students into the lab. His thesis researches several medical related applications, including iFall. Frank then went on to implement the redesign of the “favorite contacts” for Android’s Ice Cream Sandwich at Google HQ in Mountain View, California. He currently acts as a tech lead in the lab getting infrastructure and project management tools setup. He has special focus on native Android coding and UI design.