An electrooculogram-based binary saccade sequence classification (BSSC) technique for augmentative communication and control

In the field of assistive technology, the electroocu-logram (EOG) can be used as a channel of communication and the basis of a man-machine interface. For many people with severe motor disabilities, simple actions such as changing the TV channel require assistance. This paper describes a method of detecting saccadic eye movements and the use of a saccade sequence classification algorithm to facilitate communication and control. Saccades are fast eye movements that occurs when a person's gaze jumps from one fixation point to another. The classification is based on pre-defined sequences of saccades, guided by a static visual template (e.g. a page or poster). The template, consisting of a table of symbols each having a clearly identifiable fixation point, is situated within view of the user. To execute a particular command, the user moves his or her gaze through a pre-defined path of eye movements. This results in a well-formed sequence of saccades which are translated into a command if a match is found in a library of predefined sequences. A coordinate transformation algorithm is applied to each candidate sequence of recorded saccades to mitigate the effect of changes in the user's position and orientation relative to the visual template. Upon recognition of a saccade sequence from the library, its associated command is executed. A preliminary experiment in which two subjects were instructed to perform a series of command sequences consisting of 8 different commands are presented in the final sections. The system is also shown to be extensible to facilitate convenient text entry via an alphabetic visual template.

[1]  B. Estrany,et al.  Accurate interaction with computer by eye movement tracking , 2008 .

[2]  Robin Jeffries,et al.  CHI '06 Extended Abstracts on Human Factors in Computing Systems , 2006, CHI 2006.

[3]  G. Norris,et al.  The Eye Mouse, an eye communication device , 1997, Proceedings of the IEEE 23rd Northeast Bioengineering Conference.

[4]  Masaaki Fukumoto,et al.  Full-time wearable headphone-type gaze detector , 2006, CHI Extended Abstracts.

[5]  A.R. Kherlopian,et al.  Electrooculogram based system for computer control using a multiple feature classification model , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Junichi Hori,et al.  Development of EOG-Based Communication System Controlled by Eight-Directional Eye Movements , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Sunghoon Kwon,et al.  EOG-based glasses-type wireless mouse for the disabled , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[9]  S. Venkataramanan,et al.  Biomedical instrumentation based on electrooculogram (EOG) signal processing and application to a hospital alarm system , 2005, Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005..