Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016, Article ID 6898031, 9 pages
Research Article

Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM

1Department of Information Processing, Tokyo Institute of Technology, Yokohama, Japan
2Division of Information Sciences, Chiba University, Chiba, Japan
3Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan
4Brain Functions Laboratory Inc., Yokohama, Japan

Received 22 March 2016; Revised 24 August 2016; Accepted 31 August 2016

Academic Editor: Justin Dauwels

Copyright © 2016 Fuming Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease. To detect eye motions, electrooculography (EOG) is used. For efficient communication, the input speed is critical. However, it is difficult for conventional EOG recognition methods to accurately recognize fast, sequentially input eye motions because adjacent eye motions influence each other. In this paper, we propose a context-dependent hidden Markov model- (HMM-) based EOG modeling approach that uses separate models for identical eye motions with different contexts. Because the influence of adjacent eye motions is explicitly modeled, higher recognition accuracy is achieved. Additionally, we propose a method of user adaptation based on a user-independent EOG model to investigate the trade-off between recognition accuracy and the amount of user-dependent data required for HMM training. Experimental results show that when the proposed context-dependent HMMs are used, the character error rate (CER) is significantly reduced compared with the conventional baseline under user-dependent conditions, from 36.0 to 1.3%. Although the CER increases again to 17.3% when the context-dependent but user-independent HMMs are used, it can be reduced to 7.3% by applying the proposed user adaptation method.