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JACIII Vol.12 No.1 pp. 32-40
doi: 10.20965/jaciii.2008.p0032
(2008)

Paper:

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Eye Position Estimation During Sleep Using Infrared Video in Functional MRI

Syoji Kobashi*, Yuji Yahata*, Shigeyuki Kan**,
Masaya Misaki**, Takahiko Koike**, Katsuya Kondo*,
Satoru Miyauchi**, and Yutaka Hata*

*Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, Hyogo 671-2280, Japan

**CREST – Brain Function Imaging Team, Kobe Advanced ICT Research Center, National Institute of Information and Communications Technology, Japan

Received:
May 1, 2007
Accepted:
August 30, 2007
Published:
January 20, 2008
Creative Commons License
Keywords:
eye position, sleep, REM, artificial neural network, infrared video
Abstract
The determination of eye position during sleep attracted much attention in sleep study. Simultaneous measurement consisting of functional magnetic resonance imaging and infrared video was developed to study the relationship between eye movement and brain function during sleep. Conventional measurement of the eye position using infrared video images is not applicable to experiments during sleep because of the lack of tracking targets such as the pupil. This paper proposes a novel method for determining the eye position during sleep through infrared video images. This method determines the eye position by comparing intensity profile extracted from an image to intensity profiles generated by the artificial neural network (ANN). Experiments showed that the proposed method detected the eye position of the left eye with an error of 3.56 ± 3.61 (RMSE ± SD) pixels and that of the right eye with an error of 4.69 ± 4.73 pixels.
Cite this article as:
S. Kobashi, Y. Yahata, S. Kan, M. Misaki, T. Koike, K. Kondo, S. Miyauchi, and Y. Hata, “Eye Position Estimation During Sleep Using Infrared Video in Functional MRI,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.1, pp. 32-40, 2008.
Data files:
References
  1. [1] E. Aserinsky and N. Kleitman, “Regularly Occurring Periods of Eye Motility, and Concomitant Phenomena, During Sleep,” Science, Vol.118, No.3062, pp. 273-274, 1953.
  2. [2] M. Brown, M. Marmor, Vaegan, E. Zrenner, M. Brigell, and M. Bach, “ISCEV Standard for Clinical Electro-oculography (EOG) 2006,” Documenta Ophthalmologica, Vol.113, No.3, pp. 205-212, 2006.
  3. [3] D. A. Robinson, “A method of Measuring Eye Movement Using a Scleral Search Coil in a Magnetic Field,” IEEE Trans. on Biomedical Engineering, Vol.10, pp. 137-145, 1963.
  4. [4] A. Duchowski and R. Vertegaal, “Eye-based Interaction in Graphical Systems: Theory & Practice,” ACM SIGGRAPH 2000, Course Notes #5, 2000.
  5. [5] J. G. Wang and E. Sung, “Study on Eye Gaze Estimation,” IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics, Vol.32, No.3, pp. 332-350, 2002.
  6. [6] C. H. Morimoto, D. Koons, A. Amir, and M. Flickner, “Pupil Detection and Tracking Using Multiple Light Sources,” Image and Vision Computing, Vol.18, No.4, pp. 331-335, 2000.
  7. [7] S. Baluja and D. Pomerleau, “Non-intrusive gaze tracking using artificial neural networks,” Advances in Neural Information Processing Systems, CMU-CS-94-102, pp. 753-760, 1994.
  8. [8] B. Wolfe and D. Eichmann, “A Neural Network Approach to Tracking Eye Position,” International Journal of Human-Computer Interaction, Vol.9, No.1, pp. 59-79, 1997.
  9. [9] R. J. Jacob, “What you look at is what you get: eye movementbased interaction techniques,” Proc. of the ACM CHI 90 on Human Factors in Computing Systems, pp. 11-18, ACM Press, 1990.
  10. [10] Q. Ji, Z. Zhu, and P. Lan, “Real Time Non-intrusive Monitoring and Prediction of Driver Fatigue,” IEEE Trans. on Vehicular Technology, Vol.53, No.4, pp. 1052-1068, 2004.
  11. [11] A. Duchowski, “A breadth-first survey of eye-tracking applications,” Behavior research methods, instruments, & computers, Vol.34, No.4, pp. 455-470, 2002.
  12. [12] S. Miyauchi, M. Misaki, Y. Tanaka, T, Fukunaga, and S. Kan, “fMRI activation accompanying rapid eye movements during REM sleep,” 10th Annual Meeting of The Organization for Human Brain Mapping, Budapest, Hungary, 2004.
  13. [13] Y. Yahata, S. Kobashi, S. Kan, M. Misaki, K. Kondo, S. Miyauchi, and Y. Hata, “Neural Network based Eye Position Estimation from Infrared Video during Sleeping,” Proc. of Joint 3rd Int. Conf. on Soft Computing and Intelligent Systems and 7th Int. Sympo. on Advanced Intelligent System, pp. 2011-2016, Sep. 2006.
  14. [14] S. Kobashi, Y. Yahata, S. Kan, M. Misaki, K. Kondo, S. Miyauchi, and Y. Hata, “Fully Automated Detection of Eye Movement on Sleep based on Neural Network Modeling,” Proc. of 2nd Int. Sympo. on Computational Intelligence and Industrial Applications, pp. 269-273, 2006.
  15. [15] S. Haykin, “Neural Network: A Comprehensive Foundation, second edition,” Prentice Hall, 1999.

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