Automatic detection of the REM sleep phase during electrooculography

  • 1 Institute of Population and Human Studies, Bulgarian Academy of Science, Sofia, Bulgaria
  • 2 Sofia University St. Kliment Ohridski


Sleep is not just a rest; it is a necessary part of the functioning of the cognitive system of people. Studying the role of sleep for effective functioning of the immune system, temperature regulation, memory, emotional regulation, learning and many other physiological and psychological processes is gaining more and more relevance. It attracts the attention of many leading researchers from around the world. The study of sleep by electrooculography (using three electrodes – two on the temples and one on the forehead) to track eye movements has a significant advantage over the more commonly used EEG methods due to its lower cost and the ability to quickly and efficiently collect large databases. A software-implemented algorithm for automatic recognition of the REM sleep phase during sleep is presented. This algorithm is a step of a larger project to create a system for external control of the content of dreams during REM sleep by providing scents and sounds, pre-associated with various stimuli and symbols. This system will allow in the future applying an automatic external influence on the sleeper during the REM phase. The development will have applications at research on the induction of selected elements during dreaming. This can help to people with post-traumatic stress disorder and phobias, as well for a more effective learning.



  1. Walker, M. Why we sleep: Unlocking the power of sleep and dreams. Simon and Schuster (2017)
  2. Moorcroft, W. H., & Belcher, P. Understanding sleep and dreaming (pp. 168-169). New York, NY, USA:: Kluwer Academic/Plenum Publishers (2003)
  3. Aserinsky, E., & Kleitman, N. Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science, 118(3062), 273-274 (1953)
  4. Carskadon, M.A., & Dement, W.C. Monitoring and staging human sleep. In M.H. Kryger, T. Roth, & W.C. Dement (Eds.), Principles and practice of sleep medicine, 5th edition, (pp 16-26). St. Louis: Elsevier Saunders (2011)
  5. Nishida M, Walker MP (2007) Daytime Naps, Motor Memory Consolidation and Regionally Specific Sleep Spindles. PLoS ONE 2(4): e341. (2007)
  6. Armitage, R. The distribution of EEG frequencies in REM and NREM sleep stages in healthy young adults. Sleep, 18(5), 334-341 (1995).
  7. Smith, J. R., Karacan, I., & Yang, M. (1978). Automated analysis of the human sleep EEG. Waking & Sleeping (1978).
  8. Ibáñez, V., Silva, J., & Cauli, O. A survey on sleep assessment methods. PeerJ, 6, e4849 (2018).
  9. Tran, S., & Prober, D. A. Methods to Study Sleep in Zebrafish. In Circadian Clocks (pp. 259-286). New York, NY:Springer US (2022).
  10. Takahashi, K., & Atsumi, Y. Precise measurement of individual rapid eye movements in REM sleep of humans. Sleep, 20(9), 743-752 (1997)
  11. Simor, P., van der Wijk, G., Nobili, L., & Peigneux, P. The microstructure of REM sleep: why phasic and tonic?. Sleep medicine reviews, 52, 101305 (2020)
  12. Yetton, B. D., Niknazar, M., Duggan, K. A., McDevitt, E. A., Whitehurst, L. N., Sattari, N., & Mednick, S. C. Automatic detection of rapid eye movements (REMs): A machine learning approach. Journal of neuroscience methods, 259, 72-82 (2016)

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