Brain-computer interface: basic approaches. Part II. Interfaces based on eye movement and blood oxygenation levels

The final part of information-analytical review of the principles underlying the development of brain-computer interfaces is presented. In this part “dependent” interfaces based on video recording of eye movements (gaze direction), electro-oculogram registration, as well as on blood oxygenation indices, are considered. It is concluded that in all the cited studies the accuracy of recognition of the control signal rarely fell below 60%, and in some cases tended to more than 90%. Based on the analysis of the principles presented in two parts of the review, conclusions are drawn regarding the problems that prevent technology from taking a firm place in our lives – reaching a “plateau of productivity”. It is assumed that the main obstacle to the development of BCI technology is the focus of developers on the accuracy of recognition of the control signal in BCI systems without the necessary consideration of the convenience and ease of user interaction with the system. In addition it is noted that to realize the potential of brain-computer interface technology it is necessary to solve the problems of data transmission speed and preliminary training of user and computer.

Keywords

brain-computer interface, eye movements, gaze direction, electro-oculogram, blood oxygenation

Acknowledgment

Work carried out under government contract no. 0138-2022-0009

Received

27.05.2021

Publication date

11.05.2022

Number of characters

26215

Cite

GOST	Medyntsev A. Brain-computer interface: basic approaches. Part II. Interfaces based on eye movement and blood oxygenation levels // Psikhologicheskii zhurnal – 2022. – V. 43. – Issue 2 C. 116-127 [Electronic resource]. URL: https://psy.jes.su/S020595920019418-5-1 (circulation date: 27.04.2024). DOI: 10.31857/S020595920019418-5
MLA	Medyntsev, A. A "Brain-computer interface: basic approaches. Part II. Interfaces based on eye movement and blood oxygenation levels." Psikhologicheskii zhurnal 43.2 (2022):116-127. DOI: 10.31857/S020595920019418-5
APA	Medyntsev A. (2022). Brain-computer interface: basic approaches. Part II. Interfaces based on eye movement and blood oxygenation levels. Psikhologicheskii zhurnal 43(2), pp.116-127 DOI: 10.31857/S020595920019418-5

100 rub.

When subscribing to an article or issue, the user can download PDF, evaluate the publication or contact the author. Need to register.

Number of purchasers: 0, views: 350

Readers community rating: votes 0

1. Alshear O. Brain wave sensors for every body // 2018. DOI: 10.13140/RG.2.2.22223.69280. https://www.researchgate.net/publication/311582768_Brain_Wave_Sensors_for_Every_Body.

2. Allison B., Dunne S., Leeb R., Millan J., Nijholt A. Towards practical brain-computer interfaces: bridging the gap from research to real-world applications. Berlin: Springer Berlin Heidelberg, 2012. DOI: 10.1007/978-3-642-29746-5.

3. Banerjee A., Monalisa P., Shreyasi D., Tibarewala D., Konar A. Voluntary eye movement controlled electrooculogram based multitasking graphical user interface // International journal of biomedical engineering and technology. 2015. V. 3. № 18. DOI: 10.1504/IJBET.2015.070574.

4. Bauer G., Gerstenbrand F., Rumpl E. Varieties of the locked-in syndrome // Journal of neurology. 1979. V. 9. № 221. P. 77–91. DOI: 10.1007/BF00313105.

5. Bauernfeind G., Leeb R., Wriessnegger S., Pfurtscheller G. Development, set-up and first results for a one-channel near-infrared spectroscopy system // Biomedizinische technik. 2008. V. 1. № 53. P. 36–43. DOI: 10.1515/bmt.2008.005.

6. Bates R., Istance H. Why are eye mice unpopular? — a detailed comparison of head and eye controlled assistive technology pointing devices // Universal access in the information society. 2003. V. 3. № 2. P. 280–290. DOI: 10.1007/s10209-003-0053-y.

7. Beesley T., Pearson D., Le Pelley M. Eye tracking as a tool for examining cognitive processes // Biophysical measurement in experimental social science research. Academic Press, 2019. P. 1–30.

8. Bleichner M., Jansma J., Salari E., Freudenburg Z., Raemaekers M., Ramsey N. Classification of mouth movements using 7 t fMRI // Journal of neural engineering. 2015. V. 6. № 12. DOI: 10.1088/1741-2560/12/6/066026.

9. Bleichner M., Jansma J., Sellmeijer J., Raemaekers M., Ramsey N. Give me a sign: decoding complex coordinated hand movements using high-field fMRI // Brain topography. 2013. № 27. P. 248–257.

10. Coyle S., Ward T., Markham C., Mcdarby G. Give on the suitability of near-infrared (nir) systems for next-generation brain–computer interfaces // Physiological measurement. 2004. V. 4. № 25. P. 815–822. DOI: 10.1088/0967-3334/25/4/003.

11. Dhakal V., Feit A., Kristensson P., Oulasvirta A. Observations on typing from 136 million keystrokes // Proceedings of the 36th ACM conference on human factors in computing systems. ACM Press, 2018. P. 1–12.

12. Esteves A., Velloso E., Bulling A., Gellersen H. Orbits: gaze interaction for smart watches using smooth pursuit eye movements // Proceedings of the 28th annual ACM symposium on user interface software & technology. 2015. DOI: 10.1145/2807442.2807499.

13. Farwell L., Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials // Electroencephalogr clin neurophysiol. 1988. V. 6. № 70. P. 510–523. DOI: 10.1016/0013-4694(88)90149-6.

14. Fedorova А.А., Shishkin S.L., Nuzhdin Y.O., Velichkovsky B.M. Gaze based robot control: the communicative approach // The international IEEE/EMBS conference on neural engineering (ner). 2015. DOI: 10.1109/ner.2015.7146732.

15. Gallegos-Ayala G., Furdes A., Takano K., Ruf C. How many people could use an SSVEP BCI? // Neurology. 2014. V. 21. № 82. P. 1930–1932. DOI: 10.1212/WNL.0000000000000449.

16. Goossens C., Crain S. Overview of nonelectronic eye-gaze communication techniques // Augmentative and alternative communication. 1987. V. 2. № 3. P. 77–89. DOI: 10.1080/07434618712331274309

17. Guger C., Allison B., Growindhager B., Pruckl R., Hintermuller C., Kapeller C., Bruckner M., Krausz G., Edlinger G. How many people could use an SSVEP BCI? // Front neurosci. 2012. V. 169. № 6. P. 1–6. DOI: 10.1080/07434618712331274309.

18. Hutchinson T.E., White K.P., Martin W.N., Reichert K.C., Frey L.A. Human-computer interaction using eye-gaze input // IEEE Transactions on systems, man, and cybernetics. 1989. V. 19. №. 6. P. 1527–1534. DOI: 10.1109/21.44068.

19. Hwang H., Lim J., Jung Y., Choi H., Lee S., Im C. Development of an SSVEP-based BCI spelling system adopting a qwerty-style led keyboard // J neurosci methods. 2012. V. 1. № 208. P. 59–65. DOI: 10.1016/j.jneumeth.2012.04.011.

20. Jacob R. Eye movement-based human-computer interaction techniques: toward non-command interfaces // Advances in human-computer interaction. 1993. № 4. P. 151–190. DOI: 10.1126/science.929199.

21. Jobsis F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters // Science. 1977. V. 4323. № 198. P. 1264–1270. DOI: 10.1126/science.929199.

22. Khalaf A., Sejdic E., Akcakaya M. Brain-computer a novel motor imagery hybrid brain computer interface using eeg and functional transcranial doppler ultrasound // Journal of neuroscience methods. 2019. № 313. P. 44–53. DOI: 10.1016/j.jneumeth.2018.11.017.

23. Kubler A., Neumann N., Kaiser J., Kotchoubey B. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication // Archives of physical medicine and rehabilitation. 2001. V. 11. № 82. P. 1533–1539. DOI: 10.1053/apmr.2001.26621.

24. Kurauchi A., Feng W., Joshi A., Morimoto C., Betke M. Eyeswipe: dwell-free text entry using gaze paths // Proceedings of the 2016 chi conference on human factors in computing systems. 2016. DOI: 10.1145/2858036.2858335.

25. Lee K., Chang W., Kim S., Im C. Real-time “eye-writing” recognition using electrooculogram // Ieee transactions on neural systems and rehabilitation engineering. 2017. V. 1. № 25. P. 37–48. DOI: 10.1109/tnsre.2016.2542524.

26. Lledo L., Ubeda A., Ianez E., Azorin J. Internet browsing application based on electrooculography for disabled people // Expert systems with applications. 2013. V. 7. № 40. P. 2640–2648. DOI: 10.1016/j.eswa.2012.11.012

27. Majaranta P., Kari-jouko R. Twenty years of eye typing // Proceedings of the symposium on eye tracking research and applications / New york: ACM, 2002. P. 15–22.

28. Matthews P., Jezzard P. Functional magnetic resonance imaging // Journal of neurology, neurosurgery, and psychiatry. 2004. № 75. P. 6–12. DOI: 10.1142/9781860948961_0015.

29. Mishchenko Y., Kaya M., Ozbay E., Yanar H. Developing a three- to six-state EEG-based brain-computer interface for a virtual robotic manipulator control // IEEE trans biomed eng. 2019. V. 4. № 66. P. 977–987. DOI: 10.1109/TBME.2018.2865941.

30. Naci L., Owen A. Making every word count for nonresponsive patients // Jama neurology. 2013. V. 10. № 70. P. 1235–1241. DOI: 10.1001/jamaneurol.2013.3686.

31. Naseer N., Hong M., Hong K. Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface // Experimental brain research. 2013. V. 2. № 232. P. 555–564. DOI: 10.1007/s00221-013-3764-1.

32. Nijboer F. Technology transfer of brain-computer interfaces as assistive technology: barriers and opportunities // Annals of physical and rehabilitation medicine. 2015. V. 1. № 58. P. 35–38. DOI: https://doi.org/10.1016/j.rehab.2014.11.001.

33. Protzak J., Ihme H., Zander T. A passive brain-computer interface for supporting gaze-based human-machine interaction // Universal access in human-computer interaction. design methods, tools, and interaction techniques for elnclusion. UAHCI 2013. Lecture notes in computer science. Berlin: Springer, 2013. P. 662–671.

34. Qiuping D., Kaiyu T., Guang L. Development of an EOG (electro-oculography) based human-computer interface // 27th annual international conference of the engineering in medicine and biology society, IEEE-EMBS. 2005. P. 6829–6831. DOI: doi.org/10.1007/978-3-642-39188-0_71.

35. Sengupta K., Menges R., Kumar C., Staab S. Gaze the key: interactive keys to integrate word predictions for gaze-based text entry // The 22nd annual meeting of the intelligent user interfaces (iui 2017). 2017. DOI: 10.1145/3030024.3038259.

36. Shishkin S.L., Nuzhdin Y.O., Svirin E.P., Trofimov A.G., Fedorova A.A., Kozyrskiy B.L., Velichkovsky B.M. EEG negativity in fixations used for gaze-based control: Toward converting intentions into actions with an eye-brain-computer interface // Frontiers in neuroscience, 2016. 10, 528.

37. Sorger B., Dahmen B., Reithler J., Gosseries O. Gazethekey: interactive keys to integrate word predictions for gaze-based text entry // Progress in brain research. 2009. № 177. P. 275–292. DOI: 10.1016/S0079-6123(09)17719-1.

38. Speier W., Chandravadia N., Roberts D., Pendekanti S., Pouratian N. Online BCI typing using language model classifiers by ALS patients in their homes // Brain-computer interfaces. 2016. V. 2. № 4. P. 114–121. DOI: 10.1080/2326263x.2016.1252143.

39. Sun X., Huang S., Wang N. Neural interface: frontiers and applications: cochlear implants // Adv exp med biol. 2019. № 1101. P. 167–206. DOI: 10.1007/978-981-13-2050-7_7.

40. Tuisku O., Majaranta P., Isokoski P., Raiha K. Now dasher! Dash away!: longitudinal study of fast text entry by eye gaze // Etra '08: Proceedings of the 2008 symposium on eye tracking research & applications. 2008. P. 19-26. DOI: https://doi.org/10.1145/1344471.1344476.

41. Ubeda A., Ianez E., Azorin J. Wireless and portable eog-based interface for assisting disabled people // Ieee/asme transactions on mechatronics. 2011. V. 5. № 26. P. 870–873. DOI: 10.1109/tmech.2011.2160354

42. Wolpaw J., Birbaumer N., Mcfarland D., Pfurtscheller G., Vaughan T. Brain–computer interfaces for communication and control // Clinical neurophysiology. 2002. V. 6. № 113. P. 767–791. doi: 10.1016/s1388-2457(02)00057-3.