Russian Psychological Issues PsyJournals.ru
OPEN ACCESS JOURNALS
JournalsTopicsAuthorsEditor's Choice For AuthorsAbout PsyJournals.ruContact Us

  Previous issue (2019. Vol. 12, no. 1)

Included in Web of Science СС (ESCI)

CrossRef

Experimental Psychology (Russia)

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2072-7593

ISSN (online): 2311-7036

DOI: http://dx.doi.org/10.17759/exppsy

License: CC BY-NC 4.0

Started in 2008

Published quarterly

Free of fees
Open Access Journal

 

Oculomotor activity parameters of the operator in the P300 brain computer interface and similar stimulus situations 597

Basyul I.A., Research Associate, Institute of Psychology RAS, Moscow, Russia, ivbasul@gmail.com
Abstract
Hypotheses about the relationship of the processes of visual perception and variations of the task in an identical stimulus environment was tested. The following tasks were tested: 1) a simple observation of the illuminations of the character in the matrix; 2) counting the number of highlights; 3) monitoring of the target symbol highlights and typing text with the P300 BCI. In a group of 14 people showed that the highest average length of visual fixation and the lowest dispersion of fixation observed for the second type of task. Statistically significant differences in the level of dispersion of visual fixations found between 1-2 and 1-3 modes; differences between the modes for the duration of fixations are at trends. Significant differences in the number of visual fixations on the target symbols wasn’t detected. The overall conclusion is the high perspective of pairing methodology brain-computer interface on the P300 wave with eye tracking to optimize the characteristics of the stimulus in the BCI environment. The differences in the parameters of oculomotor activity between the tasks reflect the level of attention concentration in the target symbols of the P300 BCI

Keywords: brain-computer interface; event-related potentials; P300 wave; N200 wave; visual attention; human operator

Column: Research Methods

DOI: http://dx.doi.org/10.17759/exppsy.2015080410

For Reference

References
  1. Barabanschikov V.A. Okulomotomye struktury vospriyatiya [Oculomotor structures of the perception]. Moscow, Institute of psychology RAS Publ., 1997.383 p. (In Russ.).
  2. Barabanschikov V.A., Zhegallo A.V. Aitreking: metody registratsii dvizhenii glaz v psikhologicheskikh issledovaniyakh i praktike [Eyetracking: registration methods for eye movements in psychological studies and practice]. Moscow, Cogito-Centr Publ., 2014.128 p. (In Russ.).
  3. Barabanschikov V.A., Zhegallo A.V. Registratsiya i analiz napravlennosti vzora cheloveka [Registration and analysis of the human gaze], Moscow, Institute of psychology RAS Publ., 2013.323 p. (In Russ.).
  4. Basyul I.A., Kaplan A.Ya. Izmeneniya N200 i P300 komponentov potentsialov, svyazannykh s sobytiyami, pri var’irovanii uslovii vnimaniya v sisteme Brain Computer Interface [Changes in the N200 and P300 Components of Event-Related Potentials on Variations in the Conditions of Attention in a Brain- Computer Interface System]. Zh Vyssh New Deiat IP Pavlova, Moscow, 2014, no. 2 (64), pp. 159-166 (In Russ., abstract in Engl.).
  5. Ganin I.P., Kaplan A.Ya. Interfeis mozg-komp’yuter na osnove volny p300: pred”yavlenie kompleksnykh stimulov «podsvetka + dvizhenie» [The РЗОО-based brain-computer interface: presentation of the complex “flash + movement” stimuli], Zh Vyssh New Deiat ImlP Pavlova, 2014, no. 2 (64), pp. 32-40 (In Russ., abstract in Engl.).
  6. Ganin I.P., Shishkin S.L., Kochetova A.G., Kaplan A.Ya. Interfeis mozg-komp’yuter «па volne Р300»: issledovanie effekta nomera stimulov v posledovatel’nosti ikh pred”yavleniya [The РЗОО-based brain- computer interface: the effect of the stimulus position in a stimulus train], Fiziologiya cheloveka [Human Physiology], 2012, no. 38 (2), pp. 5-13 (In Russ.).
  7. Kaplan A.Ya., Kochetova A.G., Shishkin S.L., Basyul I.A., Ganin I.P., Vasil’ev A.N., Liburkina S.P. Eksperimental’no-teoreticheskie osnovaniya i prakticheskie realizatsii tekhnologii interfeis mozg- komp’yuter [Experimental and theoretical foundations and practical implementation of brain-computer interface technology]. Bulleten Sibirskoy Meditsini [Bulletin of Siberian medicine], 2013, no. 12 (2),pp. 21-29 (In Russ.).
  8. Mikhailova E.S., Chicherov V.A., Ptushenko I.A., Shevelev I.A. Prostranstvennyi gradient volny P300 zritel’nogo vyzvannogo potentsiala mozga cheloveka v modeli neirokomp’yuternogo interfeisa [Spatial Gradient of P300 Area in the Brain-Computer Interface Paradigm], Zh Vyssh New Deiat Im IP Pavlova, 2008, no. 58 (3), pp. 302-308 (In Russ.).
  9. Aloise F., Schettini F., Arico P., Salinari S., Babiloni F., Cincotti F. A comparison of classification techniques for a gaze-independent РЗОО-based brain-computer interface. J Neural Eng, 2012, no. 9, pp. 045012. doi: 10.1088/1741-2560/9/4/045012
  10. Bianchi L., Sami S., Hikkerbrand A., Fawcett I.P., Quitadamo L.R, Seri S. Which physiological components are more suitable for visual ERP based brain-computer interface? A preliminary MEG/EEG study. Brain Topogr, 2010, no. 23, pp. 180-185. doi: 10.1007/sl0548-010-0143-0
  11. Blankertz B., Tangermann М., Vidaurre C., Fazli S., Sannelli C., Haufe S., Maeder C., Ramsey L., Sturm I., Curio G., Muller K.R. The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology. Front Neurosci, 2010, no. 4, p. 198. doi: 10.3389/fnins.2010.00198
  12. Brunner P., Joshi S., Briskin S., Wolpaw J.R., Bischof H., and Schalk G. Does the “P300” Speller Depend on Eye Gaze? J Neural Eng, 2010, vol. 7, no. 5, pp. 056013. doi: 10.1088/1741-2560/7/5/056013
  13. Cipresso P., Meriggi P., Carelli L., Solca F., Meazzi D., Poletti B., Lule D., Ludolph A.C., Giuseppe R., Silani V. The combined use of Brain Computer Interface and Eye-Tracking technology for cognitive assessment in Amyotrophic Lateral Sclerosis. Pervasive Computing Technologies for Healthcare (PewasiveHealth), Dublin, Irland, 23-26 May 2011, pp. 320-324.
  14. Do A.H., Wang P.T., King C.E., Schombs A., Cramer S.C., Nenadic Z. Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke. Conf. Proc. IEEE Eng. Med. Biol. Soc, 2012, pp. 6414-6417. doi: 10.1109/EMBC.2012.6347462
  15. Dominguez-Martinez E., Parise E., Strandvall Т., Reid V.M. The Fixation Distance to the Stimulus Influences ERP Quality: An EEG and Eye Tracking N400 Study. PLoS ONE. 2015, vol. 10, no. 7, pp. e0134339. doi:10.1371/journal.pone.0134339
  16. Farwell L.A., Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event- related brain potentials. EEG a. Clin. Neurophysiol, 1988, no. 70, pp. 510-523.
  17. Frisoli A., Loconsole C., Leonardis D., Banno F., Barsotti М., Chisari C., Bergamasco M. A New Gaze- BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2012, no. 42, pp. 1169-1179.
  18. Gneo М., Severini G., Conforto S., Schmid М., D’Alessio T. Towards a brain-activated and eye-controlled wheelchair. Inter. J. of Bioelectromagnetism, 2011, vol. 13, no. 1, pp. 44-45. doi: 10.1186/1743-0003-11-7
  19. Kaplan A.Ya., Lim J.J., Jin K.S., Park B.W., Byeon J.G., Tarasova S.U. Unconscious operant conditioning in the paradigm of brain-computer interface based on color perception. Intern. J. Neurosci, 2005, no. 115, pp. 781-802.
  20. Kaplan A.Ya., Shishkin S.L., Ganin I.P., Basyul I.A., Zhigalov A.Y. Adapting the РЗОО-based brain- computer interface for gaming: a review. IEEE Trans, on Comput. Intelligence and Alin Games, 2013, vol. 5, no. 2, pp. 141-149. doi: 10.1371/journal.pone.0077755
  21. Kaufmann Т., Hammer E. М., Kubler A. ERPs Contributing to Classification in the “P300” BCI. Proceedings of the Fifth International BCI Conference, Graz, Austria, 22-24 September 2011, pp. 136-139.
  22. Kim B.H., Kim М., Jo S. Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking. Computers in Biology and Medicine, 2014, vol. 51, pp. 82-92. doi: 10.1016/j. compbiomed.2014.04.020
  23. Kleih S.C., Kaufmann Т., Zickler C., Haider S., Leotta F., Cincotti F., Aloise F., Riccio A., Herbert C., Mattia D., Kubler A. Out of the frying pan into the fire—the РЗОО-based BCI faces real-world challenges. Prog. Brain Res, 2011, vol. 194, pp. 27-46. doi: 10.1016/B978-0-444-53815-4.00019-4
  24. Krusienski D.J., Sellers E.W., McFarland D.J., Vaughan T.M., Wolpaw J.R. Toward enhanced P300 speller performance./. Neurosci. Methods, 2008, Vol. 167, pp. 15-21. doi: 10.1016/j.jneumeth.2007.07.017
  25. Lee E.C., Woo J.C., Kim J.H., Whang М., Park K.R. A brain-computer interface method combined with eye tracking for 3D interaction./. Neurosci Methods, 2010, vol. 190, no. 2, pp. 289-298. doi: 10.1016/j. jneumeth.2010.05.008
  26. Mak J.N, Arbel Y., Minett J.W., McCane L.M., Yuksel B., Ryan D., Thompson D., Bianchi L., Erdogmus D. Optimizing the РЗОО-based brain-computer interface: current status, limitations and future directions. J. Neural Eng, 2011, vol. 8, pp. 025-033. doi: 10.1088/1741-2560/8/2/025003
  27. McCullagh P., Galway L., Lightbody G. Investigation into a Mixed Hybrid Using SSVEP and Eye Gaze for Optimising User Interaction within a Virtual Environment. In C. Stephanidis and M. Antona (eds.), UAHCI/HCI, 2013, Part I, LNCS 8009, pp. 530-539. doi: 10.1007/978-3-642-39188-0_57
  28. Nicolelis M.A. Brain-machine interfaces to restore motor function and probe neural circuits. Nat. Rev. Neurosci, 2003, vol. 4, no. 5, pp. 417-422.
  29. Plochl М., Ossandon J.P., Konig P. Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data. Front. Hum. Neurosci., 2012, vol. 6, art. 278. doi: 10.3389/fnhum.2012.00278
  30. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2015. URL: http://www.R-project.org/.
  31. Sellers E.W., Vaughan T.M., Wolpaw J.R. A brain-computer interface for long-term independent home use. Amyotroph. Lateral Scler, 2010, vol. 11, pp. 449-455. doi: 10.3109/17482961003777470
  32. Shishkin S.L., Ganin I.P., Basyul I. A., Zhigalov A.Y., Kaplan A.Y. N1 wave in the P300 BCI is not sensitive to the physical characteristics of stimuli./Integr Neurosci, 2009, vol. 8, no. 4, pp. 471-485.
  33. Vidal J.J. Real-time detection of brain events in EEG. IEEE Proc, 1977, vol. 65, pp. 633-641. doi: 10.1109/PROC. 1977.10542
  34. Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M. Brain-computer interfaces for communication and control. Clin. Neurophysiol, 2002, vol. 113, pp. 767-791.
  35. Wolpaw J.R., McFarland D.J., Neat G.W., Fomeris C.A. An eeg-based brain-computer interface for cursor control. EEG a. Clin. Neurophysiol, 1991, vol. 78, no. 3, pp. 252-259.
  36. Zander T.O, Gaertner М., Kothe C., Vilimek R. Combining Eye Gaze Input with a Brain-Computer Interface for touchless Human-Computer Interaction. International journal of human-computer interaction, 2011, vol. 27, no. 1, pp. 38-51. doi: 10.1080/10447318.2011.535752
comments powered by Disqus
 
About PsyJournals.ru

© 2007–2019 Portal of Russian Psychological Publications. All rights reserved

PsyJournals.ru in Russian

Publisher: Moscow State University of Psychology and Education

Catalogue of academic journals in psychology & education MSUPE

Creative Commons License

RSS Psyjournals at facebook Psyjournals at Twitter Psyjournals at Youtube Яндекс.Метрика