• 2020 (Vol.34)

Transient processes of visual evoked potentials in the tasks of human-computer interfaces

© 2019 Yа. A. Turovsky, A. S. Surovtsev, S. A. Zaitsev, A. S. Konovskoya

Voronezh State University 394051 Voronezh, Unuversitetskaya pl. 1, Russia
V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 117997 Moskow, Profsouznaya 65, Russia

Received 24 Jun 2018

The paper analyzes the transients between states of steady-state visual evoked potentials (SSVEP) under conditions of varying the frequency of photostimulation (FS), which simulates the operation of a brain-computer synchronous interface. The dependences of SSVEP parameters on FS frequencies are investigated. The cluster analysis method (K-means) revealed three data clusters differing in the features of the response to the FS and the distribution of the EEG channels that demonstrate it. It has been established that in some cases the percentage of people “generating” a signal of the required frequency after changing the mode of photostimulation was significantly lower than 100%, up to the cases of complete absence of “signal detection” by any of the subjects. The latent time of occurrence of pronounced frequency components for the studied signals ranged from 150 to 420 ms, which makes it possible to consider this time as a prospect for indicators of the brain-computer interfaces speed. When using the chains of local maxima of the square matrix of the wavelet transform instead of wavelet filtering, the high-frequency component was identified better than the low-frequency one. When using wavelet filtering, an inverse relationship was revealed.

Key words: SSVEP, brain-computer synchronous interfaces, wavelet transformation

DOI: 10.1134/S0235009219020100

Cite: Yа. A. Turovsky, Surovtsev A. S., Zaitsev S. A., Konovskoya A. S. Perekhodnye protsessy zritelnykh vyzvannykh potentsialov v zadachakh interfeisov chelovek-kompyuter [Transient processes of visual evoked potentials in the tasks of human-computer interfaces]. Sensornye sistemy [Sensory systems]. 2019. V. 33(2). P. 157-165 (in Russian). doi: 10.1134/S0235009219020100

References:

  • Astafyeva N.M. Vejvlet-analiz: osnovy teorii i primery primenenija [Wavelet analysis: the foundations of the theory and examples of applications]. Uspekhi Fizicheskikh Nauk. 1996. V. 166 (11). P. 1145–1170 (in Russian).
  • Bozhokin S.V. Vejvlet-analiz dinamiki usvoenija i zabyvanija ritmov fotostimuljacii dlja nestacionarnoj jelektrojencefalogrammy [Wavelet analysis of the dynamics of assimilation and forgetting rhythms of photostimulation for a non-stationary electroencephalogram]. Journal of Technical Physics. 2010. V. 80 (9). P. 16–24 (in Russian).
  • Glantz S. Mediko-biologicheskaja statistika [Primer of biostatistics]. Moscow, Praktika. 1998. 459 p. (in Russian).
  • Kaplan A.Ya., Zhigalov A.Yu. Dinamika al’fa-aktivnosti jelektrojencefalografii u cheloveka pri triggernoj fotostimuljacii v konture interfejsa mozg-komp’juter [Dynamics of alpha activity of electroencephalography in humans in trigger photostimulation in the brain-computer interface circuit]. Bulletin of Siberian Medicine. 2010. V. 9 (2). P. 7–11 (in Russian).
  • Kist’ Michelangelo [Michelangelo human brush]. URL: https://www.ottobock.ru/prosthetics/upper-limbprosthetics/solution-overview/michelangelo-hand/(accessed: 20.06.2018) (in Russian).
  • Runyon R. Spravochnik po neparametricheskoj statistike [Nonparametric Statistic]. A Contemporary Approach. Moscow, Finansy i statistica. 1982. 198 p. (in Russian).
  • Turovsky Ya.A., Kurgalin S.D., Alekseev A.V. Analiz dvizhenija glaz cheloveka pri upravlenii samohodnym shassi s ispol’zovaniem sistemy videookulograficheskogo interfejsa [Analysis of the movement of the human eye during the control of the self-propelled chassis using the video-oculogram interface system]. Sensory systems. 2017. Vol. 31(1). P. 51–58 (in Russian).
  • Turovsky Ya.A., Kurgalin S.D., Semenov A.G. Dinamika cepochek lokal’nyh maksimumov spektrov jelektrojencefalogramm cheloveka [Dynamics of chains of local maxima of the spectra of human electroencephalograms]. Biophysics. 2014. T. 59 (1). P. 185–190 (in Russian). DOI: 10.1134/S0006350914010242.
  • Blankertz B., Curio G., Müller K.R. Classifying Single Trial EEG: Towards Brain Computer Interfacing. Advances in Neural Inf. Proc. Systems (NIPS 01). 2002. V. 14. P. 157–164.
  • Cecotti H., Volosyak I., Graser A. Reliable visual stimuli on LCD screens for SSVEP based BCI. 18th European Signal Processing Conference. 2010. P. 919–923.
  • Garcia G. High frequency SSVEPs for BCI applications. Computer-Human Interaction. Florence. 2008. V. 4.
  • Hochberg L.R., Bacher D., Jarosiewicz B., Masse N.Y., Simeral J.D., Vogel J., Haddadin S., Liu J., Cash S.S., Smart P., Donoghue J.P. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature. 2012. V. 485. № 7398. P. 372–375.
  • Lin Z., Zhang C., Wu W., Gao X. Frequency recognition based on canonical correlation analysis for SSVEPbased BCIs. IEEE transactions on biomedical engineering. 2007. V. 53(12). P. 2610–2614.
  • Zhu D., Bieger J., Molina G.G., Aarts R.M. A Survey of Stimulation Methods Used in SSVEP-Based BCIs. Computational Intelligence and Neuroscience. 2010. P. 1–12. DOI: 10.1155/2010/702357