BRAIN-COMPUTER INTERFACES IN ROBOTICS

Authors

  • Afonin AN Doctor, Senior researcher of faculties "Materials science and nanotechnology" Belgorod National Research University
  • Asadullaev RG Materials science and nanotechnology, Belgorod State University, 85 Pobedy str., Belgorod, 308015, Russia
  • Sitnikova MA Materials science and nanotechnology, Belgorod State University, 85 Pobedy str., Belgorod, 308015, Russia
  • Gladyshev AR Materials science and nanotechnology, Belgorod State University, 85 Pobedy str., Belgorod, 308015, Russia
  • Davletchurin K Materials science and nanotechnology, Belgorod State University, 85 Pobedy str., Belgorod, 308015, Russia

Keywords:

Brain Computer Interface (BCI), robot, neurotechnology, control system, cyborg

Abstract

The review describes the main principles as well as advantages and disadvantages of the modern brain-computer interfaces applied in robotic devices. The invasive and non-invasive devices based on the origin of a signal, invasiveness and location of probes are discussed in the paper. The description of some electrical (EMG, EEG, etc.) and chemical (fMRI, fNIRS, etc.) methods to detect neural activation are concerned. Spatial resolution of electrical neural interfaces is rather low, therefore one of their main disadvantage is the difficulty in detecting the exact region of activation. The main disadvantage of chemical neural interfaces is long reaction time. Unfortunately, none of the non-invasive methods today allows inventing an effective neural interface for interactive control of robotic devices. Modern invasive methods are rather harmful; therefore, they are unacceptable in studies with humans for ethical reasons. In this respect, the most promising is the use of the combined brain-computer noninvasive interfaces, combining sensors of both electrical and chemical activity of the nervous system. In combined neural interfaces the disadvantages of one method are compensated by the advantages of another one. The main area of practical use of neural interfaces in robotics in the foreseeable future will be devices for the rehabilitation of persons with disabilities. The use of neurointerfaces for other robotic devices will have only scientific significance until the advent of new safe invasive neurointerfaces, since non-invasive neurointerfaces do not have significant advantages over traditional control systems for healthy people.

References

Guger C., Allison B., Leuthardt E.C. (Eds.) Brain-Computer Interface Research: A State-of-the-Art Summary - 2.Springer Heidelberg New York Dordrecht London, 2014, VIII, 111 p. 38 illus., 12 illus. in color. ISBN 978-3-642-54706-5, ISBN 978-3-642-54707-2 (eBook), DOI 10.1007/978-3-642-54707-2 (Biosystems & Biorobotics, Vol. 6)

Hassanien, A.E., Azar, A.T., (2015). Brain-Computer Interfaces. Current Trends and Applications, Ist edn, Berlin, SpringerVerlag.422 p.

Wolpaw J, Wolpaw EW. Brain-Computer Interfaces: Principles and Practice. Oxford University Press; Oxford: 2012

Krol, L. R., Andreessen, L. M., & Zander, T. O. (2018). Passive Brain-Computer Interfaces: APerspective on Increased Interactivity. In C. S. Nam, A. Nijholt, & F. Lotte (Eds.), BrainComputerInterfaces Handbook: Technological and Theoretical Advances (pp. 69-86). Boca Raton, FL, USA: CRC Press.

Muzumdar, A, Powered Upper Limb Prostheses: Control, Implementation and Clinical Application; 2004; Springer-Verlag, Berlin. 220p

Gurfinkel, V.S., Malkin, V.B., Zetlin, M.L., Schneider, A.Yu., 1972. Bioelectric control. M.: Science: 245 p. (in Russian). (Гурфинкель B.C., Малкин В.Б., Цетлин М.А., Шнейдер А.Ю. Биоэлектрическое управление. М.: Наука, 1972. - 242 с. Accessed from https://www.dissercat.com/content/aktivnostdvigatelnykh-edinits-i-formirovanie-summarnykhelektromiogramm-kholodovogo-tremora)

Tomovich, R., 1969. The hand of man as a feedback system. Moscow: Publishing House of the Academy of Sciences, 1969: 13 p. (in Russian). (Томович Р., 1969. Рука человека как система обратной связи. М .: Издательство Академии наук, 1969: 13 с.)

Navarro, X. , Krueger, T. B., Lago, N. , Micera, S. , Stieglitz, T. and Dario, P. (2005), A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems. Journal of the Peripheral Nervous System, Vol 10, pp 229-

doi:10.1111/j.1085-9489.2005.10303.x

Andrey N. Afonin, Andrey Yu. Aleynikov, Marina Yu. Nazarova, Andrey R. Gladishev, Anastasiya V. Gladisheva (2018). Bionic hand

prosthesis with an improved muscle activity analyzer. Biointerface Research in Applied Chemistry. Vol 8, pp 3514-3517.)

KATZ E. 2014. Implantable bioelectronics: devices, materials and applications. New Jersey: Wiley VCH, 472 p.

Cort H. Thompson, Marissa J. Zoratti, Nicholas B. Langhals, and Erin K. Purcell. (2016). Regenerative Electrode Interfaces for Neural

Prostheses. Tissue Engineering Part B: Reviews. Vol 22, issue 2, pp 125-135)

Sato, H., & Maharbiz, M. M. (2010). Recent developments in the remote radio control of insect flight. Frontiers in neuroscience, Vol

, p199. doi:10.3389/fnins.2010.00199

Haas L. F. (2003). Hans Berger (1873-1941), Richard Caton (1842-1926), and electroencephalography. Journal of neurology, neurosurgery, and psychiatry, Vol 74, Issue 1, 9. doi:10.1136/jnnp.74.1.9.

NV Syrov., DD Zhigulskaya., DA Kirjanov., SV Borisov., A. Ya Kaplan. (2016). Whether the motor cortex excitability changes during control of phantom hand within P300-based BCI contour. Opera Med Physiol , Vol 2, Issue 2. pp 105-106.) Accessed from https://cyberleninka.ru/article/v/whether-the-motor-cortexexcitability-changes-during-control-of-phantom-hand-within-p300-based-bci-contour

AF Salazar-Gomez, J DelPreto, S Gil, FH Guenther, D Rus. (2017). Correcting robot mistakes in real time using EEG signals. IEEE International Conference on Robotics and Automation (ICRA), pp 6570-6577

José del R. Millán, Frédéric Renkens, Josep Mouriño., (2004). Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG. Transactions on Biomedical Engineering. Vol. 51, no 6, pp1026-1033. Accessed from https://infoscience.epfl.ch/record/97814/files/Millan04b.pdf

Pablo Diez. (Eds.), 2018. Smart Wheelchairs and Brain-computer Interfaces. Academic Press, London, 1st edn, 473 p. accessed from

https://www.elsevier.com/books/smart-wheelchairs-and-braincomputer-interfaces/diez/978-0-12-812892-3

LaFleur, K., Cassady, K., Doud, A. et al., (2013). Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. Journal of Neural Engineering. Vol. 10, № 046003. 15pp. Accessed from https://iopscience.iop.org/article/10.1088/1741-2560/10/4/046003/pdf

L. A. Stankevic, K. M. Sonkin N. V. Shemyakina Zh. V. Nagornova J. G. Khomenko D. S. Perets A. V. Koval.(2016), EEG pattern decoding of rhythmic individual finger imaginary movements of one hand. Volume 42, Issue 1, pp 32–42). Accessed from https://link.springer.com/article/10.1134/S0362119716010175.

Vladimir A. Maksimenko et.all. (2018). Nonlinear analysis of brain activity, associated with motor action and motor imaginary in

untrained subjects.Nonlinear Dynamics. Vol 91, No 4, pp 2803–2817. Accessed from https://link.springer.com/article/10.1007/s11071-018-4047-y

Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA.(2003). Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol, Vol. 1, No 2. pp 193 – 208. Accessed from

https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio. 0000042)

Meel Velliste, Sagi Perel, M. Chance Spalding, Andrew S. Whitford & Andrew B. Schwartz., (2008). Cortical control of a prosthetic arm

for self-feeding. Nature, 453(7198). pp 1098-1101. Accessed from https://www.nature.com/articles/nature06996.

L. R. Hochberg and J. P. Donoghue (2006). Sensors for braincomputer interfaces," in IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 5, pp. 32-38. doi: 10.1109/MEMB.2006.1705745). Accessed from https://ieeexplore.ieee.org/document/1705745

Huettel SA, Song AW, McCarthy G. Functional Magnetic Resonance Imaging. 2nd edn. Sinauer Associates, Inc; Sunderland,

Massachusetts U.S.A.; 2004. 510 p

Uludağ, Kâmil, Uğurbil, Kâmil, Berliner, Lawrence (Eds.). 2015. fMRI: From Nuclear Spins to Brain Functions. Springer USA; 1st ed. 2015 edition: 929 p

Cohen, O., Druon, S., Lengagne, S. et al., (2012). fMRI Robotic Embodiment: A Pilot Study. IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics: 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) pp 314-315.

Lee J.-H., Ryu J., Jolesz F. A., Cho Z.-H., Yoo S.-S. (2009) Brainmachine interface via real-time fMRI: Preliminary study on thoughtcontrolled robotic arm. Neuroscience Letters. Vol 450, No 1, pp 1–6. doi: 10.1016/j.neulet.2008.11.024) accessed from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3209621/

Luis J. Barrios, Maria D. del Castillo, José I. Serrano, and José L. Pons. (2012). A Review of fMRI as a Tool for Enhancing EEGBased Brain-Machine Interfaces,” Applied Bionics and Biomechanics, vol. 9, no. 2, pp. 125-133. https://doi.org/10.3233/ABB-2012-0066.

Ferrari, M., Quaresima, V. A., 2012. A brief review on the history of human functional near- infrared spectroscopy (fNIRS) development

and fields of application. NeuroImage, Vol 63, No 2, pp 921-935. Accessed from http://www.sciencedirect.com/science/article/pii/S1053811912003308)

Chen, Y and Kateb, B(Eds). (2017). Neurophotonics and brain mapping. Boca Raton:FL- CRC Press: 587 p)

Cutini, S., Moro, S. B., & Bisconti, S. (2012). Functional near Infrared Optical Imaging in Cognitive Neuroscience: An Introductory Review. Journal of Near Infrared Spectroscopy. Vol 20, No 1, pp 75–92. Accessed from https://doi.org/10.1255/jnirs.969.

Bianchi, T., Croitoru, N.I., Frenz, M. et al., 1999. NIRS monitoring of muscle contraction to control a prosthetic device. Proceedings of

SPIE - The International Society for Optical Engineering, Vol. 3570: 157-163. Accessed from https://www.spiedigitallibrary.org/conference-proceedings-ofspie/3570/1/NIRS-monitoring-of-muscle-contraction-to-control-aprosthetic-device/10.1117/12.336926.short?SSO=1)

Afonin, A.N., Asadullaev, R.G., Sitnikova, M.A., (2018). Data analysis of the fNIRS tomograph for the management of limb prostheses using the brain-computer interface. Scientific and Technical Bulletin of the Volga region, Vol 11, pp 182 - 185. (in Russian). (Анализ данных fnirs-томографа для управления протезами конечностей с помощью интерфейса мозг-компьютер.(2018) анализ данных fNIRS-томографа для управления протезами конечностей с помощью интерфейса мозг-компьютер. Научнотехнический вестник Поволжья №11, pp 182-185. Accessed from http://ntvp.ru/en/archive-vypuskov)

Alyssa M. Batula, Youngmoo E. Kim, and Hasan Ayaz, “Virtual and Actual Humanoid Robot Control with Four-Class Motor-ImageryBased Optical Brain-Computer Interface,” BioMed Research International, vol. 2017, Article ID 1463512, 13 pages, 2017. https://doi.org/10.1155/2017/1463512.)

Matsuyama, Y., Ochiai, N., Hatakeyama, T., Noguchi, K., 2010. Multimodal human-humanoid interaction using motions, brain NIRS

and spike trains. Proceedings from the 5th ACM/IEEE International Conference on Human-Robot Interaction: Accessed from https://researchmap.jp/read0169581/4.

Matveyev, M.V., Erofeev, A.I., Terekhin, S.G. et al., (2015). Implantable devices for optogenetic research and stimulation of excitable tissues. Scientific and technical statements SPbGPU. Physics and Mathematics, No. 3 (225). pp75 - 85. (in Russian). (М.В. Матвеев, А.И. Ерофеев, С.Г. Терехин, П.В. Плотникова, К.В. Воробьев, О.Л. Власова.(2015). Имплантируемые устройства для оптогенетических исследований и стимуляции возбудимых тканей. Научно-технические ведомости СПбГПУ. Физико-математические науки. No 3(225). Pp 75-85. Accessed from https://physmath.spbstu.ru/userfiles/files/volume/ph_3_2015.pdf)

Matvey Roshchin, Yulia G. Ermakova, Aleksandr A. Lanin, Artem S. Chebotarev, Ilya V. Kelmanson, Pavel M. Balaban, Aleksei M.

Zheltikov, Vsevolod V. Belousov, Evgeny S. Nikitin.(2018) Thermogenetic stimulation of single neocortical pyramidal neurons transfected with TRPV1-L channels, Neuroscience Letters, Vol 687, Pp 153-157. Accessed from http://www.sciencedirect.com/science/article/pii/S0304394018306426)

Park, S.G., Jeong, Y.C., Kim, D.G. et al., (2018). Medial preoptic circuit induces hunting-like actions to target objects and prey. Nature

Neuroscience, Vol. 21, No 3, pp 364–372. Accessed from https://www.nature.com/articles/s41593-018-0072-x

Fang, Y., Hettiarachchi, N., Zhou, D., & Liu, H. (2015). Multimodal sensing techniques for interfacing hand prostheses: a review. IEEE Sensors Journal, Vol 15, No 11, pp 6065-6076. https://doi.org/10.1109/JSEN.2015.2450211)

Khan Muhammad Jawad, Hong Keum-Shik(2017).Hybrid EEG– fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control. Frontiers in Neurorobotics, Vol 11, 6p. Accessed from https://www.frontiersin.org/article/10.3389/fnbot.2017.00006 )

Takahashi K., Maekawa S., Hashimoto M. Remarks on fuzzy reasoning-based brain activity recognition with a compact near infrared spectroscopy device and its application to robot control interface. Proceedings of the 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014; November 2014; fra. pp. 615–620. Accessed from https://ieeexplore.ieee.org/document/6996966)

Downloads

Published

2024-02-26

How to Cite

Afonin, A. N., Asadullaev, R. G., Sitnikova, M. A., Gladyshev, A. R., & Davletchurin, K. K. (2024). BRAIN-COMPUTER INTERFACES IN ROBOTICS. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(08), 3356–3361. Retrieved from https://ijact.in/index.php/j/article/view/526

Issue

Section

Review Article

Similar Articles

<< < 26 27 28 29 30 31 32 33 34 35 > >> 

You may also start an advanced similarity search for this article.