Annals of Clinical Neurophysiology

The Korean Society of Clinical Neurophysiology (대한임상신경생리학회)
권호 표지
DOI QR코드

DOI QR Code

Introduction of brain computer interface to neurologists

  • Kim, Do-Hyung (Department of Neurology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine) ;
  • Yeom, Hong Gi (Department of Electronics Engineering, Chosun University College of Engineering) ;
  • Kim, Minjung (Department of Neurology, Centum Hospital) ;
  • Kim, Seung Hwan (Department of Neurosurgery, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine) ;
  • Yang, Tae-Won (Department of Neurology, Gyeongsang National University Changwon Hospital, Gyeongsang National University College of Medicine) ;
  • Kwon, Oh-Young (Department of Neurology and Institute of Health Science, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine) ;
  • Kim, Young-Soo (Department of Neurology and Institute of Health Science, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine)
  • Received : 2021.08.30
  • Accepted : 2021.09.23
  • Published : 2021.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

A brain-computer interface (BCI) is a technology that acquires and analyzes electrical signals from the brain to control external devices. BCI technologies can generally be used to control a computer cursor, limb orthosis, or word processing. This technology can also be used as a neurological rehabilitation tool for people with poor motor control. We reviewed historical attempts and methods toward predicting arm movements using brain waves. In addition, representative studies of minimally invasive and noninvasive BCI were summarized.

Keywords

References

  1. Vidal JJ. Toward direct brain-computer communication. Annu Rev Biophys Bioeng 1973;2:157-180. https://doi.org/10.1146/annurev.bb.02.060173.001105
  2. Nicolelis M. Beyond boundaries: The new neuroscience of connecting brains with machines---and how it will change our lives. 1st ed. New York: Times Books , 2011;1-353.
  3. Rastogi A, Vargas-Irwin CE, Willett FR, Abreu J, Crowder DC, Murphy BA, et al. Neural representation of observed, imagined, and attempted grasping force in motor cortex of individuals with chronic tetraplegia. Sci Rep 2020;10:1429. https://doi.org/10.1038/s41598-020-58097-1
  4. Evarts EV. Relation of pyramidal tract activity to force exerted during voluntary movement. J Neurophysiol 1968;31:14-27. https://doi.org/10.1152/jn.1968.31.1.14
  5. Humphrey DR, Schmidt EM, Thompson WD. Predicting measures of motor performance from multiple cortical spike trains. Science 1970;170:758-762. https://doi.org/10.1126/science.170.3959.758
  6. Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci 1982;2:1527-1537. https://doi.org/10.1523/JNEUROSCI.02-11-01527.1982
  7. Georgopoulos AP, Schwartz AB, Kettner RE. Neuronal population coding of movement direction. Science 1986;233:1416-1419. https://doi.org/10.1126/science.3749885
  8. Georgopoulos AP, Kettner RE, Schwartz AB. Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J Neurosci 1988;8:2928-2937. https://doi.org/10.1523/JNEUROSCI.08-08-02928.1988
  9. Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 2000;408:361-365. https://doi.org/10.1038/35042582
  10. Flint RD, Lindberg EW, Jordan LR, Miller LE, Slutzky MW. Accurate decoding of reaching movements from field potentials in the absence of spikes. J Neural Eng 2012;9:046006. https://doi.org/10.1088/1741-2560/9/4/046006
  11. Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB. Cortical control of a prosthetic arm for self-feeding. Nature 2008;453:1098-1101. https://doi.org/10.1038/nature06996
  12. Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 2012;485:372-375. https://doi.org/10.1038/nature11076
  13. Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 2013;381:557-564. https://doi.org/10.1016/S0140-6736(12)61816-9
  14. Bouton CE, Shaikhouni A, Annetta NV, Bockbrader MA, Friedenberg DA, Nielson DM, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature 2016;533:247-250. https://doi.org/10.1038/nature17435
  15. Flesher SN, Downey JE, Weiss JM, Hughes CL, Herrera AJ, Tyler-Kabara EC, et al. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science 2021;372:831-836. https://doi.org/10.1126/science.abd0380
  16. Polikov VS, Tresco PA, Reichert WM. Response of brain tissue to chronically implanted neural electrodes. J Neurosci Methods 2005;148:1-18. https://doi.org/10.1016/j.jneumeth.2005.08.015
  17. Oxley TJ, Opie NL, John SE, Rind GS, Ronayne SM, Wheeler TL, et al. Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity. Nat Biotechnol 2016;34:320-327. https://doi.org/10.1038/nbt.3428
  18. Oxley TJ, Yoo PE, Rind GS, Ronayne SM, Lee CMS, Bird C, et al. Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience. J Neurointerv Surg 2021;13:102-108. https://doi.org/10.1136/neurintsurg-2020-016862
  19. Musk E. An integrated brain-machine interface platform with thousands of channels. J Med Internet Res 2019;21:e16194. https://doi.org/10.2196/16194
  20. Vidal JJ. Real-time detection of brain events in EEG. Proc IEEE 1977;65:633-641. https://doi.org/10.1109/PROC.1977.10542
  21. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002;113:767-791. https://doi.org/10.1016/S1388-2457(02)00057-3
  22. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A, et al. A spelling device for the paralysed. Nature 1999;398:297-298. https://doi.org/10.1038/18581
  23. Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999;110:1842-1857. https://doi.org/10.1016/S1388-2457(99)00141-8
  24. Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988;70:510-523. https://doi.org/10.1016/0013-4694(88)90149-6
  25. Wang Y, Gao X, Hong B, Jia C, Gao S. Brain-computer interfaces based on visual evoked potentials. IEEE Eng Med Biol Mag 2008;27:64-71.