Ann Clin Neurophysiol.  2021 Oct;23(2):92-98. 10.14253/acn.2021.23.2.92.

Introduction of brain computer interface to neurologists

• Kim DH
• Yeom HG
• Kim M
• Kim SH
• Yang TW
• Kwon OY
• Kim YS

• KMID: 2521886
• DOI: http://doi.org/10.14253/acn.2021.23.2.92

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.

Figure

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  Fig. 1. Concept image of a brain-computer interface. Figure adapted from Wolpaw et al.21

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  Fig. 2. Direction predictions based on neuron firing rates. Impulse activity was recorded from a single neuron during repeated arm movements toward the target. Neurons fire just (about 300 ms) before the onset of movement (i.e., 0 ms). Each dot represents a neuron firing, and each row on the vertical axis represents a repetitive arm movement in the same direction. Firing rates increased when the arm was moved 180 degrees in the desired direction and decreased when the arm was moved back to 0 degrees, suggesting that the recorded neuron prefers to move the arm toward 180 degrees.

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  Fig. 3. A monkey controlling a robotic arm with its brain waves. Figure adapted from Velliste et al.11

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  Fig. 4. Minimally invasive devices. (A) Stentrode with 8 × 750 μm electrode discs self-expanding during administration by a 4F catheter. Scale bar, 3 mm. The device is usually implanted in the superior sagittal sinus to obtain the electrical activity of the brain. Figure adapted from Oxley et al.17 (B) Neural threads. “Tree” probes with electrode contacts spaced by 75 μm. Scale bar, 100 μm. Figure adapted from Musk.19

Reference

1. Vidal JJ. Toward direct brain-computer communication. Annu Rev Biophys Bioeng. 1973; 2:157–180.

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. p. 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.

4. Evarts EV. Relation of pyramidal tract activity to force exerted during voluntary movement. J Neurophysiol. 1968; 31:14–27.

5. Humphrey DR, Schmidt EM, Thompson WD. Predicting measures of motor performance from multiple cortical spike trains. Science. 1970; 170:758–762.

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.

7. Georgopoulos AP, Schwartz AB, Kettner RE. Neuronal population coding of movement direction. Science. 1986; 233:1416–1419.

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.

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.

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.

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.

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.

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.

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.

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.

16. Polikov VS, Tresco PA, Reichert WM. Response of brain tissue to chronically implanted neural electrodes. J Neurosci Methods. 2005; 148:1–18.

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.

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.

19. Musk E. An integrated brain-machine interface platform with thousands of channels. J Med Internet Res. 2019; 21:e16194.

20. Vidal JJ. Real-time detection of brain events in EEG. Proc IEEE. 1977; 65:633–641.

21. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002; 113:767–791.

22. Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, et al. A spelling device for the paralysed. Nature. 1999; 398:297–298.

23. Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999; 110:1842–1857.

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.

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.