Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

PATENTS

Patent landscape of brain–machine interface technology

Nature Biotechnology volume 39, pages 1194–1199 (2021)Cite this article

Subjects

A study of the brain–machine interface patent landscape suggests that the technology is in its early stages of development, but patent applications have been increasing exponentially in recent years.

Brain–machine interface (BMI) technology combines brain activity with computational processing. A BMI is an input–output computing system: the input is brain activity and the output is the manipulation of software and/or hardware. BMIs can be used to extract and interpret brain signals to allow for thought-controlled manipulation of devices such as computers, robots and prosthetic limbs1. BMI technology straddles the fields of neuroscience, signal processing, machine learning and robotics.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

Buy

All prices are NET prices.

Fig. 1
Fig. 2: Priority country or region of BMI patent applications.
Fig. 3
Fig. 4
Fig. 5: EEG activity for BMIs.

References

  1. Prashant, P., Joshi, A. & Gandhi, V. (eds) Brain computer interface: a review. In 5th Nirma University International Conference on Engineering (NUiCONE) 1–6 (IEEE, 2015).

  2. Rao, R.R.N. Brain–Computer Interfacing: An Introduction (Cambridge University Press, 2013).

  3. Chorev, E., Epsztein, J., Houweling, A. R., Lee, A. K. & Brecht, M. Curr. Opin. Neurobiol. 19, 513–519 (2009).

    Article  CAS  Google Scholar 

  4. Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y. & Zhao, X. J. Neural Eng. 16, 011001 (2019).

    Article  Google Scholar 

  5. Kaplan, R. M. Australas. Psychiatry 19, 168–169 (2011).

    Article  Google Scholar 

  6. Naci, L. et al. Ann. Neurol. 72, 312–323 (2012).

    Article  Google Scholar 

  7. Ibáñez, J. et al. Biomed. Signal Process. Control 8, 822–829 (2013).

    Article  Google Scholar 

  8. Quandt, L. C., Marshall, P. J., Shipley, T. F., Beilock, S. L. & Goldin-Meadow, S. Neuropsychologia 50, 2745–2751 (2012).

    Article  Google Scholar 

  9. Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Nat. Rev. Neurol. 12, 513–525 (2016).

    Article  Google Scholar 

  10. Krusienski, D. J. et al. J. Neural Eng. 3, 299–305 (2006).

    Article  Google Scholar 

  11. Garcia-Molina, G. et al. Detection of high frequency steady state visual evoked potentials for brain-computer interfaces. In 17th European Signal Processing Conference (eds Stewart, R. & Weiss, S.) 646-650 (EURASIP, 2009).

  12. Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R. & Rossion, B. J. Vis. 15, 4 (2015).

    Article  Google Scholar 

  13. Chavarriaga, R., Sobolewski, A. & Millán, JdelR. Front. Neurosci. 8, 208–220 (2014).

    Article  Google Scholar 

  14. Chamola, V. et al. Sensors (Basel) 20, 3620 (2020).

    Article  Google Scholar 

  15. Nicolas-Alonso, L. F. & Gomez-Gil, J. Sensors (Basel) 12, 1211–1279 (2012).

    Article  Google Scholar 

  16. Ramos-Murguialday, A. et al. Ann. Neurol. 74, 100–108 (2013).

    Article  Google Scholar 

  17. Lebedev, M. Transl. Neurosci. 5, 99–110 (2014).

    Article  Google Scholar 

  18. Skansi, S. Introduction to Deep Learning (Springer International, 2018).

  19. Bhagat, N. A. et al. Front. Neurosci. 10, 122 (2016).

    Article  Google Scholar 

  20. Nowlis, D. P. & Kamiya, J. Psychophysiology 6, 476–484 (1970).

    Article  CAS  Google Scholar 

  21. Hochberg, L. R. et al. Nature 442, 164–171 (2006).

    Article  CAS  Google Scholar 

  22. Rabbani, Q., Milsap, G. & Crone, N. E. Neurotherapeutics 16, 144–165 (2019).

    Article  Google Scholar 

  23. Kaplan, A. Y. et al. IEEE Trans. Comput. Intell. AI Games 5, 141–149 (2013).

    Article  Google Scholar 

  24. Monastra, V. J. et al. Appl. Psychophysiol. Biofeedback 30, 95–114 (2005).

    Article  Google Scholar 

  25. Andersen, R. A. et al. Proc. Natl Acad. Sci. USA 116, 26274–26279 (2019).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wilmer Cutler Pickering Hale and Dorr LLP, Boston, MA, USA

    Anastasia Greenberg & Monica Grewal

  2. Wilmer Cutler Pickering Hale and Dorr LLP, Washington, DC, USA

    Alexis Cohen

Authors
  1. Anastasia Greenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexis Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monica Grewal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anastasia Greenberg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Greenberg, A., Cohen, A. & Grewal, M. Patent landscape of brain–machine interface technology. Nat Biotechnol 39, 1194–1199 (2021). https://doi.org/10.1038/s41587-021-01071-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-021-01071-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing