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Published November 30, 2023 | in press
Journal Article Open

Decoding motor plans using a closed-loop ultrasonic brain–machine interface


Brain–machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.

Copyright and License

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.


We thank K. Pejsa for assistance with animal care, surgeries and training. We thank C. Rabut and L. Lin for helpful discussions. We thank K. Passanante for her illustrations. W.S.G. was supported by an NEI F30 (NEI F30 EY032799), the Josephine de Karman Fellowship and the UCLA-Caltech MSTP (NIGMS T32 GM008042). S.L.N. was supported by the Della Martin Foundation. G.C. was supported by an NINDS T32 (T32 NS105595). This research was supported by the National Institute of Health BRAIN Initiative (grant 1R01NS123663-01 to R.A.A., M.G.S. and M.T.), the T&C Chen Brain-Machine Interface Center and the Boswell Foundation (R.A.A.). M.G.S. is an investigator of the Howard Hughes Medical Institute.


These authors contributed equally: Whitney S. Griggs, Sumner L. Norman.

W.S.G., S.L.N., V.C., M.T., M.G.S. and R.A.A. conceived the study. S.L.N. established the fUS neuroimaging sequences and T.D., B.-F.O., F.S. and M.T. wrote the acquisition software for 2-Hz real-time fUS neuroimaging. W.S.G. and S.L.N. wrote the code for the fUS-BMI. W.S.G. trained the monkeys and acquired the data. W.S.G., S.L.N. and G.C. performed the data processing and analysis. W.S.G. and S.L.N. drafted the manuscript with substantial contributions from M.G.S. and R.A.A., and all authors edited and approved the final version of the manuscript. V.C., C.L., M.T., M.G.S. and R.A.A. supervised the research.

Data Availability

Key data used in this paper are archived at https://doi.org/10.22002/pa710-cdn95.

Code Availability

Code used to generate key figures and results is available at https://github.com/wsgriggs2/rt_fUS_BMI and archived at https://doi.org/10.5281/zenodo.8414598.

Conflict of Interest

B.-F.O. is an employee of Iconeus. T.D., B.-F.O. and M.T. are co-founders and shareholders of Iconeus, which commercializes ultrasonic neuroimaging scanners. S.L.N. is the co-founder and CEO of Forest Neurotech, a nonprofit research organization creating tools for neuroscience and clinical research. The other authors declare no competing interests.


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Additional details

December 1, 2023
January 9, 2024