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Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity

Nature Biomedical Engineering volume 4, pages 672–685 (2020)Cite this article

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Abstract

The instability of neural recordings can render clinical brain–computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.

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Fig. 1: Stabilized BCI framework.
Fig. 2: Manifold-based stabilization intuition and design.
Fig. 3: Examples of neural recording instabilities.
Fig. 4: Representative experimental session.
Fig. 5: Summary of the single-day experimental sessions.
Fig. 6: Manifold-based stabilization restores performance in the presence of instabilities.
Fig. 7: Manifold-based stabilization maintains performance across multiple days.
Fig. 8: Manifold-based stabilization can outperform supervised recalibration.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. Experimental data for the stabilization of brain–computer interfaces are available at https://github.com/alandegenhart/stabilizedbci.

Code availability

The MATLAB code for the stabilization of brain–computer interfaces is available at https://github.com/alandegenhart/stabilizedbci.

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Acknowledgements

This work was supported by the Craig H. Neilsen Foundation (280028 to B.M.Y., S.M.C. and A.P.B.), NIH T32 NS086749 (to A.D.D. and E.R.O.), the DSF Charitable Foundation (132RA03 to A.D.D.), NIH R01 HD071686 (to A.P.B., B.M.Y. and S.M.C.), NSF NCS BCS1533672 (to S.M.C., A.P.B. and B.M.Y.), NIH CRCNS R01 NS105318 (to B.M.Y. and A.P.B.), the PA Department of Health (Research Formula Grant SAP 4100077048 to S.M.C. and B.M.Y.), NSF CAREER Award IOS1553252 (to S.M.C.), NSF NCS BCS1734916 (to B.M.Y.) and the Simons Foundation (364994 and 543065 to B.M.Y.).

Author information

Author notes

  1. These authors contributed equally: Alan D. Degenhart, William E. Bishop.

  2. These authors jointly supervised this work: Steven M. Chase, Aaron P. Batista, Byron M. Yu.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Alan D. Degenhart & Byron M. Yu

  2. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Alan D. Degenhart, Emily R. Oby, Elizabeth C. Tyler-Kabara & Aaron P. Batista

  3. Center for the Neural Basis of Cognition, Pittsburgh, PA, USA

    Alan D. Degenhart, William E. Bishop, Emily R. Oby, Steven M. Chase, Aaron P. Batista & Byron M. Yu

  4. Brain Institute, University of Pittsburgh, Pittsburgh, PA, USA

    Alan D. Degenhart, Emily R. Oby & Aaron P. Batista

  5. Systems Neuroscience Center, University of Pittsburgh, Pittsburgh, PA, USA

    Alan D. Degenhart, Emily R. Oby & Aaron P. Batista

  6. Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA

    William E. Bishop

  7. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    William E. Bishop

  8. Department of Neurobiology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Emily R. Oby

  9. Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Elizabeth C. Tyler-Kabara

  10. Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA

    Elizabeth C. Tyler-Kabara

  11. McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Elizabeth C. Tyler-Kabara

  12. Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Steven M. Chase & Byron M. Yu

  13. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Steven M. Chase & Byron M. Yu

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  1. Alan D. Degenhart
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Contributions

A.D.D., W.E.B., E.R.O., S.M.C., A.P.B. and B.M.Y. designed the experiments and interpreted the results. A.D.D. performed the experiments with input from W.E.B. W.E.B. and B.M.Y. designed the stabilization method. A.D.D. and W.E.B. developed the real-time implementation of the stabilized BCI. A.D.D. and W.E.B. performed the analyses and wrote the manuscript. E.R.O., E.C.T.-K. and A.D.D. implanted the electrode arrays used for the experiments. All authors provided feedback on the manuscript.

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Correspondence to Byron M. Yu.

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Degenhart, A.D., Bishop, W.E., Oby, E.R. et al. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity. Nat Biomed Eng 4, 672–685 (2020). https://doi.org/10.1038/s41551-020-0542-9

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