Cited by 113Neuroscience and Neural EngineeringEEG and Brain-Computer InterfacesNeural dynamics and brain function

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A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

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Nason, Samuel R., Vaskov, Alex K., Willsey, Matthew S., Welle, Elissa J., An, Hyochan, Vu, Philip P., Bullard, Autumn J., Nu, Chrono S., Kao, Jonathan C., Shenoy, Krishna V., Jang, Taekwang, Kim, Hun-Seok, Blaauw, David, Patil, Parag G., Chestek, Cynthia A.·2020·Nature Biomedical Engineering

DOI 10.1038/s41551-020-0591-0PMC PMC7982996

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Abstract

The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.

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Authors: Nason, S. R., Vaskov, A. K., Willsey, M. S., Welle, E. J., An, H., Vu, P. P., Bullard, A. J., Nu, C. S., Kao, J. C., Shenoy, K. V., Jang, T., Kim, H.-S., Blaauw, D., Patil, P. G., & Chestek, C. A.
Affiliations: University of Michigan (US)
University of California, Los Angeles (US)
Howard Hughes Medical Institute (US)
Corresponding author: Chestek, Cynthia A.
ORCID: Nason, Samuel R.
Vaskov, Alex K.
Willsey, Matthew S.
Welle, Elissa J.
An, Hyochan
Vu, Philip P.
Bullard, Autumn J.
Kao, Jonathan C.
Shenoy, Krishna V.
Jang, Taekwang
Kim, Hun-Seok
Blaauw, David
Patil, Parag G.
Chestek, Cynthia A.
Venue: Nature Biomedical Engineering
Publisher: Springer Science and Business Media LLC
Published: 2020-07-27
Volume: 4
Issue: 10
Pages: 973-983
ISSN: 2157-846X (ISSN-L)
DOI: 10.1038/s41551-020-0591-0
PMC: PMC7982996
OpenAlex: W3044236731
Cited by: 113
References: 88
Citations by year: 2026: 7 · 2025: 21 · 2024: 22 · 2023: 19 · 2022: 22 · 2021: 16 · 2020: 5
FWCI: 7.10
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: U.S. Department of Health & Human Services | National Institutes of Health (T32NS007222, U01NS094375, UF1NS107659, R21EY029452, R01NS076460, R01MH09964703, 8DP1HD075623, R01GM111293)
U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OT2OD024907)
U-M | MCubed, University of Michigan (1482)
National Science Foundation (Graduate Research Fellowships Program, 1926576)
United States Department of Defense | Defense Advanced Research Projects Agency (N66001-10-C-2010, W911NF-14-2-0013)
Simons Foundation (543045)
United States Department of Defense | United States Navy | Office of Naval Research (W911NF-14-2-0013)
Howard Hughes Medical Institute
A. Alfred Taubman Medical Research Institute
Craig H. Neilsen Foundation (315108)
Topics: Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords: brain-computer interface, spiking activity, single-unit activity, neural decoding, signal-to-noise ratio, non-human primates, cursor control, motion prediction, power consumption
MeSH terms: Animals, Fingers, Macaca mulatta, Male, Microelectrodes, Motor Cortex, Neurons, Prostheses and Implants, Rats, Long-Evans, Signal-To-Noise Ratio, Brain-Computer Interfaces

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A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces

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