Recording and decoding for neural prostheses
Abstract
This paper reviews technologies and signal processing algorithms for decoding peripheral nerve and electrocorticogram signals to interpret human intent and control prosthetic arms. The review includes a discussion of human motor system physiology and physiological signals that can be used to decode motor intent, electrode technology for acquiring neural data, and signal processing methods including decoders based on Kalman filtering and least-squares regressors. Representative results from human experiments demonstrate the progress that has been made in neural decoding and its potential for developing neuroprosthetic arms that act and feel like natural arms.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An extensive, quantitative comparison of peripheral nerve electrode use in neuroprostheses in recent literature is absent.
“Many electrodes have been developed for peripheral nerves (Warren et al., 2016) and a comparison of their performance been conducted more than a decade ago (Navarro et al., 2005), but an extensive, quantitative comparison (on e.g., longevity and spatial resolution) of their use in neuroprostheses in recent literature is absent.”
From Update on Peripheral Nerve Electrodes for Closed-Loop Neuroprosthetics (2018) · cites this paper as Warren et al., 2016
Selectivity, longevity, and long-term stability are integral for the establishment of long-lasting and functional sensory feedback for an amputee.
“The selectivity, longevity, and long-term stability are absolutely an integral part for the establishment of long-lasting and functional sensory feedback for an amputee (Warren et al., 2016; Wurth et al., 2017).”
From Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review. (2017) · cites this paper as Warren et al., 2016
The citation lists haven’t been fetched yet
References 155 · Cited by 52
- Authors
- Warren, D. J., Kellis, S., Nieveen, J. G., Wendelken, S. M., Dantas, H., Davis, T. S., Hutchinson, D. T., Normann, R. A., Clark, G. A., & Mathews, V. J.
- Affiliations
- University of Utah (US)
- California Institute of Technology (US)
- Oregon State University (US)
- ORCID
- Venue
- Proceedings of the IEEE
- Published
- 2016-02
- Volume
- 104
- Issue
- 2
- Pages
- 374-391
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2244893152
- Cited by
- 52
- References
- 155
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 2 · 2022: 2 · 2021: 10 · 2020: 7 · 2019: 6 · 2018: 9 · 2017: 7 · 2016: 1
- FWCI
- 4.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the IEEE (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- Defense Advanced Research Project Agency (DARPA) (N6600115C4017)
- Space and Naval Warfare Systems Center Pacific (SSC Pacific)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- neural prostheses, neural decoding, electrocorticography, prosthetic arm control, Kalman filter, least squares regression, neural recording electrodes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
