A bioelectric neural interface towards intuitive prosthetic control for amputees
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Abstract
Objective . While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful. Approach . Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject’s motor intention. Main results . A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient’s true intention. Significance . Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. Clinical trial: DExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.
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The integration of deep learning-based artificial intelligence (AI) with existing interface structures has led to the development of intuitive prosthetic control, which aims to address complex movement patterns supported by an AI decoder.
“The integration of deep learning-based artificial intelligence (AI) with existing interface structures has led to the development of intuitive prosthetic control, which aims to address complex movement patterns supported by an AI decoder [220].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [220]
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Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review
Habibollahi, M., Jiang, D., Lancashire, H. T., & Demosthenous, A. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3430038 · Cited by 11 · see citing works
“The integration of deep learning-based artificial intelligence (AI) with existing interface structures has led to the development of intuitive prosthetic control, which aims to address complex movement patterns supported by an AI decoder [220].”
- Authors
- Nguyen, A. T., Xu, J., Jiang, M., Luu, D. K., Wu, T., Tam, W.-K., Zhao, W., Drealan, M. W., Overstreet, C. K., Zhao, Q., Cheng, J., Keefer, E. W., & Yang, Z.
- Affiliations
- University of Minnesota (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Nguyen, Anh Tuan
- Xu, Jian
- Jiang, Ming
- Luu, Diu Khue
- Wu, Tong
- Tam, Wing-kin
- Zhao, Wenfeng
- Drealan, Markus W
- Overstreet, Cynthia K
- Zhao, Qi
- Cheng, Jonathan
- Keefer, Edward W
- Yang, Zhi
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2020-11-11
- Volume
- 17
- Issue
- 6
- Pages
- 066001
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3093897916
- Cited by
- 52
- References
- 68
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 11 · 2023: 5 · 2022: 12 · 2021: 10
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (NSF CAREER Award No. 1845709)
- Singapore Ministry of Education (R-263-000-A47-112)
- Defense Advanced Research Projects Agency (HR0011-17-2-0060)
- National Institutes of Health (R01-MH111413-01)
- Fasikl Incorporated
- Singapore Young Investigator Award (R-263-000-A29-133)
- University of Minnesota, Institute for Engineering in Medicine (MnDRIVE Program)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- prosthetic hand control, amputees, electroneurography, peripheral nerve recording, motor intention decoding, transradial amputation, neural prostheses
- MeSH terms
- Amputees, Artificial Intelligence, Artificial Limbs, Electrodes, Implanted, Electromyography, Hand, Humans, Prosthesis Design
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