Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface
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Abstract
Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.
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Using the sensory and proprioceptive input as feedback, FES interfaces have shown promising results in restoring motor functions of paralysed limbs.
“Using the sensory and proprioceptive input as feedback, FES interfaces have shown promising results in restoring motor functions of paralysed limbs [203].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [203]
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- Authors
- Hwang, Y.-C. E., Long, L., Filho, J. S., Genov, R., & Zariffa, J.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- Corresponding author
- Zariffa, José
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2024
- Volume
- 32
- Pages
- 504-513
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W4390939287
- Cited by
- 15
- References
- 39
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 4 · 2023: 1
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Centre for Analytics and Artificial Intelligence Engineering Seed program at the University of Toronto
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- closed-loop stimulation, functional electrical stimulation, cuff electrodes, convolutional neural networks, sensory feedback, sciatic nerve, rule-based controller
- MeSH terms
- Animals, Electric Stimulation, Electrodes, Electrodes, Implanted, Movement, Sciatic Nerve, Rats, Long-Evans, Rats
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