Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 504 - 513
• Main Authors: Hwang, Yi-Chin E., Long, Liam, Filho, José Sales, Genov, Roman, Zariffa, José
• Format: Journal Article
• Language: English
• Published: United States The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024; IEEE
• Subjects: Animals; Ankle; Artificial neural networks; Closed loops; Closed-loop; convolutional neural network; Deep learning; Electric contacts; Electric Stimulation; Electrical stimuli; Electrodes; Electrodes, Implanted; Feedback control; functional electrical stimulation; Machine learning; Movement - physiology; nerve cuff; Nerves; Neural networks; neuroprosthetic; Rats; Rats, Long-Evans; Recording; Sciatic nerve; Sciatic Nerve - physiology; Sensory feedback; Sensory neurons; Stimulation
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3355063

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.