刺激
袖口
功能性电刺激
鞋跟
神经生理学
感觉系统
闭环
神经假体
脚踝
经皮神经电刺激
神经科学
计算机科学
生物医学工程
医学
物理医学与康复
心理学
解剖
外科
控制工程
工程类
替代医学
病理
作者
Yi-Chin E. Hwang,Linda Long,José Sales Filho,Roman Genov,José Zariffa
标识
DOI:10.1101/2023.06.22.546126
摘要
Abstract Background 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. Objective To incorporate this approach into closed-loop stimulation. Methods 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 initiated by the detection of a heel prick, and induced dorsiflexion. The detection of dorsiflexion triggered stimulation to induce plantarflexion, and vice versa. A single trial began with a heel prick and ended when an incorrect state transition occurred or when a second heel prick was detected. Results Closed-loop stimulation was successfully demonstrated in 6 subjects. Number of successful trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. Conclusion 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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