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.
Subject(s)
Movement , Sciatic Nerve , Animals , Rats , Electric Stimulation , Electrodes , Electrodes, Implanted , Movement/physiology , Rats, Long-Evans , Sciatic Nerve/physiologyABSTRACT
This paper presents an innovative, minimally invasive, battery-free, wireless, peripheral nervous system (PNS) neural interface, which seamlessly integrates a millimeter-scale, fascicle-selective integrated circuit (IC) with extraneural recording and stimulating channels. The system also incorporates a wearable interrogator equipped with integrated machine-learning capabilities. This PNS interface is specifically tailored for adaptive neuromodulation therapy, targeting individuals with paralysis, amputation, or chronic medical conditions. By employing a neural pathway classifier and temporal interference stimulation, the proposed interface achieves precise deep fascicle selectivity for recording and stimulation without the need for nerve penetration or compression. Ultrasonic energy harvesters facilitate wireless power harvesting and data reception, enhancing the usability of the system. Key circuit performance metrics encompass a 2.2 µVrms input-referred noise, 14-bit ENOB, and a 173 dB Schreier figure of merit (FOM) for the neural analog-to-digital converter (ADC). Additionally, the ultra-low-power radio-frequency (RF) transmitter boasts a remarkable 1.38 pJ/bit energy efficiency. In vivo experiments conducted on rat sciatic nerves provide compelling evidence of the interface's ability to selectively stimulate and record neural fascicles. The proposed PNS neural interface offers alternative treatment options for diagnosing and treating neurological disorders, as well as restoring or repairing neural functions, improving the quality of life for patients with neurological and sensory deficits.
Subject(s)
Nerve Tissue , Quality of Life , Humans , Rats , Animals , Equipment Design , Wireless Technology , Sciatic NerveABSTRACT
A tutorial and comprehensive guide are presented for the design of planar spiral inductors with maximum energy delivery in biomedical implants. Rather than maximizing power transfer efficiency (PTE), the ratio of the received power to the square of the magnetic flux density is maximized in this technique. This ensures that the highest power is delivered for a given level of safe electromagnetic radiation, as measured by the specific absorption rate (SAR) in the tissue. By using quasi-static field approximations, the maximum deliverable power under SAR constraints is embedded in a lumped-element model of a 2-coil inductive link, from which planar coil geometries are derived. To compare the proposed methodology with the conventional approach that maximizes PTE, the results of both techniques are compared for three examples of state-of-the-art designs. It is demonstrated that the presented technique increases the maximum deliverable power while operating at a given level of non-ionizing radiation by factors of 8×, 410×, and 560× as compared to the three existing designs, and maintaining moderate link efficiencies of 12%, 23%, and 12%, respectively.
