Restoration of complex movement in the paralyzed upper limb

Transforming medicine: artificial intelligence integration in the peripheral nervous system

In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.

An Adaptive Spatial Filtering Method for Multi-Channel EMG Artifact Removal During Functional Electrical Stimulation With Time-Variant Parameters

Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G. It entails fusing the Gram-Schmidt orthogonalization (G-S) and Grubbs criterion (G) algorithms to remove the FES-evoked stimulation artifacts in multi-channel EMG signals. To verify this method, we constructed a series of simulation data by fusing the FES signal with time-variant parameters and the voluntary EMG (vEMG) signal, and applied the G-S-G method to remove any FES artifacts from the simulation data. After that, we calculated the root mean square (RMS) value for both preprocessed simulation data and the vEMG data, and then compared them. The simulation results showed that the G-S-G method was robust and effective at removing FES artifacts in simulated EMG signals, and the correlation coefficient between the preprocessed EMG data and the recorded vEMG data yielded a good performance, up to 0.87. Furthermore, we applied the proposed method to the experimental EMG data with FES-evoked stimulation artifact, and also achieved good performance with both the time-constant and time-variant parameters. This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts.

Large-scale intramuscular electrode system for chronic electromyography and functional electrical stimulation

To understand how the central nervous system (CNS) enacts movements, it seems important to monitor the activities of the many muscles involved. Likewise, to restore complex movements to paralyzed limbs with electrical stimulation requires access to most limb muscles. Intramuscular electrodes are needed to obtain isolated recordings or stimulation of individual muscles. As such, we developed and tested the stability of large arrays of implanted intramuscular electrodes. We implanted 58 electrodes in 29 upper limb muscles in each of three macaques. Electrode connectors were protected within a skull-mounted chamber. During surgery, wires were tunneled subcutaneously to target muscles, where gold anchors were crimped onto the leads. The anchors were then deployed with an insertion device. In two monkeys, the chamber was fixed to the skull with a titanium baseplate rather than acrylic cement. In multiple sessions up to 15 wk after surgery, electromyographic (EMG) signals were recorded while monkeys made the same reaching movement. EMG signals were stable, with an average (SD) coefficient of variation across sessions of 0.24 ± 0.15. In addition, at 4, 8, and 16 wk after surgery, forces to incrementing stimulus pulses were measured for each electrode. The threshold current needed to evoke a response at 16 wk was not different from that at 4 wk. Likewise, peak force evoked by 16 mA of current at 16 wk was not different from 4 wk. The stability of this system implies it could be effectively used to monitor and stimulate large numbers of muscles needed to understand the control of natural and evoked movements.NEW AND NOTEWORTHY A new method was developed to enable long-lasting recording and stimulation of large numbers of muscles with intramuscular electrodes. Electromyographic signals and evoked force responses in 29 upper limb muscles remained stable over several months when tested in nonhuman primates. This system could be used effectively to monitor and stimulate numerous muscles needed to more fully understand the control of natural and evoked movements.