Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition

Abstract

As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and ε-Normalized Least Mean Squares (ε-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.

A highly integrated bionic hand with neural control and feedback for use in daily life

“…In this work, we prioritized research on prosthetic control over the provision of sensory feedback because the former has been reported to be of higher priority for patients ( 36 ). In addition, the implementation of sensory feedback in daily life requires robust and reliable sensors in the prosthesis, as well as analog and digital strategies to reduce the effect of stimulation artifacts interfering with myoelectric recordings ( 37 , 38 ). There was no commercially available multi-articulated hand prosthesis with embedded sensors that could be used for a reliable implementation of sensory feedback in daily life during this study.…”