Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition
Journal article, 2022

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 epsilon-Normalized Least Mean Squares (epsilon-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.

neurostimulation

osseointegration

implantable electrodes

sensory feedback

myoelectric pattern recognition

artifact removal

prosthesis control

Author

Eric Earley

Chalmers, Electrical Engineering, Systems and control

Anton Berneving

Student at Chalmers

Jan Zbinden

Chalmers, Electrical Engineering, Systems and control

Max Ortiz-Catalan

University of Gothenburg

Frontiers in Human Neuroscience

16625161 (eISSN)

Vol. 16 1030207

Subject Categories

Telecommunications

Control Engineering

Signal Processing

DOI

10.3389/fnhum.2022.1030207

PubMed

36337856

More information

Latest update

10/25/2023