Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition
Artikel i vetenskaplig tidskrift, 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

Författare

Eric Earley

Chalmers, Elektroteknik, System- och reglerteknik

Anton Berneving

Student vid Chalmers

Jan Zbinden

Chalmers, Elektroteknik, System- och reglerteknik

Max Ortiz-Catalan

Göteborgs universitet

Frontiers in Human Neuroscience

16625161 (eISSN)

Vol. 16 1030207

Ämneskategorier

Telekommunikation

Reglerteknik

Signalbehandling

DOI

10.3389/fnhum.2022.1030207

PubMed

36337856

Mer information

Senast uppdaterat

2023-10-25