Myolink: a 128-channel, 18 nV/√Hz, embedded recording system, optimized for high-density surface electromyogram acquisition
Author(s)
Koutsoftidis, Simos
Barsakcioglu, Deren Y
Petkos, Konstantinos
Farina, Dario
Drakakis, Emmanuel
Type
Journal Article
Abstract
Objective: We present Myolink, a portable, modular, low-noise electrophysiology amplifier optimized for high-density surface electromyogram (HD sEMG) acquisition. Methods: Myolink consists of 4 modules. Each 10 × 8 cm module can concurrently acquire 32 unipolar electrode potentials at sampling rates of up to 8 kHz with 24-bit resolution. Modules may be stacked and operated synchronously, supporting the concurrent acquisition of up to 128 channels. A custom high-performance analog front-end provides an input-referred-noise <0.4 μVRMS for a bandwidth of 23–524 Hz (tuneable by design choices), which is lower than current commercial systems. Digitized signals are processed by a custom on-board FPGA-based controller and subsequently transmitted to a PC via a medical-grade isolated USB 2.0 interface. Results: The system has been tested by recording experimental HD sEMG signals, which have been subsequently decomposed into motor unit action potentials. Compared to commercially available systems, the proposed recording system led to higher-quality surface EMG acquisition, as well as higher decomposition accuracy across a wide range of forces, with the greater gain for forces ≤ 20% of the maximum voluntary contraction. Significance: A portable, ultra-low-noise, HD sEMG amplifier design has been implemented and characterized. The system provides IRN performance beyond the capabilities of current state-of-the-art instrumentation and this improvement has a significant effect on HD sEMG decomposition.
Date Issued
2022-11-01
Date Acceptance
2022-04-16
Citation
IEEE Transactions on Biomedical Engineering, 2022, 69 (11), pp.3389-3396
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers
Start Page
3389
End Page
3396
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
69
Issue
11
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/35468056
Subjects
Electromyography
Engineering
Engineering, Biomedical
high-density
low noise
multi-channel
neural interfacing
NOISE
recording
Science & Technology
Technology
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2022-04-25