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Cited by 23Muscle activation and electromyography studiesNeuroscience and Neural EngineeringEEG and Brain-Computer Interfaces

Myoelectric signals and pattern recognition from implanted electrodes in two TMR subjects with an osseointegrated communication interface

Mastinu, Enzo, Branemark, Rickard, Aszmann, Oskar, Ortiz-Catalan, Max·2018·2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI 10.1109/embc.2018.8513466
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Abstract

Permanent implantation of electrodes for prosthetic control is now possible using an osseointegrated implant as a long-term stable communication interface (e-OPRA). The number of myoelectric sites to host such electrodes can be increased by Targeted Muscle Reinnervation (TMR). Traditionally, patients need to wait several months before the TMR signals are strong enough to be recorded by electrodes placed over the skin. In this study, we report the evolution of the TMR myoelectric signals recorded from two subjects via implanted electrodes using e-OPRA, and monitored for up to 48 weeks after surgery. The signals were analyzed with regard to amplitude (signal-to-noise ratio), independence (cross-correlation) and myoelectric pattern recognition (classification accuracy). TMR signals appeared at the first follow-up, one month post-surgery, and developed around 20 dB by the last. Cross-correlation between signals decreased over time and converged to a few percentage points. Classification accuracies were over 97% by the last follow up. These preliminary results suggest that implanted electrodes via the e-OPRA interface allow for an earlier and more effective use of motor signals from TMR sites compared to conventional skin surface electrodes.

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  • Intuitive motor control can be attained via advanced techniques such as targeted muscle reinnervation, regenerative peripheral nerve interfaces, or myoelectric pattern recognition.

    “Intuitive motor control can be attained via the use of the advanced techniques such as targeted muscle reinnervation (TMR), regenerative peripheral nerve interfaces (RPNIs) or the use of myoelectric pattern recognition [19–21].”

    From Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review (2020) · cites this paper as [21]

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Authors
Mastinu, E., Branemark, R., Aszmann, O., & Ortiz-Catalan, M.
Affiliations
  • Chalmers University of Technology (SE)
  • Medical University of Vienna (AT)
ORCID
Venue
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher
IEEE
Published
2018-07
Volume
2018
Pages
5174-5177
ISSN
2375-7477 (ISSN-L)
DOI
10.1109/embc.2018.8513466
OpenAlex
W2898624970
Cited by
23
References
16
Citations by year
2026: 1 · 2025: 3 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 4
FWCI
6.03
Type
conference paper
Language
en
Open access
closed
Locations
  • Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
  • PubMed (publishedVersion)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
  • CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
targeted muscle reinnervation, myoelectric signals, implanted electrodes, prosthetic control, myoelectric pattern recognition, signal-to-noise ratio, cross correlation function
MeSH terms
Artificial Limbs, Electrodes, Electrodes, Implanted, Electromyography, Humans, Muscle, Skeletal

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