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Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
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3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles.

Ruben Ruiz-Mateos Serrano1, Ana Aguzin2, Eleni Mitoudi-Vagourdi1

  • 1Electrical Engineering Division, Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge, CB3 0FA, UK.

Biomaterials
|May 28, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed printable, biocompatible electrodes using PEDOT:PSS, DES, and PEGDA for long-term electrophysiology. These novel electrodes enable reliable sign language translation and advanced brain-machine interfaces.

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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Electrophysiology

Background:

  • Advancements in medical wearables require innovative electrodes for skin-based electrical recordings.
  • Existing electrodes often face limitations in long-term wearability and signal fidelity.

Purpose of the Study:

  • To develop printable, biocompatible electrodes using a novel eutectogel formulation for enhanced cutaneous electrophysiology.
  • To investigate the effects of printing parameters and electrode geometry on performance.
  • To demonstrate the utility of these electrodes for electrophysiological measurements and sign language recognition.

Main Methods:

  • Fabrication of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) based electrodes with deep eutectic solvent (DES) and polyethylene glycol diacrylate (PEGDA).
  • Optimization of printing parameters and evaluation of material properties (conductivity, morphology, stability, biocompatibility).
  • Testing electrode performance in impedance measurements, forearm EMG recordings, and body surface potential mapping (BSPM) for sign language recognition.

Main Results:

  • Optimized eutectogel formulations exhibited desirable conducting properties, mechanical stability, and biocompatibility.
  • Different electrode geometries (flat, pyramidal, striped, wavy) influenced skin conformability and contact.
  • Successful long-term forearm EMG recordings and BSPMs were achieved, enabling analysis of finger movements.
  • A logistic regressor classifier trained on BSPMs accurately identified three sign language letters (B, I, O).

Conclusions:

  • The novel eutectogel-based electrode fabrication method provides a promising platform for long-term cutaneous electrophysiology.
  • These printable electrodes offer potential for applications in online sign language translation and brain-machine interfaces.
  • The study highlights the importance of material formulation and electrode design for effective electrophysiological monitoring.