3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles.

The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) and polyethylene glycol diacrylate (PEGDA) to develop printable and biocompatible electrodes for long-term cutaneous electrophysiology recordings. The impact of printing parameters on the conducting properties, morphological characteristics, mechanical stability and biocompatibility of the material were investigated. The optimised eutectogel formulations were fabricated in four different patterns -flat, pyramidal, striped and wavy- to explore the influence of electrode geometry on skin conformability and mechanical contact. These electrodes were employed for impedance and forearm EMG measurements. Furthermore, arrays of twenty electrodes were embedded into a textile and used to generate body surface potential maps (BSPMs) of the forearm, where different finger movements were recorded and analysed. Finally, BSPMs for three different letters (B, I, O) in sign-language were recorded and used to train a logistic regressor classifier able to reliably identify each letter. This novel cutaneous electrode fabrication approach offers new opportunities for long-term electrophysiological recordings, online sign-language translation and brain-machine interfaces.