Abstract
Surface electromyography (EMG) is used as a medical diagnostic and to control prosthetic limbs. Electrode arrays that provide large-area, high density recordings have the potential to yield significant improvements in both fronts, but the need remains largely unfulfilled. Here, digital fabrication techniques are used to make scalable electrode arrays that capture EMG signals with mm spatial resolution. Using electrodes made of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) composites with the biocompatible ionic liquid (IL) cholinium lactate, the arrays enable high quality spatiotemporal recordings from the forearm of volunteers. These recordings allow to identify the motions of the index, little, and middle fingers, and to directly visualize the propagation of polarization/depolarization waves in the underlying muscles. This work paves the way for scalable fabrication of cutaneous electrophysiology arrays for personalized medicine and highly articulate prostheses.
Highlights
Surface electromyography (EMG) is used as a medical diagnostic and to needle EMG provides very high quality and locally specific signals, the insercontrol prosthetic limbs
This work paves the way for scalable fabrication of cutaneous of these devices impede large area scalability, and the fact that they still penetrate the epidermis makes them uncomfortable to wear and carries the risk of infections.[7]
The results shown here indicate that PEDOT:PSS:ionic liquids (ILs) forms good enough mechanical and electrical contact to skin to allow high quality EMG recordings to be made from mm-size electrodes
Summary
To demonstrate the ability of the electrodes to record EMG signals, arrays were placed on the posterior forearm of a volunteer, over the extensor digitorum communis and the extensor carpi ulnaris muscles (Figure 3a). A longer sequence is available for viewing as a movie in Supporting Information (Video SV1) Such high-resolution spatial maps are useful in the clinic to help determine muscle conduction velocity, estimate the size and number of motor units, and investigate (neuro-)muscular pathologies.[32] They can be used to train classifiers that learn to discriminate between different intended motions for highly articulate myoelectric prostheses.[30].
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