Conducting Polymer‐Ionic Liquid Electrode Arrays for High‐Density Surface Electromyography
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Muscle activation and electromyography studies · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrical Engineering Division University of Cambridge Cambridge CB3 0FA UK
POLYMAT University of the Basque Country UPV/EHU Avda. Tolosa 72, Donostia‐San Sebastián Gipuzkoa 20018 Spain
LAQV/REQUIMTE, Chemistry Department NOVA School of Science and Technology Caparica 2829‐516 Portugal
Ikerbasque Basque Foundation for Science Bilbao E‐48011 Spain
Funding
Deutsche Forschungsgemeinschaft · Euskal Herriko Unibertsitatea · H2020 European Research Council
References (33)
Show source-provided references
1. 10.1016/j.jelekin.2006.09.00510.1016/j.jelekin.2006.09.005 ↗
2. 10.1109/RBME.2010.208542910.1109/RBME.2010.2085429 ↗
3. 10.1016/j.jelekin.2011.12.01210.1016/j.jelekin.2011.12.012 ↗
4. 10.1002/adfm.20190718410.1002/adfm.201907184 ↗
5. 10.1002/mus.2118010.1002/mus.21180 ↗
6. 10.1016/j.sna.2017.10.04210.1016/j.sna.2017.10.042 ↗
7. 10.3390/s15071626510.3390/s150716265 ↗
8. C.Pylatiuk M.Müller‐Riederer A.Kargov S.Schulz O.Schill M.Reischl G.Bretthauer in2009 IEEE Int. Conf. on Rehabilitation Robotics IEEE Piscataway NJ2009 pp.300–304.
9. 10.1002/adma.20150415010.1002/adma.201504150 ↗
10. 10.1038/s41565-018-0226-810.1038/s41565-018-0226-8 ↗
11. 10.1021/acsami.8b2183610.1021/acsami.8b21836 ↗
12. 10.1002/adma.20190344610.1002/adma.201903446 ↗
13. 10.1126/sciadv.abd099610.1126/sciadv.abd0996 ↗
14. 10.1002/advs.20200193810.1002/advs.202001938 ↗
15. 10.1152/japplphysiol.00521.200310.1152/japplphysiol.00521.2003 ↗
16. 10.1021/acsami.6b0502510.1021/acsami.6b05025 ↗
17. 10.1038/srep2572710.1038/srep25727 ↗
18. 10.1002/adhm.20160010810.1002/adhm.201600108 ↗
19. 10.1038/s41551-019-0347-x10.1038/s41551-019-0347-x ↗
20. 10.1126/sciadv.160164910.1126/sciadv.1601649 ↗
21. 10.1002/celc.20150055510.1002/celc.201500555 ↗
22. 10.1002/adhm.20130031110.1002/adhm.201300311 ↗
23. 10.1002/adhm.20160116710.1002/adhm.201601167 ↗
24. 10.1088/2058-8585/aadb5610.1088/2058-8585/aadb56 ↗
25. 10.1002/adhm.20130061410.1002/adhm.201300614 ↗
26. 10.1039/C5TC01888A10.1039/C5TC01888A ↗
27. 10.1021/cm070398z10.1021/cm070398z ↗
28. 10.1039/B910601D10.1039/B910601D ↗
29. 10.1002/celc.20170029710.1002/celc.201700297 ↗
30. 10.1016/j.jelekin.2006.08.00610.1016/j.jelekin.2006.08.006 ↗
31. J. A.George S.Radhakrishnan M. R.Brinton G. A.Clark International Conference on Systems Man and Cybernetics IEEE Piscataway NJ2020 pp.3441–3446.10.1109/SMC42975.2020.9283086 ↗
32. 10.1016/S1050-6411(02)00082-210.1016/S1050-6411(02)00082-2 ↗
33. 10.1002/adma.20120432210.1002/adma.201204322 ↗