journal article·2024-07Open access

Formation of Anisotropic Conducting Interlayer for High‐Resolution Epidermal Electromyography Using Mixed‐Conducting Particulate Composite

Zifang Zhao, Han Yu, Duncan J. Wisniewski, Claudia Cea, Liang Ma, Eric M. Trautmann, Mark M. Churchland, Jennifer N. Gelinas, Dion Khodagholy

Advanced Science· Volume 11· Issue 27

Abstract

Abstract Epidermal electrophysiology is a non‐invasive method used in research and clinical practices to study the electrical activity of the brain, heart, nerves, and muscles. However, electrode/tissue interlayer materials such as ionically conducting pastes can negatively affect recordings by introducing lateral electrode‐to‐electrode ionic crosstalk and reducing spatial resolution. To overcome this issue, biocompatible, anisotropic‐conducting interlayer composites (ACI) that establish an electrically anisotropic interface with the skin are developed, enabling the application of dense cutaneous sensor arrays. High‐density, conformable electrodes are also microfabricated that adhere to the ACI and follow the curvilinear surface of the skin. The results show that ACI significantly enhances the spatial resolution of epidermal electromyography (EMG) recording compared to conductive paste, permitting the acquisition of single muscle action potentials with distinct spatial profiles. The high‐density EMG in developing mice, non‐human primates, and humans is validated. Overall, high spatial‐resolution epidermal electrophysiology enabled by ACI has the potential to advance clinical diagnostics of motor system disorders and enhance data quality for human‐computer interface applications.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Neuroscience and Neural Engineering · Muscle activation and electromyography studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electrical Engineering Columbia University New York 10027 USA

Department of Biomedical Engineering Columbia University New York 10027 USA

Department of Neuroscience Columbia University New York NY 10032 USA

Zuckerman Mind Brain Behavior Institute Columbia University New York 10027 USA

Kavli Institute for Brain Science Columbia University New York 10032 USA

Grossman Center for the Statistics of Mind Columbia University New York USA

Department of Neurology Columbia University Irving Medical Center New York 10032 USA

Department of Electrical Engineering University of California Irvine CA 92697 USA

Funding

National Science Foundation · National Institutes of Health

References (48)

Show source-provided references
1. 10.1109/TBME.2016.257931010.1109/TBME.2016.2579310 ↗
2. 10.1109/5.2932710.1109/5.29327 ↗
3. 10.1097/00004691-199609000-0000210.1097/00004691-199609000-00002 ↗
4. Teplan M. · Measurement Science Review · 2002 · 2 · 1
5. 10.1002/adfm.20190916510.1002/adfm.201909165 ↗
6. L. C.Miller C. K.Thompson F.Negro C. J.Heckman D.Farina J. P. A.Dewald 36th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society EMBC Chicago IL USA2014 5340.10.1109/EMBC.2014.6944832 ↗
7. 10.1016/j.bspc.2018.12.00110.1016/j.bspc.2018.12.001 ↗
8. 10.1098/rsta.2008.023510.1098/rsta.2008.0235 ↗
9. Poppendieck W. · Biomedical Engineering /Biomedizinische Technik · 2013 · 58
10. 10.1002/mus.2118010.1002/mus.21180 ↗
11. 10.1016/j.jelekin.2008.09.00210.1016/j.jelekin.2008.09.002 ↗
12. 10.1016/j.bspc.2019.10163710.1016/j.bspc.2019.101637 ↗
13. Michell A. · 2014
14. 10.1126/sciadv.abo139610.1126/sciadv.abo1396 ↗
15. 10.1002/adma.20230212710.1002/adma.202302127 ↗
16. 10.1002/celc.20170029710.1002/celc.201700297 ↗
17. 10.1002/adma.20110237810.1002/adma.201102378 ↗
18. 10.1016/j.biomaterials.2007.11.02210.1016/j.biomaterials.2007.11.022 ↗
19. 10.3389/felec.2023.125065510.3389/felec.2023.1250655 ↗
20. P.Cattarello R.Merletti 2016 IEEE Int. Symp. on Medical Measurements and Applications IEEE NYC2016.
21. 10.1002/adhm.20220266110.1002/adhm.202202661 ↗
22. Matsukawa R. · 2020 · 2001322
23. 10.1016/S0013-4694(96)96118-410.1016/S0013-4694(96)96118-4 ↗
24. 10.1126/science.120615710.1126/science.1206157 ↗
25. 10.1038/s41467-019-13993-710.1038/s41467-019-13993-7 ↗
26. 10.1002/adhm.20190023410.1002/adhm.201900234 ↗
27. 10.1038/s41528-022-00235-y10.1038/s41528-022-00235-y ↗
28. 10.1002/admt.20200032510.1002/admt.202000325 ↗
29. 10.1021/acsami.6b0502510.1021/acsami.6b05025 ↗
30. 10.1002/adhm.20140076110.1002/adhm.201400761 ↗
31. 10.1002/admi.20210035210.1002/admi.202100352 ↗
32. 10.1002/9781119082934.ch0210.1002/9781119082934.ch02 ↗
33. 10.1002/adma.20150181010.1002/adma.201501810 ↗
34. 10.1002/adma.20090088710.1002/adma.200900887 ↗
35. 10.1002/smll.20090186810.1002/smll.200901868 ↗
36. 10.1038/s43586-021-00039-w10.1038/s43586-021-00039-w ↗
37. 10.1038/s41563-023-01599-w10.1038/s41563-023-01599-w ↗
38. 10.1126/sciadv.aaz676710.1126/sciadv.aaz6767 ↗
39. 10.1002/advs.20210440410.1002/advs.202104404 ↗
40. 10.1016/0378-3782(92)90170-L10.1016/0378-3782(92)90170-L ↗
41. 10.7554/eLife.6901110.7554/eLife.69011 ↗
42. 10.1038/s41593-022-01165-810.1038/s41593-022-01165-8 ↗
43. 10.1002/advs.20200193810.1002/advs.202001938 ↗
44. 10.1002/advs.20210440410.1002/advs.202104404 ↗
45. 10.1038/s41586-022-05579-z10.1038/s41586-022-05579-z ↗
46. 10.1007/BF0020102110.1007/BF00201021 ↗
47. 10.1016/1050-6411(94)90023-X10.1016/1050-6411(94)90023-X ↗
48. 10.1016/S0925-4005(02)00448-310.1016/S0925-4005(02)00448-3 ↗