Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays. Issue 1 (11th January 2023)

• Record Type: Journal Article
• Authors: Ershad, Faheem; Houston, Michael; Patel, Shubham; Contreras, Luis; Koirala, Bikram; Lu, Yuntao; Rao, Zhoulyu; Liu, Yang; Dias, Nicholas; Haces-Garcia, Arturo; Zhu, Weihang; Zhang, Yingchun; Yu, Cunjiang
• Editors: Sharma, Pradeep
• Is Part Of: PNAS nexus. Volume 2:Issue 1(2023)
• Publication Date: 2023-01-11
• Subjects: Science -- Periodicals; 505
• DOI: 10.1093/pnasnexus/pgac291
• Languages: English
• ISSNs: 2752-6542

Abstract

Abstract: Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.