Performance Characterization and Application Assessment for a Type of 3-D-Printed Microneedle Array Electrodes Toward High-Quality Electrophysiological Recording
The electrophysiological signals are inherently characterized by low signal strength, instability, and low signal-to-noise ratio (SNR). The signal quality is usually determined by the electrode–skin interface impedance (EII) and stability of contact interface, where signals may deteriorate due to a change in EII over time by dehydration of conductive gel used in conventional wet electrodes. As a novel type of dry electrode, microneedle array electrodes (MAEs) can penetrate through the stratum corneum and contact the viable epidermis without a touch to the dermis, reducing the EII and forming a stable human-machine interface for signal recording. Various MAEs have been realized by different fabrication methods; however, the performance characterization of MAEs is still insufficient to support and facilitate their applications. In this study, we investigated a type of 3-D-printed MAEs toward high-quality electrophysiological recording. The temporal EII stability of MAEs was investigated by a comparison with the commercially available wet electrodes, which demonstrated the superior performance of MAEs in long-term use. An optimized electrical model for MAEs was proposed, with the RCL connection replacing the normally used RC connection, which showed that the RCL model could achieve a better fitting of EII characteristics for MAEs. Application assessment was fulfilled by recording both electromyography (EMG) and electrocardiography (ECG), which proved the excellent behavior of MAEs in signal recording. In all experiments, no skin allergy, inflammation, or other injury was observed by MAE application. In general, this work may demonstrate a stable and reliable approach for high-quality electrophysiological recording with a strong application potential.
Authors
- Jianglang Cao (ORCID: https://orcid.org/0009-0007-2928-2426)
- Peng Fang (ORCID: https://orcid.org/0000-0002-9070-9069)
- Guanglin Li (ORCID: https://orcid.org/0000-0001-9016-2617)
- Debin Xu
- Shanyong Huang
- J. S. Fu
Institutions
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tim.2025.3578716
- Citations
- 2
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 1.82
Funders
- National Natural Science Foundation of China