Wireless and Stretchable Multichannel Neural Interface Device Based on Conformal Parylene Thin-Film
In the field of brain-computer interfaces, implantable neural interface devices play a crucial role as powerful tools for acquiring high-quality electrophysiological signals which significantly influence the accuracy of the subsequent processing and feedback control. The paper presents a wireless and stretchable electrocorticography (ECoG) neural device based on biocompatible Parylene C polymer by incorporating serpentine metal interconnects. In vitro electrochemical measurements verified the excellent functionality of the neural electrodes, with impedances as low as $13 \pm 0.4 \mathrm{k} \Omega$ at 1 kHz, facilitating high-resolution neural recording. The mechanical and electrochemical properties have been investigated through repetitive stretching tests. The developed microelectrode array was capable of reliably and wirelessly recording the local field potentials in the primary motor cortex of the brain with an averaged amplitude of 50 $\mu \mathrm{V}$. An optimized active-matrix architecture has been proposed by incorporating high-performance silicon nanomembrane thin-film transistors (TFTs) into parylenebased electrode array. The corresponding sensing and switching characteristics were simulated using Cadence software to demonstrate the enormous potential for highfidelity detection of weak neural signals.
Authors
- Chao Zhang (ORCID: https://orcid.org/0000-0003-2712-7207)
- Chen Liu (ORCID: https://orcid.org/0000-0002-6777-0793)
- Zixuan Wang (ORCID: https://orcid.org/0000-0002-5255-3462)
- Yuming Zhang (ORCID: https://orcid.org/0000-0002-8587-0747)
- Milin Zhang (ORCID: https://orcid.org/0000-0001-7544-1837)
- Shi‐Yuan Cheng (ORCID: https://orcid.org/0000-0003-1737-0588)
- Ruiyi Gao
Institutions
- Xidian University (CN)
- Air Force Engineering University (CN)
- Tsinghua University (CN)
Publication Details
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1109/biocas61083.2024.10798197
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Xidian University