Evidence map›Paper›PMID 41839845›Full record

ArticleMicrosystems & nanoengineering2026

Conformal bumped electrode web for chronic ECoG recordings in swine.

Minghao Wang, Hao Jiang, Chuner Ni, Xinhua Zhou, Yuchen Xu, Siyan Shang, Xiaoli You, Wenhao Wang, Chaojie Zhou, Wenbin Zhang and 4 more

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Minghao Wang *MOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Hao Jiang *MOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Chuner Ni *MOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Xinhua ZhouMOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Yuchen XuCenter of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, China.
Siyan ShangMOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Xiaoli YouNational Key Laboratory of Unmanned Aerial Vehicle Technology, Unmanned System Research Institute, School of Artificial Intelligence, Northwestern Polytechnical University, Xi'an, China.
Wenhao WangMOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Chaojie ZhouMOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Wenbin ZhangMOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China.
Xinyi WangMOE Engineering Research Center of Smart Microsensors and Microsystems, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China. wxy317@hdu.edu.cn.
Shaomin ZhangQiushi Academy for Advanced Studies, Zhejiang University, The State Key Laboratory of Brain-Machine Intelligence, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China. shaomin@zju.edu.cn.
Lufeng ShiDepartment of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Bowen JiNational Key Laboratory of Unmanned Aerial Vehicle Technology, Unmanned System Research Institute, School of Artificial Intelligence, Northwestern Polytechnical University, Xi'an, China. bwji@nwpu.edu.cn.ORCID http://orcid.org/0000-0002-2655-4840

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62204204
6 · The paper itself

Abstract

The acquisition of high-quality electrocorticogram (ECoG) signal is of great significance for the diagnosis and treatment of neurological diseases such as high amputation, visual injury, epilepsy and Parkinson's disease. Currently, flexible ECoG electrodes have received attention due to their low mechanical mismatch and minimally invasive characteristics. However, the traditional ECoG electrodes are non-stretchable planar structures that cannot be conformal with the cerebral cortex, which is in constant motion and has sulci and gyri structure. In this work, a flexible stretchable ECoG electrode with bumped electrodes was developed to alleviate these problems. Firstly, the mechanical simulation results show that this stretchable electrode structure can effectively reduce the stress mismatch between electrode and tissue interface. Secondly, the results of cyclic voltammetry scanning and mechanical tensile experiments show that the stretchable ECoG electrode structure can be conformally attached to the surface of the cerebral cortex and maintain good electrochemical stability during continuous stretching. Third, the bumped electrode has a larger adhesive force than the planar electrode and can significantly reduce the background noise by conformal attachment and electrochemical modification of PEDOT:PSS. Most importantly, in vivo animal experiments showed that the stretchable ECoG electrode can continuously record high-quality ECoG signals on the surface of the cerebral cortex of swine over an area of 22 × 22 mm

Identifiers

PMID41839845
PMCPMC12992586

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.