Evidence map›Paper›PMID 42189404›Full record

ReviewNano convergence2026

Advances in neuroprostheses: interfaces, materials, and applications.

Enhui He, Kangming Chen, Shishuo Liu, Haisong Chen, Yu Xiao, Rongrong Chen, Peiqin Tu, Gang Pan, Peng Lin

Abstract readReview
In one paragraph

Review in Nano convergence, 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

9 authors.

Enhui He *The State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Kangming Chen *The State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Shishuo Liu *Nanhu Brain-Computer Interface Institute, Hangzhou, China.
Haisong ChenThe State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Yu XiaoThe State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Rongrong ChenThe State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Peiqin TuThe State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Gang PanThe State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Peng LinThe State Key Laboratory of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China. penglin@zju.edu.cn.ORCID http://orcid.org/0000-0002-0679-8063

Funding

Natural Science Foundation of China 62301483Natural Science Foundation of China 62574180Pioneer R&D Program of Zhejiang 2026C01008Zhejiang Provincial Natural Science Foundation of China LZ26F010001
6 · The paper itself

Abstract

Neuroprostheses have become a pivotal technology for restoring sensory, motor, and cognitive functions, offering transformative therapeutic strategies for neurological disorders by bridging or bypassing damaged neural pathways through electronic systems. However, achieving long-term stability and high-fidelity interaction between biological and electronic systems remains a significant challenge due to the mismatch at the neural interface. This review examines the critical role of nanotechnology in building high performance neuroprostheses across six key classes: motor, visual, tactile, language, memory and olfactory. A system architecture of the neuroprostheses is proposed that highlights two critical interfaces, namely, "neural-electronic" and "environment-electronic" interfaces. We survey recent advances in materials and devices that shape better neural electrodes and novel sensors, and discuss the potential utilization of neuromorphic computing for efficient edge processing in neuroprostheses. This review aims to outline future trajectories toward high-throughput bidirectional interaction, biomimetic encoding, and adaptive closed-loop systems, aspiring to achieve seamless integration between electronic systems and biological neural circuitry.

Indexed as

Brain–computer interfaceNeural electrodesNeuromorphicNeuroprosthesesSensing materials

Identifiers

PMID42189404
PMCPMC13212867

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.