Evidence map›Paper›PMID 40995804›Full record

ReviewSmall methods2026

Multi-Channel Neural Interface for Neural Recording and Neuromodulation.

Eunmin Kim, Won Gi Chung, Enji Kim, Myoungjae Oh, Joonho Paek, Taekyeong Lee, Dayeon Kim, Seung Hyun An, Sumin Kim, Jang-Ung Park

Abstract readReview
In one paragraph

Review in Small methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
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

10 authors.

Eunmin KimDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Won Gi ChungDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Enji KimDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Myoungjae OhDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Joonho PaekDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Taekyeong LeeDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Dayeon KimDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Seung Hyun AnDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Sumin KimDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
Jang-Ung ParkDepartment of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.ORCID https://orcid.org/0000-0003-1522-4958

Funding

ERC Program RS-2024-00406240Institute for Basic Science IBS-R026-D1Korea Institute of Science and Technology 2E33190Korea Institute of Science and Technology 2E33191Ministry of Science & ICT (MSIT)Ministry of Trade, Industry and EnergyNational Research Foundation 2023R1A2C2006257National Research Foundation RS-2024-00464032National Research Foundation RS-2025-16063568Sejong Science Fellowship RS-2025-00514998STEAM Research Programs RS-2024-00460364
6 · The paper itself

Abstract

Neural interfaces have emerged as pivotal platforms for advancing digital neurotherapies by enabling the real-time acquisition and monitoring of neural signals. Traditional single-channel systems are inherently limited in their capacity to capture the complex and large-scale interactions among diverse neuronal populations. In contrast, multi-channel systems provide the high spatiotemporal resolution necessary to decode the dynamic activity of neural circuits across multiple brain and spinal cord regions. This review provides a comprehensive overview of recent advances in multi-channel neural interface technologies, encompassing both penetrating and non-penetrating systems for high-resolution electrophysiological recording, as well as multifunctional platforms that integrate additional modalities such as drug delivery, optical stimulation, and chemical sensing. Recent progress in this field has been driven by advances in structural and material design, including the development of soft, flexible architectures and materials for both substrates and electrodes, which improve long-term stability and minimize tissue damage. In parallel, emerging data analysis techniques have enhanced the capacity to decode complex neural activity patterns from high-dimensional, multi-channel recordings. These technological advancements have broadened the potential applications of neural interfaces in brain-machine interfaces (BMIs), facilitating precise neuromodulation, real-time monitoring of neurological states, and integration with immersive systems such as virtual and augmented reality.

Indexed as

Brain-Computer InterfacesNeuronsAnimalsBrainHumansbrain‐machine interfacesdata analysis techniquesdigital neurotherapieshigh‐resolution recordingsmulti‐channelneural interfaces

Identifiers

PMID40995804
PMCPMC12893312

What OpenQuestion holds

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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 OpenQuestion graph.