Evidence map›Paper›PMID 35892035›Full record

ReviewBiomedical engineering letters2022

High-density neural recording system design.

Han-Sol Lee, Kyeongho Eom, Minju Park, Seung-Beom Ku, Kwonhong Lee, Hyung-Min Lee

Open access · greenAbstract readReview
In one paragraph

Review in Biomedical engineering letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
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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

6 authors at 1 institution in 1 country.

Han-Sol LeeSchool of Electrical Engineering, Korea University, Seoul, South Korea.
Kyeongho EomSchool of Electrical Engineering, Korea University, Seoul, South Korea.
Minju ParkSchool of Electrical Engineering, Korea University, Seoul, South Korea.
Seung-Beom KuSchool of Electrical Engineering, Korea University, Seoul, South Korea.
Kwonhong LeeSchool of Electrical Engineering, Korea University, Seoul, South Korea.
Hyung-Min LeeSchool of Electrical Engineering, Korea University, Seoul, South Korea.ORCID 0000-0003-1191-3553
Korea University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Implantable medical devices capable of monitoring hundreds to thousands of electrodes have received great attention in biomedical applications for understanding of the brain function and to treat brain diseases such as epilepsy, dystonia, and Parkinson's disease. Non-invasive neural recording modalities such as fMRI and EEGs were widely used since the 1960s, but to acquire better information, invasive modalities gained popularity. Since such invasive neural recording system requires high efficiency and low power operation, they have been implemented as integrated circuits. Many techniques have been developed and applied when designing integrated high-density neural recording architecture for better performance, higher efficiency, and lower power consumption. This paper covers general knowledge of neural signals and frequently used neural recording architectures for monitoring neural activity. For neural recording architecture, various neural recording amplifier structures are covered. In addition, several neural processing techniques, which can optimize the neural recording system, are also discussed.

Indexed as

High-densityNeural processingNeural recordingNeural signal

Identifiers

PMID35892035
PMCPMC9308855
OpenAlexW4281787652

What OpenQuestion holds

Textmetadata
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.