Evidence map›Paper›PMID 38975192›Full record

ArticleHeliyon2024

Brain-machine interactive neuromodulation research tool with edge AI computing.

Yan Li, Yingnan Nie, Zhaoyu Quan, Han Zhang, Rui Song, Hao Feng, Xi Cheng, Wei Liu, Xinyi Geng, Xinwei Sun and 2 more

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

12 authors.

Yan LiInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Yingnan NieInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Zhaoyu QuanEngineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China.
Han ZhangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Rui SongInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Hao FengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Xi ChengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Wei LiuEngineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China.
Xinyi GengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Xinwei SunSchool of Data Science, Fudan University, Shanghai, China.
Yanwei FuSchool of Data Science, Fudan University, Shanghai, China.
Shouyan WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

Indexed as

Artificial intelligenceClosed-loop neuromodulationEdge AI computingMachine learningReal-timeSeizure detection

Identifiers

PMID38975192
PMCPMC11225749

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.