Evidence map›Paper›PMID 35231982›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2022

[Key technologies for intelligent brain-computer interaction based on magnetoencephalography].

Haotian Xu, Anmin Gong, Peng Ding, Jiangong Luo, Chao Chen, Yunfa Fu

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. 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

6 authors at 2 institutions in 1 country.

Haotian XuFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Anmin GongSchool of Information Engineering, Chinese People's Armed Police Force Engineering University, Xi'an 710000, P. R. China.
Peng DingFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Jiangong LuoFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Chao ChenTianjin University of Technology School of Electrical and Electronic Engineering, Tianjin 300000, P. R. China.
Yunfa FuFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Kunming University · CNKunming University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.

Indexed as

Brain-Computer InterfacesMagnetoencephalographyBrainElectroencephalographyHumansImagery, PsychotherapyTechnologyBrain-computer interfaceIntelligent brain-computer interfaceMagnetoencephalographyMagnetoencephalography-brain-computer interface experimental paradigm designMagnetoencephalography feature extraction.

Identifiers

PMID35231982
PMCPMC9927744
OpenAlexW4214916804

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.