Evidence map›Paper›PMID 37294411›Full record

ArticleMedical & biological engineering & computing2023

Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU.

Linlin Wang, Mingai Li, Liyuan Zhang

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Linlin WangFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Mingai LiFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China. limingai@bjut.edu.cn.ORCID http://orcid.org/0000-0003-0718-8555
Liyuan ZhangFaculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China.

Funding

National Key Research and Development Program of China 2021YFA1000200National Natural Science Foundation of China 11832003National Natural Science Foundation of China 62173010
6 · The paper itself

Abstract

Deep learning has been applied to the recognition of motor imagery electroencephalograms (MI-EEG) in brain-computer interface, and the performance results depend on data representation as well as neural network structure. Especially, MI-EEG is so complex with the characteristics of non-stationarity, specific rhythms, and uneven distribution; however, its multidimensional feature information is difficult to be fused and enhanced simultaneously in the existing recognition methods. In this paper, a novel channel importance (NCI) based on time-frequency analysis is proposed to develop an image sequence generation method (NCI-ISG) for enhancing the integrity of data representation and highlighting the contribution inequalities of different channels as well. Each electrode of MI-EEG is converted to a time-frequency spectrum by utilizing short-time Fourier transform; the corresponding part to 8-30 Hz is combined with random forest algorithm for computing NCI; and it is further divided into three sub-images covered by α (8-13 Hz), β

Indexed as

AlgorithmsBrain-Computer InterfacesElectroencephalographyImagery, PsychotherapyImaginationNeural Networks, ComputerReproducibility of ResultsBrain computer interfaceConvolutional neural networkGate recurrent unitMotor imagery electroencephalogramNovel channel importance

Identifiers

PMID37294411

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.