Evidence map›Paper›PMID 32848688›Full record

ArticleFrontiers in neuroinformatics2020

Sparse Logistic Regression With

Dong-Wei Chen, Rui Miao, Zhao-Yong Deng, Yue-Yue Lu, Yong Liang, Lan Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroinformatics, 2020. 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
–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

5 citing papers in PubMed.

  1. Article
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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.

Dong-Wei ChenSchool of Electronic Information Engineering, University of Electronic Science and Technology of China, Zhongshan, China.
Rui MiaoFaculty of Information Technology, Macau University of Science and Technology, Macau, China.
Zhao-Yong DengSchool of Electronic Information Engineering, University of Electronic Science and Technology of China, Zhongshan, China.
Yue-Yue LuSchool of Electronic Information Engineering, University of Electronic Science and Technology of China, Zhongshan, China.
Yong LiangState Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China.
Lan HuangSchool of Electronic Information Engineering, University of Electronic Science and Technology of China, Zhongshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (

Indexed as

EEGemotion recognitionL1/2 regularizationL1 regularizationRidge Regressionsparse logistic regression

Identifiers

PMID32848688
PMCPMC7427509

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.