Evidence map›Paper›PMID 32736630›Full record

ArticleBiomedical engineering online2020

Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis.

Guoqiang Hu, Tianyi Zhou, Siwen Luo, Reza Mahini, Jing Xu, Yi Chang, Fengyu Cong

Abstract read
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In one paragraph

Article in Biomedical engineering online, 2020. 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

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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. Disulfidptosis-related geneTranslational cancer research · 2024
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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

7 authors.

Guoqiang HuSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.ORCID http://orcid.org/0000-0003-4961-6300
Tianyi ZhouSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Siwen LuoAffiliated Zhongshan Hospital of Dalian University, Dalian, China.
Reza MahiniSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Jing XuDepartment of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China. xujing_doc@aliyun.com.
Yi ChangDepartment of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China. changee99@gmail.com.
Fengyu CongSchool of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China. cong@dlut.edu.cn.

Funding

China Scholarship Council 201806060038Fundamental Research Funds for the Central Universities DUT2019Innovative Research Group Project of the National Natural Science Foundation of China 81471742Innovative Research Group Project of the National Natural Science Foundation of China 91748105
6 · The paper itself

Abstract

backgroundNonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms.

resultsIn simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted.

conclusionBased on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.

Indexed as

AlgorithmsElectroencephalographySignal Processing, Computer-AssistedSignal-To-Noise RatioHumansModels, TheoreticalClusteringEEGNonnegative matrix factorizationStability

Identifiers

PMID32736630
PMCPMC7393858

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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.