A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis (2020)


Hu, Guoqiang; Zhou, Tianyi; Luo, Siwen; Mahini, Reza; Xu, Jing; Chang, Yi; Cong, Fengyu (2020). Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis. Biomedical Engineering Online, 19, 61. DOI: 10.1186/s12938-020-00796-x


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Hu, Guoqiang; Zhou, Tianyi; Luo, Siwen; Mahini, Reza; Xu, Jing; Chang, Yi; Cong, Fengyu

Lehti tai sarja: Biomedical Engineering Online

eISSN: 1475-925X

Julkaisuvuosi: 2020

Volyymi: 19

Artikkelinumero: 61

Kustantaja: BioMed Central

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: https://doi.org/10.1186/s12938-020-00796-x

Avoin saatavuus: Open access -julkaisukanavassa ilmestynyt julkaisu

Julkaisukanavan avoin saatavuus:

Julkaisun avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/71329


Tiivistelmä

Background
Nonnegative 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.

Results
In 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.

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


YSO-asiasanat: EEG; spektrianalyysi; algoritmit; klusterit; stabiilius (muuttumattomuus)

Vapaat asiasanat: nonnegative matrix factorization; stability; clustering; EEG


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Alustava JUFO-taso: 1


Viimeisin päivitys 2021-02-02 klo 10:00