A1 Journal article (refereed)
Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis (2020)
Hu, G., Zhou, T., Luo, S., Mahini, R., Xu, J., Chang, Y., & Cong, F. (2020). Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis. Biomedical Engineering Online, 19, Article 61. https://doi.org/10.1186/s12938-020-00796-x
JYU authors or editors
Publication details
All authors or editors: Hu, Guoqiang; Zhou, Tianyi; Luo, Siwen; Mahini, Reza; Xu, Jing; Chang, Yi; Cong, Fengyu
Journal or series: Biomedical Engineering Online
eISSN: 1475-925X
Publication year: 2020
Publication date: 31/07/2020
Volume: 19
Article number: 61
Publisher: BioMed Central
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1186/s12938-020-00796-x
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71329
Abstract
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.
Keywords: EEG; spectrum analysis; algorithms; clusters; stability (invariability)
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1
