Abstract
Abstract
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.
Funder
Innovative Research Group Project of the National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Subject
Radiology Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
Cited by
5 articles.
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