Nonnegative Matrix and Tensor Decomposition of EEG



Power spectrum and time-frequency representation (TFR) are two significant methods to analyze EEG data in the frequency and in the time-frequency domain. The nonnegative data in the two domains can be modeled using bilinear and multi-linear transform. In this chapter, the nonnegative matrix factorization (NMF) and tensor decomposition of the canonical polyadic and Tucker models are introduced for decomposing the two-way data and the multi-way data including the modes of time, frequency, space, and subject. The strength of each decomposition model is shown by real EEG data example.

Keywords Nonnegative matrix factorization Tensor decomposition EEG 



This work was supported by the Fundamental Research Funds for the Central Universities [DUT16JJ(G)03] in Dalian University of Technology, and National Natural Science Foundation of China (Grant No. 81471742).


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© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Department of Biomedical Engineering, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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