Abstract
Tensor decomposition has been widely employed for EEG signal processing in recent years. Constrained and regularized tensor decomposition often attains more meaningful and interpretable results. In this study, we applied sparse nonnegative CANDECOMP/PARAFAC tensor decomposition to ongoing EEG data under naturalistic music stimulus. Interesting temporal, spectral and spatial components highly related with music features were extracted. We explored the ongoing EEG decomposition results and properties in a wide range of sparsity levels, and proposed a paradigm to select reasonable sparsity regularization parameters. The stability of interesting components extraction from fourteen subjects’ data was deeply analyzed. Our results demonstrate that appropriate sparsity regularization can increase the stability of interesting components significantly and remove weak components at the same time.
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Notes
- 1.
The stability was measured by number of maximum correlation coefficient within [0.95, 1]. But there are two exceptions of “#11-FlucCentroid” and “#13-FlucCentroid”, which were measured within [0.9, 0.95], because no significant value appeared within [0.95, 1].
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 81471742), the Fundamental Research Funds for the Central Universities [DUT16JJ(G)03] in Dalian University of Technology in China, and the scholarships from China Scholarship Council (Nos. 201600090043 and 201600090042).
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Wang, D. et al. (2018). Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_89
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