Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
Wang, D., Wang, X., Zhu, Y., Toiviainen, P., Huotilainen, M., Ristaniemi, T., & Cong, F. (2018). Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition. In T. Huang, J. Lv, C. Sun, & A. V. Tuzikov (Eds.), ISNN 2018 : Advances in Neural Networks : 15th International Symposium on Neural Networks, Proceedings (pp. 789-799). Springer International Publishing. Lecture Notes in Computer Science, 10878. https://doi.org/10.1007/978-3-319-92537-0_89
Published inLecture Notes in Computer Science
© Springer International Publishing AG, part of Springer Nature 2018.
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.
PublisherSpringer International Publishing
Parent publication ISBN978-3-319-92536-3
ConferenceInternational Symposium on Neural Networks
Is part of publicationISNN 2018 : Advances in Neural Networks : 15th International Symposium on Neural Networks, Proceedings
Publication in research information system
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