Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition

Deqing Wang, Xiaoyu Wang, Yongjie Zhu, Petri Toiviainen, Minna Huotilainen, Tapani Ristaniemi, Fengyu Cong

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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 …
Original languageEnglish
Title of host publicationAdvances in Neural Networks – ISNN 2018 : 15th International Symposium on Neural Networks, ISNN 2018, Minsk, Belarus, June 25–28, 2018, Proceedings
EditorsTingwen Huang, Jiancheng Lv, Changyin Sun, Alexander V. Tuzikov
Number of pages11
Publication date2018
ISBN (Print)978-3-319-92536-3
ISBN (Electronic)978-3-319-92537-0
Publication statusPublished - 2018
MoE publication typeA4 Article in conference proceedings
Event15th International Symposium on Neural Networks - Minsk, Belarus
Duration: 25 Jun 201828 Jun 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of Science

  • 516 Educational sciences
  • 3112 Neurosciences
  • 3111 Biomedicine

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