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Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition.

Authors
  • Zhang, Guanghui1, 2
  • Zhang, Chi1
  • Cao, Shuo3
  • Xia, Xue4
  • Tan, Xin1
  • Si, Lichengxi1
  • Wang, Chenxin1
  • Wang, Xiaochun4
  • Zhou, Chenglin4
  • Ristaniemi, Tapani2
  • Cong, Fengyu5, 6
  • 1 School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. , (China)
  • 2 Faculty of Information Technology, University of Jyväskylä, 40100, Jyvaskyla, Finland. , (Finland)
  • 3 School of Foreign Languages, Dalian University of Technology, Dalian, 116024, China. , (China)
  • 4 School of Psychology, Shanghai University of Sport, Shanghai, 200438, China. , (China)
  • 5 School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. [email protected] , (China)
  • 6 Faculty of Information Technology, University of Jyväskylä, 40100, Jyvaskyla, Finland. [email protected] , (Finland)
Type
Published Article
Journal
Brain Topography
Publisher
Springer-Verlag
Publication Date
Jan 01, 2020
Volume
33
Issue
1
Pages
37–47
Identifiers
DOI: 10.1007/s10548-019-00750-8
PMID: 31879854
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time-frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP data) influences the time-frequency results. In this study, an optimal set of CFBW was firstly selected from the number sets of CFBW, to further analyze for TFA of the ERP data in a cognitive experiment paradigm of emotion (Anger and Neutral) and task (Go and Nogo). Then tensor decomposition algorithm was introduced to investigate the NPLC of interest from the fourth-order tensor. Compared with the TFA results which only revealed a significant difference between Go and Nogo task condition, the tensor-based analysis showed significant interaction effect between emotion and task. Moreover, significant differences were found in both emotion and task conditions through tensor decomposition. In addition, the statistical results of TFA would be affected by the selected region of interest (ROI), whereas those of the proposed method were not subject to ROI. Hence, this study demonstrated that tensor decomposition method was effective in extracting NPLC, by considering spatial information simultaneously as the potential to explore the brain mechanisms related to experimental design.

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