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DALIAN UNIVERSITY OF TECHNOLOGY Login 中文
Chi Zhang

Associate Professor
Supervisor of Master's Candidates


Gender:Male
Alma Mater:东北大学
Degree:Doctoral Degree
School/Department:生物医学工程学院
Discipline:Biomedical Engineering. Signal and Information Processing
E-Mail:chizhang@dlut.edu.cn
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Current position: Home >> Scientific Research >> Paper Publications

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

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Indexed by:Journal Papers

Date of Publication:2020-01-01

Journal:Brain topography

Included Journals:PubMed

Volume:33

Issue:1

Page Number:37-47

ISSN No.:1573-6792

Key Words:ERP,Mother wavelet,Non-phase locked,Tensor decomposition,Time–frequency analysis

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.