Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition
Zhang, G., Zhang, C., Cao, S., Xia, X., Tan, X., Si, L., Wang, C., Wang, X., Zhou, C., Ristaniemi, T., & Cong, F. (2020). Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition. Brain Topography, 33(1), 37-47. https://doi.org/10.1007/s10548-019-00750-8
Published in
Brain TopographyDate
2020Copyright
© The Author(s) 2019
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.
...


Publisher
SpringerISSN Search the Publication Forum
0896-0267Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/33905529
Metadata
Show full item recordCollections
Additional information about funding
Open access funding provided by University of Jyväskylä (JYU). This work was supported by National Natural Science Foundation of China (Grant Nos. 91748105, 81471742, and 61703069) and the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China, and the scholarships from China Scholarship Council (No. 201806060165).License
Related items
Showing items with similar title or keywords.
-
Evaluation and extraction of mismatch negativity through exploiting temporal, spectral, time-frequency, and spatial features
Cong, Fengyu (University of Jyväskylä, 2010) -
Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition
Wang, Deqing; Wang, Xiaoyu; Zhu, Yongjie; Toiviainen, Petri; Huotilainen, Minna; Ristaniemi, Tapani; Cong, Fengyu (Springer International Publishing, 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 ... -
Effect of parametric variation of center frequency and bandwidth of morlet wavelet transform on time-frequency analysis of event-related potentials
Zhang, Guanghui; Tian, Lili; Chen, Huaming; Li, Peng; Ristaniemi, Tapani; Wang, Huili; Li, Hong; Chen, Hongjun; Cong, Fengyu (Springer Nature Singapore Pte Ltd., 2017) -
Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint
Han, Yue; Lin, Qiu-Hua; Kuang, Li-Dan; Gong, Xiao-Feng; Cong, Fengyu; Wang, Yu-Ping; Calhoun, Vince D. (Institute of Electrical and Electronics Engineers (IEEE), 2022)Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity ... -
Generalization of Linked Canonical Polyadic Tensor Decomposition for Group Analysis
Wang, Xiulin; Zhang, Chi; Ristaniemi, Tapani; Cong, Fengyu (Springer International Publishing, 2019)Real-world data are often linked with each other since they share some common characteristics. The mutual linking can be seen as a core driving force of group analysis. This study proposes a generalized linked canonical ...