Sequential nonnegative tucker decomposition on multi-way array of time-frequency transformed event-related potentials

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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“…In this study, the fourth-order tensor consisted of the time-frequency results. It can be extracted by CPD (Cong et al 2014(Cong et al , 2012d):…”

Section: Tensor Decomposition Algorithmmentioning

confidence: 99%

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

Zhang

Cao

et al. 2019

Brain Topogr

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“…15 We have previously shown that the extracted components from the fourth-order ERP tensor by LRAS NTD and the benchmark algorithm referred to as HALS NTD 16 were highly similar. 17 However, the former algorithm was much faster than the latter. 17 This finding motivated us to study the fast (probability of presentation = 0.8) were considered standard stimuli.…”

Section: 4mentioning

confidence: 99%

“…17 However, the former algorithm was much faster than the latter. 17 This finding motivated us to study the fast (probability of presentation = 0.8) were considered standard stimuli. Happy and fearful expressions were rarely presented deviant stimuli (probability = 0.1 for each) (henceforth referred to as Fear and Happy for fearful faces and happy faces, respectively).…”

Section: 4mentioning

confidence: 99%

Low-Rank Approximation Based Non-Negative Multi-Way Array Decomposition on Event-Related Potentials

Cong

Zhou

Astikainen

et al. 2014

Int. J. Neur. Syst.

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Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.

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“…To solve the problem, Tucker decomposition, a higher‐order extension of traditional singular value decomposition (SVD) and Principal Component Analysis (PCA), has been used for multichannel signal analysis and showed advantages. 32 , 33 Tucker is a method used to estimate the ranks of an N‐order input tensor so that the high‐ and low‐frequency noises can be removed synchronously. 23 , 34 While its application in multichannel bioelectric signals is rare.…”

Section: Introductionmentioning

confidence: 99%

The association of magnetoencephalography high‐frequency oscillations with epilepsy types and a ripple‐based method with source‐level connectivity for mapping epilepsy sources

Shi

Lin

et al. 2023

CNS Neurosci Ther

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Sequential nonnegative tucker decomposition on multi-way array of time-frequency transformed event-related potentials

Cited by 4 publications

References 35 publications

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

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

Low-Rank Approximation Based Non-Negative Multi-Way Array Decomposition on Event-Related Potentials

The association of magnetoencephalography high‐frequency oscillations with epilepsy types and a ripple‐based method with source‐level connectivity for mapping epilepsy sources

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