Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition

Wang, Xiulin; Liu, Wenya; Toiviainen, Petri; Ristaniemi, Tapani; Cong, Fengyu

doi:10.1016/j.jneumeth.2019.108502

Cited by 15 publications

(26 citation statements)

References 36 publications

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“…However, the selection of the number of coupled components and the coupled modes mainly relys on the data characteristics used in the applications. Refer to our previous study, we use a correlation analysis in the spectral and adjacency modes in our study [24].…”

Section: Discussionmentioning

confidence: 99%

“…L f and L c denote the number of components coupled in spectral and adjacency modes, and L f,c ≤ min( R HC , R MDD ). The fast hierarchical alternative least squares (FHALS), an accelerated version of the hierarchical alternative least squares (HALS) algorithm, has been effectively applied to a number of (coupled) tensor decomposition problems [24], [43], [44]. In this study, we apply the FHALS algorithm to optimize the DC-NTD problem in (3), and introduce the low-rank approximation to reduce computational complexity [45], [46].…”

Section: Methodsmentioning

confidence: 99%

“…However, most previous studies filter EEG signals into a range of frequency bands (e.g., 8-13 Hz for the alpha band), which considers the FC as a static state in a predefined frequency range and ignores exhaustive spectral dynamics in FC. Music perception is a complex cognitive task, which is characterized by dynamics of frequency-specific brain networks for musical features processing [20]- [24]. To the best of our knowledge, the oscillatory dFC in MDD during music perception has not been investigated yet.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Zhu et al applied CP decomposition to explore the task-related dFC characterized by spatio-temporal-spectral modes of covariation from the adjacency tensor (connectivity × time-subject × frequency) [22], [34]. However, those applications only focus on the decomposition of one single tensor, which are based on the assumption that the underlying spatio-spectral features are consistent among subjects or groups [24], [28]. Coupled tensor decomposition (CTD), the extension of tensor decomposition to multiple block tensors, enables the simultaneous extraction of common features shared among tensors and individual features specified for each tensor.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Liu

Wang

et al. 2021

Preprint

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Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Liu

Wang

et al. 2021

Preprint

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“…Therefore, in this paper we arrange EEG data in multiple dimensions as a tensor. Now-a-days, EEG data is extensively arranged as tensor [20,21] which preserve multi-way nature of the data. To recover missing data, we employ modern tensor factorization methods such as CPD and CP-WOPT.…”

Section: Introductionmentioning

confidence: 99%

Classification Analysis of Tensor-Based Recovered Missing EEG Data

Akmal

Zubair²,

Alquhayz

2021

IEEE Access

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This paper discusses impact of recovering missing Electroencephalography (EEG) data on classification accuracy of hand movements using tensor-based methods. Improvement in classification accuracy is important for efficient performance of prosthesis. Classification accuracy relies on quality of the observed data. In practice, observed data is usually incomplete because of disconnection of electrodes and other artefacts, which negatively affect the classification accuracy. In this paper, we employ tensor-based imputation methods (canonical/polyadiac decomposition (CPD), weighted optimization version of CPD (CP-WOPT and Nonnegative Matrix Factorization (NMF)) to recover missing data in EEG signals and apply various classifiers to classify hand movements on recovered data. In particular, structured missing data was considered because that is how data gets missed in real-life data acquisition. Percentage of missing data was changed from 10% to 50%. Classifiers are applied on complete, missing and recovered data explicitly to test the performance of our framework. Results show that mean classification accuracy on complete data, missing data and recovered data was 71%, 53% and 64% respectively which shows applicability of our framework.

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Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

Belyaeva

Gabrielson²,

Wang³

et al. 2022

Neuroinform

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Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition

Cited by 15 publications

References 36 publications

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Classification Analysis of Tensor-Based Recovered Missing EEG Data

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

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