Extraction of Common Task Features in EEG-fMRI Data Using Coupled Tensor-Tensor Decomposition
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Summary
This work proposes the use of CTTD of a 4th order EEG tensor and 3rd order fMRI tensor, coupled partially in time and participant domains, for the extraction of the task related features in both modalities and recapitulates the well-known attention network as being positively, and the default mode network working negatively time-locked to the memory task.
- Type
- preprint
- Published
- 2019-07-02
- Cited by
- 24
- References
- 76
- Access
- Open access
- OpenAlex
- https://openalex.org/W2954500270
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:198257376
Keywords
Electroencephalography, Tensor (intrinsic definition), Computer science, Artificial intelligence, Pattern recognition (psychology)
References
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- Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG
- Realignment parameter-informed artefact correction for simultaneous EEG-fMRI recordings
- Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery
- Trial-by-Trial Coupling of Concurrent Electroencephalogram and Functional Magnetic Resonance Imaging Identifies the Dynamics of Performance Monitoring
- Structure-revealing data fusion model with applications in metabolomics
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Cited by
- Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition
- Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?
- Functional, structural, and phenotypic data fusion to predict developmental scores of pre-school children based on Canonical Polyadic Decomposition
- Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis
- Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data
- Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data
- Low-Rank Tensor Patching Based on Convolutional Sparse Coding for Communication Data Repair
- Robust coupled tensor decomposition and feature extraction for multimodal medical data
- Spectroscopic technologies and data fusion: Applications for the dairy industry
- Fast Learnings of Coupled Nonnegative Tensor Decomposition Using Optimal Gradient and Low-rank Approximation
- Decoding Multi-Brain Motor Imagery From EEG Using Coupling Feature Extraction and Few-Shot Learning
- Federated Learning Using Coupled Tensor Train Decomposition
- EEG tensor decomposition delineates neurophysiological principles underlying conflict-modulated action restraint and action cancellation
- Tensor Methods in Biomedical Image Analysis
- Assessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis
- Generalized Coupled Matrix Tensor Factorization Method Based on Normalized Mutual Information for Simultaneous EEG-fMRI Data Analysis
- Aim-based choice of strategy for MEG-based brain state classification
- Novel neural activity profiles underlying inhibitory control deficits of clinical relevance in ADHD - insights from EEG tensor decomposition.
- FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
- Validating conceptions on the role of theta and alpha band activity during the management perception-action associations through EEG-tensor decomposition.
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