Decomposing EEG data into space-time-frequency components using Parallel Factor Analysis

By F. Miwakeichi, Eduardo Martínez-Montes, Pedro A. Valdes-Sosa, N. Nishiyama, H. Mizuhara, Yoko Yamaguchi. NeuroImage, 2004

408 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/45d035883c6d8c646128e603c495d51306f77520

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Citations in the corpus

Concurrent EEG/fMRI analysis by multiway Partial Least Squares (2004, methodology): “The detailed formulation for obtaining this inverse solution can be found in a companion paper (Miwakeichi et al., 2004).”
Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “In fact, for these applications, ICA/BSS techniques have been successfully applied to remove artifacts and noise including background brain activity, electrical activity of the heart, eye-blink and other muscle activity, and environmental noise efficiently [84, 83, 54, 141, 80, 108].”
Scalable Tensor Factorizations for Incomplete Data (2010, background): “We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structure of the data and possibly reconstructing missing values (i.e., tensor completion).”
Tensor Ring Decomposition (2016, background): “These two models have been widely investigated and applied in various real-world applications, e.g., [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22].”
Tensor Decompositions and Applications (2009, methodology): “[138, 140] apply CP to a time-varying EEG spectrum arranged as a three-dimensional array with modes corresponding to time, frequency, and channel.”
Trial-by-trial coupling between EEG and BOLD identifies networks related to alpha and theta EEG power increases during working memory maintenance (2009, methodology): “Interestingly, Ishii et al. (1999), who applied a beamformer approach on MEG data, and Miwakeichi et al. (2004) and Martinez-Montes et al. (2004), who applied distributed source models on EEG data, located frontal theta activity in a region in the medial prefrontal cortex that overlaps considerably…”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “However, it has been proposed to augment this data using a third type of diversity, electrode ⇥ time ⇥ frequency [101]– [103].”
Tensorial extensions of independent component analysis for multisubject FMRI analysis (2005, methodology): “3 Also known as dcanonical decompositionT (CANDECOMP; Carroll and Chang, 1970). used extensively to decompose fluorescence spectroscopy data and in neuroimaging have recently been applied to EEG data to give a space–time–frequency decomposition (Miwakeichi et al., 2004).”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, background): “The N -tuple ( R 1 , R 2 , . . . , R N ) is consequently called the multilinear rank of the tensor X .”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “…Component Analysis (SCA), Sparse PCA (SPCA), Non-negative Matrix Factorization (NMF), Smooth Component Analysis (SmoCA), Parallel Factor Analysis (PARAFAC), Time-Frequency Component Analyzer (TFCA) and Multichannel Blind Deconvolution (MBD) [4] [40] [78] [13] [90] [149] [150] [36] [95] [108].”