Journal of Neuroscience Methods· 2015Q2· Review
Tensor decomposition of EEG signals: A brief review
- 344citations
- Q2SCImago
- 2015year
Short summary
This review highlights tensor decomposition methods, specifically Canonical Polyadic Decomposition (CPD) and Tucker decomposition, as powerful tools for analyzing multi-modal EEG data beyond traditional vector or matrix approaches.
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Key points
- EEG signals can be represented as multi-way arrays (tensors) to capture their inherent multi-modal nature.
- Canonical Polyadic Decomposition (CPD) and Tucker decomposition are fundamental tensor models applicable to EEG analysis.
- The review addresses the critical step of determining the number of components for each mode in tensor decomposition.
- N-way and higher-order partial least square methods are presented as future trends for simultaneous analysis of multiple brain signal modalities.
AI-generated from the title and abstract; the full text is not read.
Abstract
Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously.
The authors' abstract, as published at the source. Journal of Neuroscience Methods, 2015 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics