Multi-way space–time–wave-vector analysis for EEG source separation

Multi-way space-time-wave-vector analysis for EEG source separation

For the source analysis of electroencephalographic (EEG) data, both equivalent dipole models and more realistic distributed source models are employed. Several authors have shown that the canonical polyadic decomposition (also called ParaFac) of space–time– frequency (STF) data can be used to fit equivalent dipoles to the electric potential data. In this paper we propose a new multi-way approac...

EEG Montage Analysis in Blind Source Separation

Blind source separation (BSS) is a relatively recent technique, more and more applied in electroencephalographic (EEG) signal processing. Still, the classical mixing model of the BSS does not take into account the real recording set-up. In fact, a major problem in electrophysiological recording systems (e.g. ECG, EEG, EMG) is to find a region in the human body whose bio-potential activity can b...

EEG montage analysis in the Blind Source Separation framework

Blind source separation (BSS) is a relatively recent technique, more and more applied in electroencephalographic (EEG) signal processing. Still, the classical mixing model of the BSS does not take into account the real recording set-up. In fact, a major problem in electrophysiological recording systems (e.g. ECG, EEG, EMG) is to find a region in the human body whose bio-potential activity can b...

Nonnegative Matrix and Tensor Factorizations - Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Multivariate multi-way analysis of multi-source data

MOTIVATION Analysis of variance (ANOVA)-type methods are the default tool for the analysis of data with multiple covariates. These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality. However, the existing multi-way analysis methods are not designed for the currently...