… where the so-called core array equation image with elements g l,m,n accounts for all possible
… are found to be L, M, and N respectively, then a Tucker (L, M, N) model fits the data perfect. …

The problem of incomplete data – ie, data with missing or unknown values – in multi-way
arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, …

In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and
independent component analysis (ICA) have widely been used. However, as both methods …

We present a novel method for blind separation of instruments in single channel polyphonic
music based on a non-negative matrix factor 2-D deconvolution algorithm. The method is an …

… We analyzed a positron emission tomography data set with M=40 time points and N=157,244
voxels based on [ 18 F]-Altanserin as radioligand in order to measure serotonin-2A …

There is a increasing interest in analysis of large-scale multiway data. The concept of
multiway data refers to arrays of data with more than two dimensions, that is, taking the form of …

Quantification of cerebral blood flow (CBF) using dynamic‐susceptibility contrast MRI relies
on the deconvolution of the arterial input function (AIF), which is commonly estimated from …

The open source toolbox ‘ERPWAVELAB’ is developed for multi-channel time–frequency
analysis of event related activity of EEG and MEG data. The toolbox provides tools for data …

… of maximal curvature in the graph of {m, ExpVar}. An approximate … m = 3 (ie a Tucker(1,1,1))
and m = 4 (ie for instance given by a Tucker(2,1,1)) pose the same modeling ability thus m …

Background: Quantitative EEG (qEEG) power could potentially be used as a diagnostic tool
for Alzheimer’s disease (AD) and may further our understanding of the pathophysiology. …