Independent component analysis (ICA) is increasing in popularity in the field of biomedical
signal processing. It is generally used when it is required to separate measured multi …

The book systematically introduces theories of frequently-used modern signal processing
methods and technologies, and focuses discussions on stochastic signal, parameter …

Tensors or multiway arrays are functions of three or more indices (i, j, k,...)-similar to matrices
(two-way arrays), which are functions of two indices (r, c) for (row, column). Tensors have a …

Over the last ten years blind source separation (BSS) has become a prominent processing
tool in the study of human electroencephalography (EEG). Without relying on head modeling …

X≈ WH, W∈ RM× R, H∈ RN× R,(1) to 'explain'the data matrix X, where W≥ 0, H≥ 0, and
R≤ min {M, N}. At first glance, NMF is nothing but an alternative factorization model to …

Edited by the people who were forerunners in creating the field, together with contributions
from 34 leading international experts, this handbook provides the definitive reference on …

Tensor decompositions are at the core of many blind source separation (BSS) algorithms,
either explicitly or implicitly. In particular, the canonical polyadic (CP) tensor decomposition …

The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a
new method for muscle artifact removal in EEG is presented, based on canonical correlation …

In real-world applications such as those for speech and audio, there are signals that are
nonstationary but can be modeled as being stationary within local time frames. Such signals …

Canonical decomposition is a key concept in multilinear algebra. In this paper we consider
the decomposition of higher-order tensors which have the property that the rank is smaller …