A new on-line learning algorithm which minimizes a statistical de (cid: 173) pendency among
outputs is derived for blind separation of mixed signals. The dependency is measured by …

In these lecture notes, the authors have outlined several approaches to solve a NMF/NTF
problem. The following main conclusions can be drawn: 1) Multiplicative algorithms are not …

The widespread use of multisensor technology and the emergence of big data sets have
highlighted the limitations of standard flat-view matrix models and the necessity to move toward …

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces (BCIs)
are based on machine learning algorithms. There is a large diversity of classifier types …

With solid theoretical foundations and numerous potential applications, Blind Signal Processing
(BSP) is one of the hottest emerging areas in Signal Processing. This volume unifies …

After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been
shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, …

Learning algorithms and underlying basic mathematical ideas are presented for the
problem of adaptive blind signal processing, especially instantaneous blind separation and …

Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor
Factorization (NTF) have become prominent techniques for blind sources separation (BSS), …

In this paper, we present a multimodal emotion recognition framework called EmotionMeter
that combines brain waves and eye movements. To increase the feasibility and wearability of …

Blind source separation (BSS) and independent component analysis (ICA) are generally
based on a wide class of unsupervised learning algorithms and they found potential …