[HTML][HTML] Tensor decomposition of EEG signals: a brief review

F Cong, QH Lin, LD Kuang, XF Gong… - Journal of neuroscience …, 2015 - Elsevier
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 …

Diagnosis of Alzheimer's disease from EEG signals: where are we standing?

J Dauwels, F Vialatte, A Cichocki - Current Alzheimer Research, 2010 - benthamdirect.com
This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from
electroencephalograms (EEG). Three major effects of AD on EEG have been observed …

An electroencephalographic signature predicts antidepressant response in major depression

W Wu, Y Zhang, J Jiang, MV Lucas, GA Fonzo… - Nature …, 2020 - nature.com
Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part
because the clinical diagnosis of major depression encompasses biologically …

Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions

A Cichocki, N Lee, I Oseledets, AH Phan… - … and Trends® in …, 2016 - nowpublishers.com
Modern applications in engineering and data science are increasingly based on
multidimensional data of exceedingly high volume, variety, and structural richness …

Tensor decompositions for signal processing applications: From two-way to multiway component analysis

A Cichocki, D Mandic, L De Lathauwer… - IEEE signal …, 2015 - ieeexplore.ieee.org
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 …

Signal processing techniques for vibration-based health monitoring of smart structures

JP Amezquita-Sanchez, H Adeli - Archives of Computational Methods in …, 2016 - Springer
Signal processing is the key component of any vibration-based structural health monitoring
(SHM). The goal of signal processing is to extract subtle changes in the vibration signals in …

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

T Yang, AA Asanjan, E Welles, X Gao… - Water Resources …, 2017 - Wiley Online Library
Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh
surface water in a timely manner for many purposes. Efficient reservoir operation requires …

Independent EEG sources are dipolar

A Delorme, J Palmer, J Onton, R Oostenveld, S Makeig - PloS one, 2012 - journals.plos.org
Independent component analysis (ICA) and blind source separation (BSS) methods are
increasingly used to separate individual brain and non-brain source signals mixed by …

Ensemble empirical mode decomposition: a noise-assisted data analysis method

Z Wu, NE Huang - Advances in adaptive data analysis, 2009 - World Scientific
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach
consists of sifting an ensemble of white noise-added signal (data) and treats the mean as …

Independent component analysis: algorithms and applications

A Hyvärinen, E Oja - Neural networks, 2000 - Elsevier
A fundamental problem in neural network research, as well as in many other disciplines, is
finding a suitable representation of multivariate data, ie random vectors. For reasons of …