[HTML][HTML] Tensor decomposition of EEG signals: a brief review
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 …
signals tend to be represented by a vector or a matrix to facilitate data processing and …
Introduction to machine learning for brain imaging
Machine learning and pattern recognition algorithms have in the past years developed to
become a working horse in brain imaging and the computational neurosciences, as they are …
become a working horse in brain imaging and the computational neurosciences, as they are …
Tensors for data mining and data fusion: Models, applications, and scalable algorithms
Tensors and tensor decompositions are very powerful and versatile tools that can model a
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …
wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which …
Applications of tensor (multiway array) factorizations and decompositions in data mining
M Mørup - Wiley Interdisciplinary Reviews: Data Mining and …, 2011 - Wiley Online Library
Tensor (multiway array) factorization and decomposition has become an important tool for
data mining. Fueled by the computational power of modern computer researchers can now …
data mining. Fueled by the computational power of modern computer researchers can now …
Image-based process monitoring using low-rank tensor decomposition
Image and video sensors are increasingly being deployed in complex systems due to the
rich process information that these sensors can capture. As a result, image data play an …
rich process information that these sensors can capture. As a result, image data play an …
Testing the stimulus-to-response bridging function of the oddball-P3 by delayed response signals and residue iteration decomposition (RIDE)
It has been proposed that the P3b component of event-related potentials (ERPs) reflects
linking of responses to target stimuli. This proposal was tested by disconnecting the …
linking of responses to target stimuli. This proposal was tested by disconnecting the …
A regularized discriminative framework for EEG analysis with application to brain–computer interface
We propose a framework for signal analysis of electroencephalography (EEG) that unifies
tasks such as feature extraction, feature selection, feature combination, and classification …
tasks such as feature extraction, feature selection, feature combination, and classification …
[HTML][HTML] A tutorial on the use of temporal principal component analysis in developmental ERP research–Opportunities and challenges
Developmental researchers are often interested in event-related potentials (ERPs). Data-
analytic approaches based on the observed ERP suffer from major problems such as …
analytic approaches based on the observed ERP suffer from major problems such as …
Modeling sparse connectivity between underlying brain sources for EEG/MEG
We propose a novel technique to assess functional brain connectivity in
electroencephalographic (EEG)/magnetoencephalographic (MEG) signals. Our method …
electroencephalographic (EEG)/magnetoencephalographic (MEG) signals. Our method …
[HTML][HTML] Advances in electrophysiological research
Electrophysiological measures of brain function are effective tools to understand
neurocognitive phenomena and sensitive indicators of pathophysiological processes …
neurocognitive phenomena and sensitive indicators of pathophysiological processes …