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 …

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 …

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 …

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 …

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 …

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 …

We propose a framework for signal analysis of electroencephalography (EEG) that unifies
tasks such as feature extraction, feature selection, feature combination, and classification …

Developmental researchers are often interested in event-related potentials (ERPs). Data-
analytic approaches based on the observed ERP suffer from major problems such as …

We propose a novel technique to assess functional brain connectivity in
electroencephalographic (EEG)/magnetoencephalographic (MEG) signals. Our method …

Electrophysiological measures of brain function are effective tools to understand
neurocognitive phenomena and sensitive indicators of pathophysiological processes …