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 …

Traditional spectral-based methods such as PCA are popular for anomaly detection in a
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …

Canonical correlation analysis (CCA) has been one of the most popular methods for
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …

Effective common spatial pattern (CSP) feature extraction for motor imagery (MI)
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …

Here, we present a state-of-the-art review of the research performed on the brain-computer
interface (BCI) technologies with a focus on signal processing approaches. BCI can be …

This article provides an overview of tensors, their properties, and their applications in
statistics. Tensors, also known as multidimensional arrays, are generalizations of matrices to …

The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-
deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children …

The P300-based brain–computer interface (BCI) is an extension of the oddball paradigm,
and can facilitate communication for people with severe neuromuscular disorders. It has …

Driver fatigue can be detected by constructing a discriminant mode using some features
obtained from physiological signals. There exist two major challenges of this kind of …

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize
the reference signals by extracting common features from multiple sets of …