Collecting comprehensive data sets of the same subject has become a standard in
neuroscience research and uncovering multivariate relationships among collected data sets …

Multivariate decoding methods were developed originally as tools to enable accurate
predictions in real-world applications. The realization that these methods can also be …

The 21st century marks the emergence of “big data” with a rapid increase in the availability
of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more …

In recent years, there has been increasing interest in using steady-state visual evoked
potential (SSVEP) in brain–computer interface (BCI) systems. However, several aspects of …

Objective In this paper, we develop a dynamic functional network connectivity (FNC)
analysis approach using correlations between windowed time-courses of different brain …

Canonical correlation analysis (CCA) is applied to analyze the frequency components of
steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The …

The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a
new method for muscle artifact removal in EEG is presented, based on canonical correlation …

Canonical correlation analysis (CCA) is applied to analyze the frequency components of
steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The …

Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and
designed reference signals of sine-cosine waves usually works well for steady-state visual …

Multichannel frequency recognition methods are prevalent in SSVEP-BCI systems. These
methods increase the convenience of the BCI system for users and require no calibration …