Finding the means to efficiently summarize electroencephalographic data has been a long-
standing problem in electrophysiology. A popular approach is identification of component …

In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and
independent component analysis (ICA) have widely been used. However, as both methods …

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and
relatively low-cost measure of mesoscale brain dynamics with high temporal resolution …

Abstract Independent Component Analysis (ICA) has proven to be an effective data driven
method for analyzing EEG data, separating signals from temporally and functionally …

It has been known for several decades that electric potential recordings provide a wealth of
information about brain function. Electroencephalogram (EEG) signals inform about various …

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 …

Multi-subject or group-level component analysis provides a data-driven approach to study
properties of brain networks. Algorithms for group-level data decomposition of functional …

Independent component analysis (ICA) is a multivariate approach that has become
increasingly popular for analyzing brain imaging data. In contrast to the widely used general …

Principal component analysis (PCA) by singular value decomposition (SVD) may be used to
analyze an epoch of a multichannel electroencephalogram (EEG) into multiple linearly …

Data may now be recorded concurrently from EEG and functional MRI, using the
Simultaneous Imaging for Tomographic Electrophysiology (SITE) method. As yet, there is no …