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EEG frequency bands in psychiatric disorders: a review of resting state studies
A significant proportion of the electroencephalography (EEG) literature focuses on
differences in historically pre-defined frequency bands in the power spectrum that are …
differences in historically pre-defined frequency bands in the power spectrum that are …
Removal of artifacts from EEG signals: a review
Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
Autoreject: Automated artifact rejection for MEG and EEG data
We present an automated algorithm for unified rejection and repair of bad trials in
magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method …
magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method …
EEG artifact removal—state-of-the-art and guidelines
This paper presents an extensive review on the artifact removal algorithms used to remove
the main sources of interference encountered in the electroencephalogram (EEG) …
the main sources of interference encountered in the electroencephalogram (EEG) …
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features
A successful method for removing artifacts from electroencephalogram (EEG) recordings is
Independent Component Analysis (ICA), but its implementation remains largely user …
Independent Component Analysis (ICA), but its implementation remains largely user …
[HTML][HTML] Making the case for mobile cognition: EEG and sports performance
In the high stakes world of International sport even the smallest change in performance can
make the difference between success and failure, leading sports professionals to become …
make the difference between success and failure, leading sports professionals to become …
Independent component analysis: algorithms and applications
A fundamental problem in neural network research, as well as in many other disciplines, is
finding a suitable representation of multivariate data, ie random vectors. For reasons of …
finding a suitable representation of multivariate data, ie random vectors. For reasons of …
Default-mode brain dysfunction in mental disorders: a systematic review
In this review we are concerned specifically with the putative role of the default-mode
network (DMN) in the pathophysiology of mental disorders. First, we define the DMN concept …
network (DMN) in the pathophysiology of mental disorders. First, we define the DMN concept …
Mining event-related brain dynamics
This article provides a new, more comprehensive view of event-related brain dynamics
founded on an information-based approach to modeling electroencephalographic (EEG) …
founded on an information-based approach to modeling electroencephalographic (EEG) …
Validating the independent components of neuroimaging time series via clustering and visualization
Recently, independent component analysis (ICA) has been widely used in the analysis of
brain imaging data. An important problem with most ICA algorithms is, however, that they are …
brain imaging data. An important problem with most ICA algorithms is, however, that they are …