Functional connectivity and effective connectivity of the human brain, representing statistical
dependence and directed information flow between cortical regions, significantly contribute …

Electroencephalography (EEG) signals have been proven to be one of the most predictive
and reliable indicators for estimating driving fatigue state. However, how to make full use of …

Deep neural networks have recently been successfully extended to EEG-based driving
fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel …

In EEG based driving fatigue detection, the redundant EEG channels would increase the
probability of introducing noise and heaving calculation burden. Aiming at this point, we built …

This study aimed to develop a time–frequency method for measuring directional interactions
over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a …

Postictal generalized EEG suppression is the state of suppression of electrical activity at the
end of a seizure. Prolongation of this state has been associated with increased risk of …

The mental state of a driver can be accurately and reliably evaluated by detecting the
driver's electroencephalogram (EEG) signals. However, traditional machine learning and …

Current research aims at identifying voluntary brain activation in patients who are
behaviorally diagnosed as being unconscious, but are able to perform commands by …

Objective We sought to determine whether the presence or surgical removal of certain
nodes in a connectivity network constructed from intracranial electroencephalography …

Two stages of the creative writing process were characterized through mobile scalp
electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG …