Investigations of (sub-) acute ischaemic stroke (IS) employing quantitative
electroencephalographic (QEEG) methods, in concert with other assessments, are reviewed …

One of the current challenges for translational rehabilitation research is to develop the
strategies to deliver accurate evaluation, prediction, patient selection, and decision-making …

In this paper, we propose a novel neural network model, called bi-hemisphere domain
adversarial neural network (BiDANN) model, for electroencephalograph (EEG) emotion …

Electroencephalography (EEG)–based motor imagery (MI) brain-computer interface (BCI)
technology has the potential to restore motor function by inducing activity-dependent brain …

Electroencephalography (EEG) is immediate and sensitive to cortical impairment resulting
from ischemic stroke and is considered as the potential predictive tool of stroke onset, and …

Neonatal seizures are a common emergency in the neonatal intensive care unit (NICU).
There are many questions yet to be answered regarding the temporal/spatial characteristics …

Objective Visual assessment of the EEG still outperforms current computer algorithms in
detecting epileptiform discharges. Deep learning is a promising novel approach, being able …

Electroencephalography (EEG) is a non-invasive method employed to discern human
behaviors by monitoring the neurological responses during cognitive and motor tasks …

Electroencephalography (EEG) can access ischemic stroke-derived cortical impairment and
is believed to be a prospective predictive method for acute stroke prognostics, neurological …

OBJECTIVE: Stroke is a major cause of adult-onset disability and dependency. We
investigated whether EEG parameters are of prognostic value for functional outcome …