Background Computational analysis of routine electroencephalogram (rEEG) could improve
the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic …

Dynamically assessing the level of consciousness is still challenging during anesthesia.
With the help of Electroencephalography (EEG), the human brain electric activity can be …

Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately
by video electroencephalogram. In this study, we analyzed microstate epileptic …

Abstract Machine learning techniques for clinical applications are evolving, and the potential
impact this will have on clinical neurology is important to recognize. By providing a broad …

Objective Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's
electric field into semistable topographical EEG activity maps at different time points. Our …

Objective This study aimed to establish a machine learning model that can predict the
efficacy of antiseizure medications (ASMs) in patients with familial genetic generalized …

Electroencephalography (EEG) microstate analysis is a neuroimaging analytical method that
has received considerable attention in recent years and is widely used for analysing EEG …

Background: Depression is the most common psychiatric comorbidity of temporal lobe
epilepsy (TLE). In the recent years, studies have focused on the common pathogenesis of …

Electroencephalogram (EEG) is a very promising and widely implemented procedure to
study brain signals and activities by amplifying and measuring the post-synaptical potential …

Microstate analysis is a spatiotemporal method where instantaneous scalp potential
topography represents the current state of the brain. The temporal evolution of these scalp …