Electroencephalography (EEG) is the most popular brain activity recording technique used
in wide range of applications. One of the commonly faced problems in EEG recordings is the …

Neonatal seizures are a neurological emergency and prompt treatment is required. Seizure
burden in neonates can be very high, status epilepticus a frequent occurrence, and the …

Identifying a core set of features is one of the most important steps in the development of an
automated seizure detector. In most of the published studies describing features and seizure …

Ocular artifacts (OAs) are one the most important form of interferences in the analysis of
electroencephalogram (EEG) research. OAs removal/reduction is a key analysis before the …

Background Traditionally, assessment of psychiatric symptoms has been relying on their
retrospective report to a trained interviewer. The emergence of smartphones facilitates …

Time-frequency (TF) based machine learning methodologies can improve the design of
classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF …

In clinical practice, algorithmic predictions may seriously jeopardise patients' health and thus
are required to be validated by medical experts before a final clinical decision is met …

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts.
Muscular activities strongly obscure EEG signals and complicate subsequent EEG-based …

The electrical potential produced by the cardiac activity sometimes contaminates
electroencephalogram (EEG) recordings, resulting in spiky activities that are referred to as …

Electroencephalogram (EEG) data are often contaminated by various electrophysiological
artifacts. Among all these artifacts, the muscle activity is particularly difficult to remove. In the …