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 …

A deep learning classifier for detecting seizures in neonates is proposed. This architecture is
designed to detect seizure events from raw electroencephalogram (EEG) signals as …

This paper presents a signal modeling-based new methodology of automatic seizure
detection in EEG signals. The proposed method consists of three stages. First, a multirate …

The aim of this study was to develop methods for detecting the nonstationary periodic
characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates …

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate
seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based …

This paper presents a method for estimating the instantaneous frequency (IF) of
multicomponent signals. The technique involves, firstly, the transformation of the one …

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 …

In this paper, a feature extraction method through the time-series prediction based on the
adaptive neuro-fuzzy inference system (ANFIS) is proposed for brain–computer interface …

Objective The primary aim of this study is to develop and evaluate algorithms for neonatal
EEG artefact detection. The secondary aim is to subsequently assess its application as a …