This research focuses on the development of a machine learning technique based on Time-Delay Neural Networks (TDNN) and Independent Component Analysis (ICA), to analyze EEG signal dynamics related to the initiation and propagation of epileptic seizures. We aim at designing a generative model to simulate EEG time-series after alteration of specific localized channels (electrodes) in order to exp...

OBJECTIVE Self-reported sleep disturbances are present in over 80% of patients with depression. However, sleep electroencephalography (EEG) findings, based on overnight polysomnography have not always differentiated depressed patients from healthy individuals. METHOD The present paper will review the findings on sleep EEG studies in depression highlighting how recent technological and methodo...

Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abn...

Background: In Canada, 7% of children are born preterm between 29 and 36 weeks gestational age (GA). Electroencephalography (EEG) provides a bedside evaluation brain activity, yet the clinical significance several EEG patterns requires further study. The goal this study is to determine features at term equivalent (TEA) that correlate with neurodevelopmental TEA in infants 29-36 GA. Methods: Pro...

More than 80 years after its introduction by Hans Berger, the electroencephalogram (EEG) remains as an important supplementary examination in the investigation of neurological disorders and gives valuable and accurate information about cerebral function. Abnormal EEG findings may include ictal patterns, interictal epileptiform activity and non-epileptiform abnormalities. The aim of this study i...

INTRODUCTION West Syndrome (WS) (infantile myoclonic encephalopathy with hypsarrhythmia--IMEH) belongs to the infantile epileptic encephalopathies and is characterized by infantile spasms, hypsarrythmia in EEG, and abnormal psychomotor development of children. AIM Evaluation of the EEG patterns of patients with WS, correlation of the EEG patterns with the cause of epilepsy and an assessment o...

Brain is one of the most complex organ of the humans, it controls the coordination of human muscles & nerves. EEG keeps its importance for identifying the physiological, and the psychological situations of the human and the functional activity of the brain. Being a non stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. Epilepsy is o...

A plausible model of the statistical mechanics of neocortical interactions (SMNI), spans several important neuroscientific phenomena including phenomena measured by electroencephalography (EEG) (Ingber, 1982; Ingber, 1983; Ingber, 1984; Ingber, 1985a; Ingber, 1985b; Ingber, 1991; Ingber, 1992; Ingber, 1994; Ingber, 1995a; Ingber and Nunez, 1990). Fitted SMNI functional forms to EEG data may hel...

PURPOSE To describe the development and implementation of video EEG telemetry (VT) in the patient's home (home video telemetry, HVT) in a single centre. METHODS HVT met the UK Medical Research Council definition of a complex intervention, and we used its guidance to evaluate the process of piloting, evaluating, developing and implementing this new clinical service. The first phase was a feasi...

Frequency-transformed EEG resting data has been widely used to describe normal and abnormal brain functional states as function of the spectral power in different frequency bands. This has yielded a series of clinically relevant findings. However, by transforming the EEG into the frequency domain, the initially excellent time resolution of time-domain EEG is lost. The topographic time-frequency...