Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well nonmotor symptoms such anxiety depression. Biofeedback improves functions of patients regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or othe...

OF THESIS AGING AND SLEEP STAGE EFFECTS ON ENTROPY OF ELECTROENCEPHALOGRAM SIGNALS The aging brain is characterized by alteration in synaptic contacts, which leads to decline of motor and cognitive functions. These changes are reflected in the age related shifts in power spectrum of electroencephalogram (EEG) signals in both wakefulness and sleep. Various non-linear measures have been used to o...

The aim of this paper is to develop the classification system using Artificial Neural Network for Electroencephalogram (EEG) signals. A good standard traditional method is to use Electroencephalogram for diagnosing patients brain functioning that corresponds to epilepsy and different brain disorders. This research focused on designing new classification techniques for single channel EEG recordi...

Sleep disorders affect an individual’s ability to sleep well on a regular and natural basis. Inadequate can have adverse outcomes for health safety. Electroencephalogram (EEG) has been presented as authentic indicator monitor brain activities. In this review paper, different procedures of EEG tests recording monitoring activity during such the electrodes system Dreem headband (DH) introduced. A...

In this paper we proposed a technique to remove eye blink artifact from electroencephalogram (EEG) using lifting wavelet transform (LWT). The LWT has been successfully used in eye blink artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The LWT is applied to decompose EEG signal into a finite set of subbands. The energy ...

The electroencephalogram (EEG) is useful for clinical diagnosis and in biomedical research. EEG recordings are distorted by electrooculogram (EOG) artifacts causing serious problem for EEG interpretation and analysis. An often preferable method is to apply Independent Component Analysis (ICA) to multichannel EEG recordings and remove a wide variety of artifacts from EEG recordings by eliminatin...

The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities. The detection of epileptic seizures based on EEG signal is very useful in diagnostics. In this paper, we present a new method for discrimination between seizure and seizure-free EEG signals. The proposed method is based on empirical mode decomposition (EMD) process. We investigated that th...

Cardiac arrhythmias occurring in association with epileptic seizures are a potential source of diagnostic confusion and a possible cause of sudden unexpected death in epilepsy. A case is described in which simultaneous ambulatory electroencephalography and electrocardiography revealed periods of asystole coinciding with epileptic seizures. The aystole appeared to precede obvious changes in the ...

A case of 50 years of age, male with sporadic Creutzfeldt Jakob Disease (sCJD) is reported. Patient had dementia, behavioural abnormalities, unsteady gait and myoclonic jerks. Magnetic resonance imaging (MRI) brain T2 weighted and Fluid Attenuated Inverse Recovery (FLAIR) images showed abnormally increased signal intensity in caudate nucleus and putamen. Scalp electroencephalogram (EEG) reveale...

We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier re...