In this paper, recent applications of autoregressive (AR) and adaptive autoregressive (TVAR) models to EEG signals for detection of epileptic seizures are addressed. First of all, AR/TVAR models based the complexity measure with the order of AR model and the spectrum estimation with online AR model are introduced and employed to analyze the EEG signals with epileptic seizures. Then, three new a...

Electroencephalogram (EEG) blend reflects the summation of the synchronous activity of thousands or millions of neurons that have parallel spatial direction. EEG Signals are extracted from Human Brain. While Extracting an EEG Signals, large amount of data with diverse categories will be collected from the human skull. To investigate and categorize the valuable information from the EEG recording...

within the neuroscience and computational neuroscience communities, applications such as evaluating different cognitive responses of the brain, brain-computer interface (bci) systems and brain connectivity studies have increasingly been using eeg phase information during the past few decades. the utility of eeg phase can be directly linked to the neural propagation and synchronized firing of ne...

Background: Ensuring adequate depth of anesthesia during surgery is essential for anesthesiologists to prevent the occurrence of unwanted alertness during surgery or failure to return to consciousness. Since the purpose of using anesthetics is to affect the central nervous system, brain signal processing such as electroencephalography (EEG) can be used to predict different levels of anesthesia....

localization of sources in patients with focal seizure has recently attracted many attentions. in the severe cases of focal seizure, there is a possibility of doing neurosurgery operation to remove the defected tissue. the prosperity of this heavy operation completely depends on the accuracy of source localization. to increase this accuracy, this paper presents a new weighted beamforming method...

Background: Like the brain, muscles also generate electrical signals. These signals are picked up by EEG electrodes and become muscle artifacts in EEG recording. MA is looking like fast oscillations. EEG signals disturbed by MA have larger amplitudes than normal EEG signals. Human beings have a large number of muscles all over their bodies. The muscle movement that happens near electrodes, such...

This paper focuses on the development of an automatic system for sleep analysis. The system proposed in this paper combines two phases needed in sleep analysis. In a first step, an artefact detection system selects the polysomnographic signals (EEG, EOG, EMG) that are not corrupted by artefacts. In a second step, relevant features are extracted from the selected signals and classified using a n...

The electroencephalogram (EEG) signal plays a vital role in the detection of various types of neural disorders such as autism spectrum disorder, epilepsy, Alzheimer’s, etc. Since EEG signals are lengthy and it contains a vast amount of useful data and artifacts analysing these signals by an expert using traditional methods are monotonous and time consuming. Recent years, many automatic diagnost...

Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder.Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has b...

Seizure activity takes place due to an irregular excessive electrical action in the human brain. The electrical activity in the form of brain waves (signals) can measured by using the device called Electroencephalogram (EEG). In this paper, we have reviewed our work so far made regarding EEG signals. Comparative between Artificial Neural Network (ANN) and Support Vector Machine (SVM) this revie...