Abstract Various forms of artifacts can readily contaminate an electroencephalogram recorded using surface electrodes. A comparison several (EEG) de-noising methods is shown here. Five distinct noise are reduced three different strategies, and the results compared. These procedures Recursive Least Squares (RLS) adaptive algorithm, Mean (LMS) method, Fully Connected Neural Network (FCNN). The ti...

The principal challenges involved with recording and analyzing surface electroencephalography (EEG) are presented in a way that is clear for the nontechnical reader. The influence of interpatient variability, signal acquisition techniques, and general effects of digital signal processing are described. A signal-processing example using surface EEG data is presented, and recommendations are prop...

Electroencephalogram (EEG) registration as a direct measure of brain activity has unique potentials. It is one of the most reliable and predicative indicators when studying human cognition, evaluating a subject's health condition, or monitoring their mental state. Unfortunately, standard signal acquisition procedures limit the usability of EEG devices and narrow their application outside the la...

in recent decades, seizure prediction caused a lot of research in both signal processing and neuroscience field. the researches tried to enhance the conventional seizure prediction algorithms such that rate of the false alarms be appropriately small so that seizures can be predicted according to clinical standards. up to now none of the proposed algorithms have been sufficiently adequate. in th...

A novel approach is proposed for Electroencephalogram signal classification using Artificial Neural Network based on Independent Component Analysis and Short Time Fourier Transform. The source EEG signals contain the electrical activity of the brain produced in the background by the cerebral cortex nerve cells. EEG is one of the most utilized methods for effective analysis of the brain function...

Texture discontinuities are a fundamental cue by which the visual system segments objects from their background. The neural mechanisms supporting texture-based segmentation are therefore critical to visual perception and cognition. In the present experiment we employ an EEG source-imaging approach in order to study the time course of texture-based segmentation in the human brain. Visual Evoked ...

Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface(BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals on-line. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ...

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Artifacts in EEG signals are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The removal of artifact from scalp EEGs is of considerable importance for analysis of underlying brainwave activity. The presence of artifacts such as muscl...

Electroencephalography (EEG) signals reƒect activities on certain brain areas. E‚ective classi€cation of time-varying EEG signals is still challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge. In addition, most existing studies focus on domain-speci€c classi€cation algorithms which may not be applicable to other domains. Moreov...