The correlation between signals acquired using electroencephalography (EEG) and fMRI was investigated in humans during visual stimulation. Evoked potential EEG and BOLD fMRI data were acquired independently under similar conditions from eight subjects during stimulation by a checkerboard flashed at frequencies ranging from 2-12 Hz. The results indicate highly correlated changes in the strength ...

An Electroencephalogram (EEG) is the recording of electrical activity from the surface of the brain. It is proved that there is a significant amount of individuality in the EEG signals, accordingly its can be treated as a biometric feature for an individual and used for human identification. In this paper, we present an artificial immune system for identifying users using EEG signals; we conduc...

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artif...

Extracellular electric potentials, such as local field potentials (LFPs) or the elec-troencephalogram (EEG), are routinely measured in electrophysiological experiments. LFPs are recorded using micrometer-size electrodes, and sample relatively localized populations of neurons, as these signals can be very different for electrodes separated by 1 mm (Destexhe et al., 1999a) or by a few hundred mic...

Introduction Many features, emerging from mathematical techniques, have been used in the analysis of brain signals. In this study, the physical quantity of “moment of inertia” (MOI) was introduced as a feature to enhance high-frequency waves (HFWs) in electroencephalography (EEG). Materials and Methods In this research, the recorded EEGs from F3, F4, and Cz points in 20 males were used. A total...

Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool which makes processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. In this paper, a hybrid approach based on deep features extracted from Wave...

Electroencephalographic (EEG) recordings are often contaminated with several artifacts.Powerline interference and baseline noise is always present in EEG response of every patient. A number of strategies are available to deal with noise effectively both at the time of EEG recording as well as during preprocessing of recorded data. The aim of the paper is to give an overview of the most common s...

In this paper, a novel method for EEG(Electroencephalography) based emotion recognition is introduced. This uses transfer learning to extract features from multichannel EEG signals, these are then arranged in an 8×9 map represent their spatial location on scalp and we introduce CNN model which takes the feature extracts relations between channel finally classify emotions. First, signals convert...

The present study seeks to investigate the limits of scalp EEG (electroencephalogram) SSVEP (Steady State Visual Evoked Potentials) phenomena (to which maximal and minimal frequencies SSVEP can be recorded at the scalp level). SSVEP are periodic evoked signals buried in the non-stationary waves of EEG recordings. EEG signals are furthermore noisy and contain artifacts which may interfere with b...

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 ...