INTRODUCTION The spatial patterns of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals generated by source currents in different parts of the brain typically overlap extensively. For simultaneous sources, overlapping patterns result in reduced resolution in inverse estimates of the underlying source distributions. In particular, cancellation due to superposition of fields ...

EEG is a collaborative tool to diagnosis brain function and disease. Modern interpretation of EEG origin rest with knowledge of basic neuronal electrochemical process. The EEG is composed of electrical rhythms and transient discharges which are distinguished by location, frequency, amplitude, form, periodicity and functional properties. The low-level EEG Signal from the brain as recorded is amp...

Many biological signal analyses have been proceeded in various research fields. In particular, electroencephalographic (EEG) signal analyses have attracted attention because the EEG signal includes a mixture of endogenous brain activities. However, the EEG signal is often mixed physiological artifacts such as the eye blinking, oculogyration, heart beat, or muscle activity. Specifically, humans ...

Measuring the depth of anesthesia using electroencephalogram (EEG) is an important and challenging task. Although various methods using EEG have been proposed, these algorithms quantify the depth of anesthesia without any physiological models. In this paper, a method to quantify the depth of anesthesia as well as a signal model that describes the changes in EEG during anesthesia is presented. T...

An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or emit message at the moment user desires by decoding EEG signals of speech. In order correctly implement these types BCI, we must be able detect from continuous signal, when subject starts imagine words. this work, five methods feature extraction wavelet decomposition, ...

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of n...

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 ...