One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on huma...

D ysfunction in the central nervous system of the neonate is often rst identi ed through seizures. The di culty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerabl...

0.1016/j.dcan.201 hongqing Universi thor. : [email protected] responsibility of Abstract Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment o...

Wavelet transform provides a fine means of classifying seizure EEG signals from the normal EEG signals. Stationary wavelet transform (SWT) is used to further improve the performance of discrete wavelet transform. EEG signal prediction and classification can be bolstered up by applying SWT. In this work the residues obtained from denoising the signal using SWT is considered. Its arithmetical fac...

The paper presents an analysis of EEG signal processing methods for studying correlations between human muscle and brain activity. The main task of this work is to design the methods of EEG signal processing and to verify them on artificial and real signals. The paper introduces methods of EEG processing in time and frequency domain.

Introduction: Simultaneous EEG and fMRI recordings (EEG-fMRI) can detect haemodynamic changes associated with spontaneous events observed on the EEG, such as interictal epileptiform discharges (“spikes”). Event-related analyses of these studies typically assume a canonical HRF model and define the start of the event as the onset of EEG changes. There have been two recent reports, however, of BO...

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are now being combined to analyze brain function. Confounding the EEG signal acquired in the MR environment is a ballistocardiogram artifact (BA), which is predominantly caused by cardiac-related body movement. The objective of this study was to develop and evaluate a method for reducing these MR-induced artifacts to ...

UNLABELLED BACKGROUND We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). METHODS The microEEG was used to obtain EEGs from healthy...

We derive an asymptotic Newton algorithm for Quasi Maximum Likelihood estimation of the ICA mixture model, using the ordinary gradient and Hessian. The probabilistic mixture framework can accommodate non-stationary environments and arbitrary source densities. We prove asymptotic stability when the source models match the true sources. An application to EEG segmentation is given. Index Terms Ind...

An Electroencephalogram (EEG) is produced as a consequence of the electrical voltage neurons in brain. The EEG signal crucial for detecting brain activity and attitude. Because this has very low amplitude, it easily corrupted by different artefacts. study analysis signals presence these artifacts challenging task. ECG, EOG, EMG, motion are popular that induce disturbance to signal. This survey ...