Electroencephalographic (EEG) signal records the electrical activity of the neurons near the scalp within the brain. Significant artifacts are introduced in the recording of EEG signals which leads to unreliable results. EEG paves the way for diagnosis of many neurological disorders and other abnormalities in the human body. The main aim of research is to get clean EEG signal with enhanced accu...

Brain–Computer Interfaces (BCIs) provide a nonmuscular channel to communicate with the outside world by means of brain activity. A crucial step for efficient BCI operation is brain signal processing methods. Most BCI systems for humans use scalp recorded electroencephalographic activity, whereas Electrocorticography (ECoG) is a minimally-invasive alternative to electroencephalogram (EEG), provi...

Analysis of amplitude and phase characteristics for delta, theta, and alpha bands at localized time instant from EEG signals is important for the characterizing information processing in the brain. In this paper, complex demodulation method was used to analyze EEG (Electroencephalographic) signal, particularly for auditory evoked potential response signal, with sufficient time resolution and de...

Analysis of amplitude and phase characteristics for delta, theta, and alpha bands at localized time instant from EEG signals is important for the characterizing information processing in the brain. In this paper, complex demodulation method was used to analyze EEG (Electroencephalographic) signal, particularly for auditory evoked potential response signal, with sufficient time resolution and de...

The distributed nature of nervous systems makes it necessary to record from a large number of sites in order to decipher the neural code, whether single cell, local field potential (LFP), micro-electrocorticograms (μECoG), electroencephalographic (EEG), magnetoencephalographic (MEG) or in vitro micro-electrode array (MEA) data are considered. High channel-count recordings also optimize the yiel...

This work reports the use of neural networks for pattern recognition in electroencephalographic signals related to intermittent photic-stimulation. Due to the low signal/noise ratio of this kind of signal, it was necessary the use of a spectrogram as a predictor and a chain of LVQ neural networks. The efficiency of this pattern recognition structure was tested for many different configurations ...

The lossless compression of electroencephalographic (EEG) data is of great interest to the biomedical research community. In this paper, a two-stage technique of lossless compression involving decorrelating the sample points of the EEG signal and then entropy coding the resulting signal is examined. Two alternatives are presented for performing the rst task. Speciically, the rst stage consists ...

We report transient focal abnormalities on MR in a patient having frequent electrographic seizures that were not obvious clinically. Marked mass effect (confirmed with volumetric studies) and abnormal T2 signal intensity in the right hippocampal region correlated with electroencephalographic ictal activity and with increased positron-emitting radiotracer uptake in the medial temporal lobe. The ...

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive revie...

Based on the analysis delayed ischemia after subarachnoid hemorrhage indicators, a new approach to of data from long-term electroencephalographic patients monitoring is proposed and described. It based wavelet transforms ridges, in which power spectrum density, frequency phase, are equal square amplitude, phase signal respectively. The main formulas justifying this given. Illustrations use Morl...