In Electroencephalography (EEG) processing one of the crucial step is to select the features which can be used to characterize the different patterns. This paper presents relative wavelet energy as a new feature extraction technique to recognize the sleep EEG patterns. Experimental results on the Physionet databases with different sleep stages indicate that the relative wavelet energy is a comp...

The electrical activity of the brain can be monitored using ElectroEncephaloGraphy (EEG). From the positions of the EEG electrodes, it is possible to localize focal brain activity. Thereby, the accuracy of the localization strongly depends on the accuracy with which the positions of the electrodes can be determined. In this work, we present an automatic, simple, and accurate scheme that detects...

Background: More than 80% of neonatal seizures are completely subclinical and represent a risk factor for neurodevelopmental delays in preterm infants. Amplitude integrated electroencephalography combined with raw video images (video aEEG/EEG) provides real-time monitoring seizure detection.

There are several techniques for measuring brain activities such as magnetoencephalogram (MEG), near infrared spectroscopy (NIRS), electrocorticogram (ECoG), functional magnetic resonance imaging (fMRI), and electroencephalography (EEG). Each technique has some advantages and disadvantages compared to other techniques. For example, in EEG the temporal resolution is high but the special resoluti...

An audit of 165 requests for electroencephalography (EEG) was undertaken before and after the introduction of guidelines and recommendations, 12 months apart. Inadequate clinical information was provided in requests in both surveys; 40% of requests were considered to be unnecessary, and approximately 50% of clinicians felt that EEG could diagnose epilepsy.

This paper presents classification methods for electroencephalography (EEG) signals in imagination of direction measured by a portable EEG headset. In the authors’ previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE) for the clas...

Knowledge tracing (KT) is widely used in Intelligent Tutoring Systems (ITS) to measure student learning. Inexpensive portable electroencephalography (EEG) devices are viable as a way to help detect a number of student mental states relevant to learning, e.g. engagement or attention. This paper reports a first attempt to improve KT estimates of the student’s hidden knowledge state by adding EEG-...

We focus on the Forward Problem of electroencephalography, discuss a mathematical model and state properties of its weak solutions. A static and a timedependent model for the source are considered. Numerical solutions, obtained by a Boundary Element Method technique, are compared with the analytical ones and with EEG recordings. Keywords– EEG, Forward Problem, Epilepsy, Modeling

Sixty-five patients undergoing cardiac valve replacement were followed for one year by electroencephalography (EEG). Occurrence of delta or sharp wave disturbances of low frequency of dominant activity before operation was found to have prognostic significance. The degree of EEG change after operation correlated with clinical signs of cerebral involvement, and predicted the later course.