Most of the current epileptic seizure prediction algorithms require much prior knowledge of a patient’s pre-seizure electroencephalogram (EEG) patterns. They are impractical to be applied to a wide range of patients due to a very high inter-individual variability of EEG patterns. This paper proposes an adaptive prediction framework, which is capable of accumulating knowledge of pre-seizure EEG ...

A number of natural time series including electroencephalogram (EEG) show highly non-stationary characteristics in their behavior. We analyzed the EEG in sleep apnea that typically exhibits non-stationarity and long-range correlations by calculating its scaling exponents. Scaling exponents of the EEG dynamics are obtained by analyzing its fluctuation with detrended fluctuation analysis (DFA), w...

Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analysed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose th...

This publication aims at developing computer based learning environments adapting to learners’ individual cognitive condition. The adaptive mechanism, based on Brain-Computer-Interface (BCI) methodology, relays on electroencephalogram (EEG)-data to diagnose learners’ mental states. A first within-subjects study (10 students) was accomplished aiming at differentiating between states of learning ...

Abstract— The BCI research has now witnessed incredible expansion using invasive and noninvasive methods but especially the use of Electroencephalogram (EEG) signals (method of non-invasive BCI) has attained a lot of significance. The applications of EEG based BCI ranges from medicine to entertainment. In this paper, a general Electro-Encephalogram (EEG) based BCI system is discussed. Thus deve...

A novel approach is proposed for Electroencephalogram signal classification using Artificial Neural Network based on Independent Component Analysis and Short Time Fourier Transform. The source EEG signals contain the electrical activity of the brain produced in the background by the cerebral cortex nerve cells. EEG is one of the most utilized methods for effective analysis of the brain function...

INTRODUCTION Neuroglycopenia in type 1 diabetes mellitus (T1DM) results in reduced cognition, unconsciousness, seizures, and possible death. Characteristic changes in the electroencephalogram (EEG) can be detected even in the initial stages. This may constitute a basis for a hypoglycemia alarm device. The aim of the present study was to explore the characteristics of the EEG differentiating nor...

brain-computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing eeg signals measured in different mental states.  therefore, choosing suitable features is demanded for a good bci communication. in this regard, one of the points to be considered is feature vector dimensionality. we present a method of feature reduction us...

BACKGROUND Electroencephalogram (EEG) acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a multivariable diagnostic prediction model based on electroencephalogram functional network characteristics....