The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is to develop a new method for automatic detection of EEG patterns using ...

A Brain Computer Interface (BCI) system takes and classifies a user’s brain activity into a signal to which a computer can respond. To control a BCI, the user should produce various brain activity patterns which are captured in form of Electroencephalogram (EEG) and converted to commands by identifying the patterns by the system. Such classification was undertaken by various methods, and perfor...

Psychogenic nonepileptic seizures (PNESs) are diagnosed when disruptive changes in behaviour, thinking, or emotion resemble epileptic seizures (ESs), but no paroxysmal discharges are seen on electroencephalogram (EEG) and do not originate from another medical illness. The gold standard for PNES diagnosis is video electroencephalogram (Video-EEG). PNESs are defined by modern psychiatry as conver...

This paper discusses a wavelet based, lossless compression scheme for Electroencephalogram(EEG) signals. For any wavelet based compression scheme, selection of wavelet base is very important task for obtaining effective signal compression. Here, emphasis is given for selection of wavelet basis and number of decomposition levels. The proposed EEG compression algorithm comprises of a preprocessin...

This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable b...

Developmental research is enhanced by use of multiple methodologies for examining psychological processes. The electroencephalogram (EEG) is an efficient and relatively inexpensive method for the study of developmental changes in brain-behavior relations. In this review, we highlight some of the challenges for using EEG in cognitive development research. We also list best practices for incorpor...

Wavelets and wavelet transforms (WT) could be a very useful tool to analyze electroencephalogram (EEG) signals. To illustrate the WT method we make use of a simple electric circuit model introduced by Niederhauser [1], which is used to produce EEG-like signals, particularly during an epileptic seizure. The original model is modified to resemble the 10-20 derivation of the EEG measurements. WT i...

This topic discusses the use of EEG in the diagnosis of seizures and epilepsy. A more general discussion of EEG, and the use of other diagnostic tests in the evaluation of patients with seizures and epilepsy are presented separately. (See "Clinical neurophysiology", section on 'Electroencephalogram' and "Video and ambulatory EEG monitoring in the diagnosis of seizures and epilepsy" and "Evaluat...

In this paper human normal and epileptic Electroencephalogram signals are analyzed with popular and efficient signal processing techniques like Fourier and Wavelet transform. The delta, theta, alpha, beta and gamma sub bands of EEG are obtained and studied for detection of seizure and epilepsy. The extracted feature is then applied to ANN for classification of the EEG signals. Keywords—EEG, Epi...

We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes, the features were reduced according the Fisher distance. This reduced feature set is finally fed to a linear discriminant for classification....