The extraction of epileptic seizure waveform from the electroencephalogram (EEG) using Independent Component Analysis (ICA) was demonstrated by James and Lowe. A recent variation, Constrained ICA, allows a supplied reference signal to select a single component to be extracted. We show how this algorithm can be applied to the problem of seizure waveform extraction from EEG signals prior to the v...

One of the main areas of behavioural neuroscience is forecasting the human behaviour. Epilepsy is a central nervous system disorder in which nerve cell activity in the brain becomes disrupted, causing seizures or periods of unusual behaviour, sensations and sometimes loss of consciousness. An estimated 5% of the world population has epileptic seizure but there is not any method to cure it. More...

PURPOSE To evaluate the value of magnetoencephalography (MEG) source localization in localization of epileptic activities and predicting surgical outcome in frontal lobe epilepsies (FLE). METHODS Forty-six patients with presurgical MEG evaluation and intractable FLE surgery (28 male patients) were analyzed retrospectively with a mean follow-up of 5 years. Dipole analysis was performed for MEG...

The topic of this presentation is the investigation of the epileptic human brain as a nonlinear system that undergoes a phase transition (epileptic seizure). The estimated values of the largest Lyapunov exponent L over time indicated a more chaotic state postictally than ictally or preictally. The start of a seizure corresponds to a simultaneous drop in the values of L at the focal electrode si...

In this study, the wavelet-based fractal analysis is applied to analyze epileptic ECoG data obtained during nonseizure period and epileptic seizure events. The spectral exponents of the epileptic ECoG data obtained using the waveletbased fractal analysis from various intervals of levels are examined. The computational results show that the estimated spectral exponents of the epileptic ECoG epoc...

A reliable algorithm for the timely prediction of epileptic seizures would be a milestone in epilepsy research. Prediction performances have so far been determined using retrospective data assessment, leaving open the question as to whether they prove statistically significant and clinically useful under prospective conditions. To this aim, a Seizure Prediction Competition has been set up. Here...

A rapidly growing number of studies deals with the prediction of epileptic seizures. For this purpose, various techniques derived from linear and nonlinear time series analysis have been applied to the electroencephalogram of epilepsy patients. In none of these works, however, the performance of the seizure prediction statistics is tested against a null hypothesis, an otherwise ubiquitous conce...

Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. To this end, many computational methods have been developed for both the detection and prediction of epileptic seizures. Techniques derived from li...

Prediction of EEG signal from past samples is needed for early diagnosis of patients, suffering from frequent epileptic seizure and/or psychotherapeutic treatment of psychiatric patients. This paper compares the performance of various digital filter algorithms to identify the right candidate for application in EEG prediction. The study includes variation of filter order and past sample size, an...