Several methods have been suggested to predict the onset of epileptic seizures from EEG data. We evaluated the performance of three predictions methods: the “dynamical similarity index”, the “effective correlation dimension” and an extended, prospective version of the “accumulated energy”. These prediction methods were applied on a large pool of intracranial EEG data from 21 patients. Altogethe...

A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/ deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall predicti...

We present in this study a novel approach to predicting EEG epileptic seizures: we accurately model and predict non-ictal cortical activity and use prediction errors as parameters that significantly distinguish ictal from non-ictal activity. We suppress seizure-related activity by modeling EEG signal acquisition as a cocktail party problem and obtaining seizure-related activity using Independen...

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...