Automated focal EEG signal detection based on third order cumulant function Abstract   Epilepsy is a chronic neurological disorder which occurs due to recurrent seizures. The epilepsy surgery is the only cure of epileptic seizures as it cannot be controlled with medication. Hence, it becomes the primary task to localize the epileptogenic zone for successful epilepsy surgery. The epileptic surgical area can be recognized by the focal intracranial electroencephalogram (EEG) signals. In this paper, a nonlinear thirdorder cumulant has been proposed for the classification of the nonfocal and focal intracranial EEG signals efficiently. The attributes are measured from the logarithm of the diagonal slice of thirdorder cumulant. It provides relevant subtle information about the nonlinear dynamics of EEG signals. A data reduction technique, locality sensitive discriminant analysis (LSDA), has been introduced to map the measured features at higher dimensional space and ranked them according to the probability of discrimination. The achieved results reveal that the ranked LSDA features with the support vector machine (SVM) classifier have yielded maximum classification accuracy of 99% on the Bern Barcelona EEG database. Thus, the proposed algorithm helps a clinician to localize the epileptogenic zone for successful brain surgery.

Classification of Epileptic Activity Through Temporal and Spatial Characterization of Intracranial Recordings Focal epilepsy is a chronic condition characterized by hyperactivity and abnormal synchronization of a specific brain region. For pharmacoresistant patients, the surgical resection of the critical area is considered a valid clinical solution, therefore, an accurate localization is crucial to minimize neurological damage. In current clinical routine the characterization of the Epileptogenic Zone (EZ) is performed using invasive methods, such as StereoElectroEncephaloGraphy (SEEG). Medical experts perform the tag of neural electrophysiological recordings by visually inspecting the acquired data, a highly time consuming and subjective procedure. Here we show the results of an automatic multimodal classification method for the evaluation of critical areas in focal epileptic patients. The proposed method represents an attempt in the characterization of brain areas which integrates the anatomical information on neural tissue, inferred using Magnetic Resonance Imaging (MRI) in combination with spectral features extracted from SEEG recordings.