Automatic Identification of Epileptic Focus on HighFrequency Components in Interictal iEEG The localization of the seizure focus affected by epilepsy is crucial for epilepsy treatment due to observing longterm interictal intracranial electroencephalogram (iEEG) for categorizing the patterns of seizures by the neurological experts. Therefore, a computeraided system based on machine learning method for automatic localization of focal patterns is promising future. In this study, we presents a filterbank entropybased featureextraction approach in highfrequency components to detect epileptic focus, which consider a valid biomarkers to guide epilepsy surgery. The experimental results on realworld interictal iEEG recorded from eight patients demonstrate that our proposed method can achieve average AUC 0.79, which can reduce the workload of clinical experts for detection of epileptic focal.

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.