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Zafer Doğan - Classification of EEG microstates among PNES patients before and after nonepileptic seizures using machine learning methods


The Psychogenic Nonepileptic Seizures (PNES) are attacks that may look like epileptic seizures but are not epileptic and instead are cause by psychological factors. The only reliable test to positively make the diagnosis of PNES is video electroencephalography (vEEG) monitoring, which can be very long, and time consuming. Instead, in this work, we focus on the analyzing the changes observed in EEG signal before and after the seizures among the PNES patients. In EEG, microstates emerge as distributions of activity across the scalp that persist for several tens of milliseconds before changing into a different pattern. Hence, microstate analysis will be used as a way of utilizing EEG as both temporal and spatial imaging tool. Finally, the EEG microstates of PNES patients will be classified into groups representing different time information of the seizures.