Mapping Scalp to Intracranial EEG using Generative Adversarial Networks for Automatically Detecting Interictal Epileptiform Discharges

Both scalp and intracranial electroencephalograms (EEGs) are of great importance for diagnosing brain disorders. However, the scalp EEG (sEEG) is attenuated by the skull and contaminated with artifacts. At the same time, intracranial EEG (iEEG) is almost free of artifacts and can capture all brain activities without any attenuation due to being close to the brain sources. In this study, the aim is to enhance the performance of sEEG by mapping the sEEG to the iEEG. To do so, we here develop a deep neural network using a generative adversarial network to estimate the sEEG from the iEEG. The proposed method is applied to sEEG and iEEG recorded simultaneously from epileptics to detect interictal epileptiform discharges (IEDs). The proposed method detects IEDs with 76% accuracy outperforming the state-of-the-art methods. Furthermore, it is at least twelve times less complex than the compared methods.