Deep Learning For Epileptic Intracranial EEG Data
Andreas Antoniades, University of Surrey
Loukianos Spyrou, University of Surrey
Clive Cheong Took, University of Surrey
Saeid Sanei, University of Surrey

Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal"s specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. In this paper, we consider deep learning for automatic feature generation from epileptic intracranial EEG data in the time domain. Specifically, we consider convolutional neural networks (CNNs) in a subject independent fashion and demonstrate that meaningful features, representing IEDs are automatically learned. The resulting model achieves state-of-the-art classification performance, provides insights for the different types of IEDs within the group, and is invariant to time differences between the IEDs. This study suggests that automatic feature generation via deep learning is suitable for IEDs and EEG in general.