Generative Adversarial Network Based Semi-supervised Learning for Epileptic Focus Localization

Accurate localization of the epileptic focus plays an important role in the success of epileptic surgery. Epileptic focus localization process helps the physicians to determine the epileptogenic source in the brain by classifying the intracranial Electroencephalogram (iEEG) recordings into focal and non-focal. Using machine learning to automatically localize the epileptic focus requires a large number of labeled EEG recordings to effectively tackle such challenging classification problem. However, obtaining that much of labeled EEG data is expensive and time consuming. In order to accurately automate the focus localization task while having limited number of labeled EEG recordings, we introduce a semi-supervised learning method based on convolutional generative adversarial network. The proposed method is able to accurately classify the EEG signals into focal and non-focal using small amount of labeled training data which helps in reducing the burden of EEG data annotations. Discriminative spatial features are automatically extracted and classified by convolutional layers from raw EEG data without any preprocessing rather than manual feature extraction in the previous work. High classification accuracy of 95.5% makes the proposed method the most efficient among the state of the art.