Unsupervised Seizure Detection using a Convolutional Autoencoder

This project trains a convolutional autoencoder for anomaly detection using publicly available interictal iEEG data. The end goal is to develop a channel-by-channel seizure detector, allowing us to analyse the onset and spread of a seizure. Code is available on Github.