Week 4: Understanding EEG Data and Deep Learning

Much of this week was dedicated to developing a stronger understanding of how deep learning (especially convolutional neural networks) are used for seizure prediction. Understanding this will not only allow me to gain a better understanding of how I can code my complex-valued convolutional neural network (CV-CNN) in my project, but also get a baseline of how good current models are at predicting seizures. 

One of the most central concepts I encountered throughout the literature is the classification of EEG states into preictal and interictal phases. The ictal phase refers to the actual seizure event, where abnormal electrical activity in the brain causes convulsions, loss of consciousness, or sensory dysfunction. Thus, preictal and interictal respectively refer to the stage before the seizure, lasting minutes to hours, and the stable period between seizures which make up most of a person’s life. The goal of seizure prediction then is to correctly identify the preictal state and not get it confused with the interictal state.

In reviewing the literature of CNN approaches to seizure prediction, I also found that the use of one-dimensional kernels (filters) was almost universally used. In CNN applications to image processing, kernels are applied across height and width, making it two-dimensional. EEG data is different as it runs across a temporal domain. Thus, a 1D kernel is applied to detect changes in amplitude over time. This concept will be useful in my application of CV-CNNs for seizure prediction.