Week 3: Pivoting to Intracranial Seizure Data with Complex-Valued Convolutional Neural Networks

Finding a sizable amount of interferometric data has been proven difficult. Reflecting on the scope and direction of this research, I have concluded that improving interferometric systems does not align with my current research interests. Thus, much of this week was spent finding alternative applications of CVNNs that leverage more accessible forms of data. 

One of the first ways I realized I could use CVNNs was for epilepsy prediction. Deep learning has been used in many ways in the last decade for epilepsy prediction, yet nearly no studies have been done using CVNNs for epilepsy prediction. 

With the help of a research connection, I was recently able to receive six hours of intracranial electroencephalogram (EEG) recording from four epileptic mice, sampled at 2000 Hz. The data displays dynamic transitions into and out of seizures, and contains seven seizures in total. 

Besides looking at data, I spent time this week researching more into different forms of CVNNs. Most likely, I will be using CV-CNNs (complex-valued convolutional neural networks). Like the name suggests, a CV-CNN is a complex-valued form of a convolutional neural network (CNN). A CNN is a type of neural network designed to detect spatial patterns and features in data through the use of filters that are learned. CV-CNNs extend this idea to the complex domain, replacing weights and activations with their complex-valued counterparts. Since CNNs have been used in previous scientific literature for waveform analysis, using its complex form would be logical and straightforward.