Physical Sciences and Mathematics Commons^™

Classification Of Patterns In Eeg Recordings : A Comparison Of Back-Propagation Networks Vs. Predictive Autoencoder Networks, Brian Armieri

Theses

Recent research exploring the use of neural networks for electro-encephalogram (EEG) pattern classification has found that a three-layer back-propagation network could be successfully trained to identify high voltage spike-and-wave spindle (HVS) patterns caused by epileptic seizures (Jando et. al., in press). However, there is no reason to predict that back-propagation is the best possible network architecture for EEG classification. A back-propagation neural network and a predictive autoencoder neural network were compared to determine which network was better at correct classifying both HVS and non-HVS patterns.

Both networks were able to classify 88%-89% of all patterns using a limited set of …