Document Type
Thesis
Date of Award
5-31-1993
Degree Name
Master of Science in Computer Science - (M.S.)
Department
Computer and Information Science
First Advisor
Mark A. Gluck
Second Advisor
Catherine E. Myers
Third Advisor
Peter A. Ng
Abstract
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 training data. The predictive autoencoder network trained with less epochs and appeared more resistant to overtraining. However, performance of the predictive autoencoder network may vary if it is stopped before it has trained for a sufficient number of epochs.
Recommended Citation
Armieri, Brian, "Classification of patterns in EEG recordings : a comparison of back-propagation networks vs. predictive autoencoder networks" (1993). Theses. 1720.
https://digitalcommons.njit.edu/theses/1720
