Dantanarayana, Navini, authorAnderson, Charles, advisorBen-Hur, Asa, committee memberDavies, Patricia, committee member2007-01-032007-01-032014http://hdl.handle.net/10217/88516Generative Topographic Mapping (GTM) assumes that the features of high dimensional data can be described by a few variables (usually 1 or 2). Based on this assumption, the GTM trains unsupervised on the high dimensional data to find these variables from which the features can be generated. The variables can be used to represent and visualize the original data on a low dimensional space. Here, we have applied the GTM algorithm on Electroencephalography (EEG) signals in order to find a two dimensional representation for them. The 2-D representation can also be used to classify the EEG signals with P300 waves, an Event Related Potential (ERP) that occurs when the subject identifies a rare but expected stimulus. Furthermore, unsupervised feature learning capability of the GTM algorithm is investigated by providing EEG signals of different subjects and protocols. The results indicate that the algorithm successfully captures the feature variations in the data when generating the 2-D representation, therefore can be efficiently used as a powerful data visualization and analysis tool.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.dimensionality reductiongenerative topographic mappingelectroencephalographyText