Repository logo
 

Generative topographic mapping of electroencephalography (EEG) data

Simple item page
dc.contributor.authorDantanarayana, Navini, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorBen-Hur, Asa, committee member
dc.contributor.authorDavies, Patricia, committee member
dc.date.accessioned2007-01-03T05:58:36Z
dc.date.available2007-01-03T05:58:36Z
dc.date.issued2014
dc.description.abstractGenerative 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.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierDantanarayana_colostate_0053N_12792.pdf
dc.identifier.urihttp://hdl.handle.net/10217/88516
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright 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.
dc.subjectdimensionality reduction
dc.subjectgenerative topographic mapping
dc.subjectelectroencephalography
dc.titleGenerative topographic mapping of electroencephalography (EEG) data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dantanarayana_colostate_0053N_12792.pdf
Size:
5.35 MB
Format:
Adobe Portable Document Format
Description:
Download

Collections

2000-2019
Theses and Dissertations