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Comparison of EEG preprocessing methods to improve the performance of the P300 speller

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dc.contributor.authorCashero, Zachary, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorChen, Thomas, advisor
dc.contributor.authorTobet, Stuart, committee member
dc.contributor.authorBen-Hur, Asa, committee member
dc.date.accessioned2007-01-03T05:42:54Z
dc.date.available2007-01-03T05:42:54Z
dc.date.issued2011
dc.description.abstractThe classification of P300 trials in electroencephalographic (EEG) data is made difficult due the low signal-to-noise ratio (SNR) of the P300 response. To overcome the low SNR of individual trials, it is common practice to average together many consecutive trials, which effectively diminishes the random noise. Unfortunately, when more repeated trials are required for applications such as the P300 speller, the communication rate is greatly reduced. Since the noise results from background brain activity and is inherent to the EEG recording methods, signal analysis techniques like blind source separation (BSS) have the potential to isolate the true source signal from the noise when using multi-channel recordings. This thesis provides a comparison of three BSS algorithms: independent component analysis (ICA), maximum noise fraction (MNF), and principal component analysis (PCA). In addition to this, the effects of adding temporal information to the original data, thereby creating time-delay embedded data, will be analyzed. The BSS methods can utilize this time-delay embedded data to find more complex spatio-temporal filters rather than the purely spatial filters found using the original data. One problem that is intrinsically tied to the application of BSS methods is the selection of the most relevant source components that are returned from each BSS algorithm. In this work, the following feature selection algorithms are adapted to be used for component selection: forward selection, ANOVA-based ranking, Relief, and recursive feature elimination (RFE). The performance metric used for all comparisons is the classification accuracy of P300 trials using a support vector machine (SVM) with a Gaussian kernel. The results show that although both BSS and feature selection algorithms can each cause significant performance gains, there is no added benefit from using both together. Feature selection is most beneficial when applied to a large number of electrodes, and BSS is most beneficial when applied to a smaller set of electrodes. Also, the results show that time-delay embedding is not beneficial for P300 classification.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierCashero_colostate_0053N_10585.pdf
dc.identifier.urihttp://hdl.handle.net/10217/49866
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.subjectblind source separation
dc.subjectbrain computer interface
dc.subjectclassification
dc.subjectP300 speller
dc.subjectsignal analysis
dc.subject.lcshEEG
dc.titleComparison of EEG preprocessing methods to improve the performance of the P300 speller
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.)

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