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Detecting error related negativity using EEG potentials generated during simulated brain computer interaction

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dc.contributor.authorVerlekar, Prathamesh, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorRuiz, Jaime, committee member
dc.contributor.authorDavies, Patricia, committee member
dc.date.accessioned2007-01-03T06:51:09Z
dc.date.available2007-01-03T06:51:09Z
dc.date.issued2014
dc.description.abstractError related negativity (ERN) is one of the components of the Event-Related Potential (ERP) observed during stimulus based tasks. In order to improve the performance of a brain computing interface (BCI) system, it is important to capture the ERN, classify the trials as correct or incorrect and feed this information back to the system. The objective of this study was to investigate techniques to detect presence of ERN in trials. In this thesis, features based on averaged ERP recordings were used to classify incorrect from correct actions. One feature selection technique coupled with four classification methods were used and compared in this work. Data were obtained from healthy subjects who performed an interaction experiment and the presence of ERN indicating incorrect responses was studied. Using suitable classifiers trained on data recorded earlier, the average recognition rate of correct and erroneous trials was reported and analyzed. The significance of selecting a subset of features to reduce the data dimensionality and to improve the classification performance was explored and discussed. We obtained success rates as high as 72% using a highly compact feature subset.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierVerlekar_colostate_0053N_12637.pdf
dc.identifier.urihttp://hdl.handle.net/10217/84568
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.subjectRFE
dc.subjectSVM
dc.subjectbrain computer interface
dc.subjectmachine learning
dc.subjectneural network
dc.subject.lcshEEG
dc.titleDetecting error related negativity using EEG potentials generated during simulated brain computer interaction
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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