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P300 classification using deep belief nets

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dc.contributor.authorSobhani, Amin, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorBen-Hur, Asa, committee member
dc.contributor.authorPeterson, Chris, committee member
dc.date.accessioned2007-01-03T06:42:48Z
dc.date.available2007-01-03T06:42:48Z
dc.date.issued2014
dc.description.abstractElectroencephalogram (EEG) is measure of the electrical activity of the brain. One of the most important EEG paradigm that has been explored in BCI systems is the P300 signal. The P300 wave is an endogenous event-related-potential which can be captured during the process of decision making as a subject reacts to a stimulus. One way to detect the P300 signal is to show a subject two types of visual stimuli occurring at different rates. The event occurring less frequently than the other elicits a positive signal component with a latency of roughly 250-500 ms. P300 detection has many applications in the BCI field. One of the most common applications of P300 detection is the P300 speller which enables users to type letters on the screen. Machine Learning algorithms play a crucial role in designing a BCI system. One important purpose of using the machine learning algorithms in BCI systems is the classification of EEG signals. In order to translate EEG signals to a control signal, BCI systems should first capture the pattern of EEG signals and discriminate them into different command categories. This is usually done using different machine learning-based classifiers. In the past, different linear and nonlinear methods have been used to discriminate the P300 signals from nonP300 signals. This thesis provides the first attempt to implement and examine the performance of the Deep Belief Networks (DBN) to model the P300 data for classification. The highest classification accuracy we achieved with DBN is 97 percent for testing trials. In our experiments, we used EEG data collected by the BCI lab at Colorado State University on both healthy and disabled subjects.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierSobhani_colostate_0053N_12432.pdf
dc.identifier.urihttp://hdl.handle.net/10217/84142
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.subjectbrain computer interface
dc.subjectP300 classification
dc.subjectmachine learning
dc.subjectdeep belief networks
dc.subject.lcshEEG
dc.titleP300 classification using deep belief nets
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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