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Single-trial P300 classification using PCA with LDA and neural networks

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dc.contributor.authorSharma, Nand, author
dc.contributor.authorAnderson, Charles, advisor
dc.contributor.authorKirby, Michael, advisor
dc.contributor.authorPeterson, Chris, committee member
dc.date.accessioned2007-01-03T06:11:37Z
dc.date.available2007-01-03T06:11:37Z
dc.date.issued2013
dc.description.abstractA brain-computer interface (BCI) is a device that uses brain signals to provide a non-muscular communication channel for motor-impaired patients. It is especially targeted at patients with 'locked-in' syndrome, a condition where the patient is awake and fully aware but cannot communicate with the outside world due to complete paralysis. The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoughts into commands to control artificial devices. The main challenge in the classification of P300 trials in electroencephalographic (EEG) data is 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. This has resulted in a need for better methods to improve single-trial classification accuracy of P300 response. In this work, we use Principal Component Analysis (PCA) as a preprocessing method and use Linear Discriminant Analysis (LDA)and neural networks for classification. The results show that a combination of PCA with these methods provided as high as 13% accuracy gain while using only 3 to 4 principal components. So, PCA feature selection not only increased the classification accuracy but also reduced the execution time of the algorithms by the resulting dimensionality reduction. It was also observed that when treating each data sample from each EEG channel as a separate data sample, PCA successfully separates out the variance across channels.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierSharma_colostate_0053N_12118.pdf
dc.identifierETDF2013500423COMS
dc.identifier.urihttp://hdl.handle.net/10217/81079
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.titleSingle-trial P300 classification using PCA with LDA and neural networks
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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