An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis

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dc.contributor.advisorJohn, Lesteren_ZA
dc.contributor.authorBentley, Alexander Simon Jeremyen_ZA
dc.date.accessioned2014-12-26T14:17:24Z
dc.date.available2014-12-26T14:17:24Z
dc.date.issued2011en_ZA
dc.description.abstractThis thesis investigated a multi-class auditory P300 BCI as a step towards FES applicability. A multi-class P300 paradigm approach provides degrees-of-freedom in operating an FES device over the traditional P300 paradigm. Accuracy in classification of target P300s contributes to the paradigm's applicability in a 'real' environment. The computational effectiveness of the paradigm can be enhanced through signal processing prior to classification. A combination of principal component analysis (PCA) and independent component analysis (ICA), together with a method of enhancing the P300 properties through temporal and spatial manipulation are investigated as a means of improving classification accuracy. The combination of these techniques and the use of a multi-class P300 paradigm presents a different approach as a step towards FES applicability in an auditory BCI.en_ZA
dc.identifier.apacitationBentley, A. S. J. (2011). <i>An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering. Retrieved from http://hdl.handle.net/11427/10127en_ZA
dc.identifier.chicagocitationBentley, Alexander Simon Jeremy. <i>"An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2011. http://hdl.handle.net/11427/10127en_ZA
dc.identifier.citationBentley, A. 2011. An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Bentley, Alexander Simon Jeremy AB - This thesis investigated a multi-class auditory P300 BCI as a step towards FES applicability. A multi-class P300 paradigm approach provides degrees-of-freedom in operating an FES device over the traditional P300 paradigm. Accuracy in classification of target P300s contributes to the paradigm's applicability in a 'real' environment. The computational effectiveness of the paradigm can be enhanced through signal processing prior to classification. A combination of principal component analysis (PCA) and independent component analysis (ICA), together with a method of enhancing the P300 properties through temporal and spatial manipulation are investigated as a means of improving classification accuracy. The combination of these techniques and the use of a multi-class P300 paradigm presents a different approach as a step towards FES applicability in an auditory BCI. DA - 2011 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2011 T1 - An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis TI - An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis UR - http://hdl.handle.net/11427/10127 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/10127
dc.identifier.vancouvercitationBentley ASJ. An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2011 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/10127en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Biomedical Engineeringen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherBiomedical Engineeringen_ZA
dc.titleAn offline multi-class auditory P300 brain-computer interface using principal and independent component analysisen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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