{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/10127"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/10127","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis","abstract":"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.","abstract_html":"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. 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