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An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis
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 appli...
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| Format: | Thesis |
| Language: | English |
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Division of Biomedical Engineering
2014
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| _version_ | 1867613203917176832 |
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| access_status_str | Open Access |
| author | Bentley, Alexander Simon Jeremy |
| author2 | John, Lester |
| author_browse | Bentley, Alexander Simon Jeremy John, Lester |
| author_facet | John, Lester Bentley, Alexander Simon Jeremy |
| author_sort | Bentley, Alexander Simon Jeremy |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/10127 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:24.523Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | Division of Biomedical Engineering |
| publisherStr | Division of Biomedical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/10127 An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis Bentley, Alexander Simon Jeremy John, Lester Biomedical Engineering 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. 2014-12-26T14:17:24Z 2014-12-26T14:17:24Z 2011 Master Thesis Masters MSc http://hdl.handle.net/11427/10127 eng application/pdf Division of Biomedical Engineering Faculty of Health Sciences University of Cape Town |
| spellingShingle | Biomedical Engineering Bentley, Alexander Simon Jeremy An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis |
| thesis_degree_str | Master's |
| title | An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis |
| title_full | An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis |
| title_fullStr | An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis |
| title_full_unstemmed | An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis |
| title_short | An offline multi-class auditory P300 brain-computer interface using principal and independent component analysis |
| title_sort | offline multi class auditory p300 brain computer interface using principal and independent component analysis |
| topic | Biomedical Engineering |
| url | http://hdl.handle.net/11427/10127 |
| work_keys_str_mv | AT bentleyalexandersimonjeremy anofflinemulticlassauditoryp300braincomputerinterfaceusingprincipalandindependentcomponentanalysis AT bentleyalexandersimonjeremy offlinemulticlassauditoryp300braincomputerinterfaceusingprincipalandindependentcomponentanalysis |