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A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG
Includes bibliographical references.
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2015
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| _version_ | 1867613148930899968 |
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| access_status_str | Open Access |
| author | Mulligan, Shaun R |
| author2 | Verrinder, Robyn |
| author_browse | Mulligan, Shaun R Verrinder, Robyn |
| author_facet | Verrinder, Robyn Mulligan, Shaun R |
| author_sort | Mulligan, Shaun R |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/13149 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:31.816Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/13149 A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG Mulligan, Shaun R Verrinder, Robyn John, L Electrical Engineering Includes bibliographical references. The patent developed by Dr. L. John [1] allows for the the detection of deep muscle activation through the combination of specially positioned monopolar surface Electromyography (sEMG) electrodes and a Blind Source Separation algorithm. This concept was then proved by Morowasi and John [2] in a 12 electrode prototype system around the bicep. This proof of concept showed that it was possible to extract the deep tissue activity of the brachialis muscle in the upper arm, however, the effect of surface electrode positioning and effectual number of electrodes on signal quality is still unclear. The hope of this research is to extend this work. In this research, a genetic algorithm (GA) is implemented on top of the Fast Independent Component Analysis (FastICA) algorithm to reduce the number of electrodes needed to isolate the activity from all muscles in the upper arm, including deep tissue. The GA selects electrodes based on the amount of significant information they contribute to the ICA solution and by doing so, a reduced electrode set is generated and alternative electrode positions are identified. This allows a near optimal electrode configuration to be produced for each user. The benefits of this approach are: 1.The generalized electrode array and this algorithm can select the near optimal electrode arrangement with very minimal understanding of the underlying anatomy. 2. It can correct for small anatomical differences between test subjects and act as a calibration phase for individuals. As with any design there are also disadvantages, such as each user needs to have the electrode placement specifically customised for him or her and this process needs to be conducted using a higher number of electrodes to begin with. 2015-06-29T07:40:05Z 2015-06-29T07:40:05Z 2014 Master Thesis Masters MSc http://hdl.handle.net/11427/13149 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Mulligan, Shaun R A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG |
| thesis_degree_str | Master's |
| title | A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG |
| title_full | A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG |
| title_fullStr | A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG |
| title_full_unstemmed | A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG |
| title_short | A Comparison of ICA versus genetic algorithm optimized ICA for use in non-invasive muscle tissue EMG |
| title_sort | comparison of ica versus genetic algorithm optimized ica for use in non invasive muscle tissue emg |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/13149 |
| work_keys_str_mv | AT mulliganshaunr acomparisonoficaversusgeneticalgorithmoptimizedicaforuseinnoninvasivemuscletissueemg AT mulliganshaunr comparisonoficaversusgeneticalgorithmoptimizedicaforuseinnoninvasivemuscletissueemg |