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Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : University of Stellenbosch
2009
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| _version_ | 1867613882556612608 |
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| access_status_str | Open Access |
| author | Lodder, Shaun |
| author2 | Du Preez, J. A. |
| author_browse | Du Preez, J. A. Lodder, Shaun |
| author_facet | Du Preez, J. A. Lodder, Shaun |
| author_sort | Lodder, Shaun |
| collection | Thesis |
| dc_rights_str_mv | University of Stellenbosch |
| description | Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/2791 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:43:12.690Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2009 |
| publishDateRange | 2009 |
| publishDateSort | 2009 |
| publisher | Stellenbosch : University of Stellenbosch |
| publisherStr | Stellenbosch : University of Stellenbosch |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/2791 Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis Lodder, Shaun Du Preez, J. A. University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Feature extraction Wavelet packet decomposition (WPD) Cepstral analysis Brain-computer interfaces Wavelets (Mathematics) Electroencephalography Dissertations -- Electronic engineering Theses -- Electronic engineering Electrical and Electronic Engineering Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. ENGLISH ABSTRACT: Brain-Computer Interface (BCI) monitors brain activity by using signals such as EEG, EcOG, and MEG, and attempts to bridge the gap between thoughts and actions by providing control to physical devices that range from wheelchairs to computers. A crucial process for a BCI system is feature extraction, and many studies have been undertaken to find relevant information from a set of input signals. This thesis investigated feature extraction from EEG signals using two different approaches. Wavelet packet decomposition was used to extract information from the signals in their frequency domain, and cepstral analysis was used to search for relevant information in the cepstral domain. A BCI was implemented to evaluate the two approaches, and three classification techniques contributed to finding the effectiveness of each feature type. Data containing two-class motor imagery was used for testing, and the BCI was compared to some of the other systems currently available. Results indicate that both approaches investigated were effective in producing separable features, and, with further work, can be used for the classification of trials based on a paradigm exploiting motor imagery as a means of control. AFRIKAANSE OPSOMMING: ’n Brein-Rekenaar Koppelvlak (BRK) monitor brein aktiwiteit deur gebruik te maak van seine soos EEG, EcOG, en MEG. Dit poog om die gaping tussen gedagtes en fisiese aksies te oorbrug deur beheer aan toestelle soos rolstoele en rekenaars te verskaf. ’n Noodsaaklike proses vir ’n BRK is die ontginning van toepaslike inligting uit inset-seine, wat kan help om tussen verskillende gedagtes te onderskei. Vele studies is al onderneem oor hoe om sulke inligting te vind. Hierdie tesis ondersoek die ontginning van kenmerk-vektore in EEG-seine deur twee verskillende benaderings. Die eerste hiervan is golfies pakkie ontleding, ’n metode wat gebruik word om die sein in die frekwensie gebied voor te stel. Die tweede benadering gebruik kepstrale analise en soek vir toepaslike inligting in die kepstrale domein. ’n BRK is geïmplementeer om beide metodes te evalueer. Die toetsdata wat gebruik is, het bestaan uit twee-klas motoriese verbeelde bewegings, en drie klassifikasie-tegnieke was gebruik om die doeltreffendheid van die twee metodes te evalueer. Die BRK is vergelyk met ander stelsels wat tans beskikbaar is, en resultate dui daarop dat beide metodes doeltreffend was. Met verdere navorsing besit hulle dus die potensiaal om gebruik te word in stelsels wat gebruik maak van motoriese verbeelde bewegings om fisiese toestelle te beheer. 2009-11-25T14:15:26Z 2010-06-01T08:58:25Z 2009-11-25T14:15:26Z 2010-06-01T08:58:25Z 2009-12 Thesis http://hdl.handle.net/10019.1/2791 en University of Stellenbosch application/pdf Stellenbosch : University of Stellenbosch |
| spellingShingle | Feature extraction Wavelet packet decomposition (WPD) Cepstral analysis Brain-computer interfaces Wavelets (Mathematics) Electroencephalography Dissertations -- Electronic engineering Theses -- Electronic engineering Electrical and Electronic Engineering Lodder, Shaun Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| title | Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| title_full | Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| title_fullStr | Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| title_full_unstemmed | Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| title_short | Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| title_sort | single trial classification of an eeg based brain computer interface using the wavelet packet decomposition and cepstral analysis |
| topic | Feature extraction Wavelet packet decomposition (WPD) Cepstral analysis Brain-computer interfaces Wavelets (Mathematics) Electroencephalography Dissertations -- Electronic engineering Theses -- Electronic engineering Electrical and Electronic Engineering |
| url | http://hdl.handle.net/10019.1/2791 |
| work_keys_str_mv | AT loddershaun singletrialclassificationofaneegbasedbraincomputerinterfaceusingthewaveletpacketdecompositionandcepstralanalysis |