Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis

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dc.contributor.advisorDu Preez, J. A.en_ZA
dc.contributor.authorLodder, Shaunen_ZA
dc.contributor.otherUniversity of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
dc.date.accessioned2009-11-25T14:15:26Zen_ZA
dc.date.accessioned2010-06-01T08:58:25Z
dc.date.available2009-11-25T14:15:26Zen_ZA
dc.date.available2010-06-01T08:58:25Z
dc.date.issued2009-12en_ZA
dc.descriptionThesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009.en_ZA
dc.description.abstractENGLISH 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.en_ZA
dc.description.abstractAFRIKAANSE 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.af_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/2791
dc.language.isoenen_ZA
dc.publisherStellenbosch : University of Stellenbosch
dc.rights.holderUniversity of Stellenbosch
dc.subjectFeature extractionen_ZA
dc.subjectWavelet packet decomposition (WPD)en_ZA
dc.subjectCepstral analysisen_ZA
dc.subjectBrain-computer interfacesen_ZA
dc.subjectWavelets (Mathematics)en_ZA
dc.subjectElectroencephalographyen_ZA
dc.subjectDissertations -- Electronic engineeringen_ZA
dc.subjectTheses -- Electronic engineeringen_ZA
dc.subject.otherElectrical and Electronic Engineeringen_ZA
dc.titleSingle-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysisen_ZA
dc.typeThesisen_ZA
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