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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2791 |
| Date | 12 1900 |
| Creators | Lodder, Shaun |
| Contributors | Du Preez, J. A., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Stellenbosch |
Page generated in 0.0019 seconds