In this study, a P300 based Brain-Computer Interface (BCI) system design is
realized by the implementation of the Spelling Paradigm. The main challenge in
these systems is to improve the speed of the prediction mechanisms by the
application of different signal processing and pattern classification techniques in
BCI problems.
The thesis study includes the design and implementation of a 10 channel
Electroencephalographic (EEG) data acquisition system to be practically used in
BCI applications. The electrical measurements are realized with active electrodes
for continuous EEG recording. The data is transferred via USB so that the device
can be operated by any computer.
v
Wiener filtering is applied to P300 Speller as a signal enhancement tool for the
first time in the literature. With this method, the optimum temporal frequency
bands for user specific P300 responses are determined. The classification of the
responses is performed by using Support Vector Machines (SVM&rsquo / s) and Bayesian
decision. These methods are independently applied to the row-column
intensification groups of P300 speller to observe the differences in human
perception to these two visual stimulation types. It is observed from the
investigated datasets that the prediction accuracies in these two groups are
different for each subject even for optimum classification parameters.
Furthermore, in these datasets, the classification accuracy was improved when the
signals are preprocessed with Wiener filtering. With this method, the test
characters are predicted with 100% accuracy in 4 trial repetitions in P300 Speller
dataset of BCI Competition II. Besides, only 8 trials are needed to predict the
target character with the designed BCI system.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12611141/index.pdf |
| Date | 01 September 2009 |
| Creators | Erdogan, Hasan Balkar |
| Contributors | Gencer, Nevzat G |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.0014 seconds