M.Sc. Tezi Görüntüleme
A brain computer interface (BCI) is a communication system that does not depend onthe normal output pathways consisting of periphery nerves and muscles. BCI transforms
mental intentions into control commands by analyzing the biomedical brain activity. Thetechnique can be an interface to patients who totally losing volitional motor ability like
amyotrophic lateral sclerosis (ALS). Thus, life quality of these type patients will beincreased.
In the past decade BCI technology has developed rapidly. In BCI applicationsclassification accuracy and information transfer rate are two important issues. The goal in
area of BCI research is to develop a method which has higher classification rate and braincomputer interfacing data rate than existing methods. One method to boost classification
accuracy is to improve the quality of input signal of a BCI system. Electrocorticographic(ECoG) recordings, derived from surface of the cortex, have the advantages of higher
signal-to-noise ratio and better spatial resolution, and thus may be used as a feasiblealternative of BCI signal source.
In this thesis, it was studied on ECoG dataset which was obtained under differentmental and visual tasks used in literature. In this data set, creating algorithms for
contributing the classification of signals in different session situations is required. Featureextraction process is the main and also most difficult issue in BCI applications. In this
work features are extracted from ECoG signals which have two different classes by meansof wavelet transform. According to discovered features classification was done by using knearest
neighbor (KNN), support vector machines (SVM) and linear discriminant analyses(LDA) classifier. All these studies actualized by considering the goal of to obtain higher
classification rate and high brain computer interfacing data rate.
Key Words: ECoG, BCI, Feature Extraction, Classification