M.Sc. Tezi Görüntüleme

Student: Önder AYDEMİR
Supervisor: Assoc. Prof. Dr. Temel KAYIKÇIOĞLU
Department: Elektrik-Elektronik Müh.
Institution: Graduate School of Natural and Applied Sciences
University: Karadeniz Technical University, Turkey
Title of the Thesis: Feature Extraction For EEG Signals Towards Brain Computer Interface Applications
Level: M.Sc.
Acceptance Date: 26/6/2008
Number of Pages: 93
Registration Number: i1914


The goal in area of EEG based BCI research is to develop a method which has higher classification rate and brain computer interfacing data rate than existing methods.

       In this thesis, it was studied on EEG dataset which was obtained under different mental and visual tasks used in literature. Feature extractions from that EEG dataset were analysed with various mathematical methods. According to discovered features classification was done by using support vector machines (SVM) and KNN classifier. All these studies actualized by considering the goal of to obtain higher classification rate and high brain computer interfacing data rate.

      Key Words: EEG, BCI, Feature Extraction, Classification