Öğrenci:	Osman Kerem ATEŞ
Danışman:	Doç. Dr. Önder AYDEMİR
Anabilim Dalı:	Elektrik-Elektronik Müh.
Enstitü:	Fen Bilimleri Enstitüsü
Üniversite:	Karadeniz Teknik Üniversitesi

Tez Adı:	EL İLE KAVRAMA HAREKETİNİN DÜŞÜNÜLMESİ SIRASINDA KAYDEDİLEN EEG İŞARETLERİNİN PSO TABANLI SINIFLANDIRILMASI
Tezin Türü:	Yüksek Lisans
Kabul Tarihi:	5/4/2021
Sayfa Sayısı:	67
Tez No:	i3871

Özet:

One of the important objective of the Brain Computer Interface (BCI) systems is to search

innovative solutions like rehabilitation scenario for disabled or patient subjects. People who have

stroke or have an accident still can provide accurately some imagery movements. Automated decoding

of these imagery movements from brain signals will be very helpful for rehabilitation and the

development of robot-assisted technologies based on BCI systems. Then, work on the patients data

instead of using healthy subjects data can be more meaningful for these interfaces. In this thesis work,

a dataset that of EEG brain imaging data for 10 stroke patients having hand functional disability was

used. This current data was also used in Clinical BCI Challenge WCCI 2020 competition.

With proposed method, the effective electrodes and features were selected for high

classification accuracy purpose. In feature selection stage, the Particle Swarm Optimization (PSO)

algorithm was used. Through selected effective parameters, discrimination of imagery of right and left

hand movement was done with 84.32%, 80.25%, 77.25% and 83.08% accuracy rate by using

respectively k-nearest neighbors, linear discriminant analysis, support vector machines and bagging

decision tree algorithms.