Murat KAYA

Computer Engineer | ug.murat.kaya@toros.edu.tr

About Me

I am Murat KAYA,


I am a post graduate student at Mersin University Computer Engineering Dept. I have studied about "Brain Computer Interface" for 3,5 years. I have some articles about that scientific field because of that ı am a scholarship at TUBİTAK for 2 years. I have experience about teaching programming to children who are age range 9 and 12. I took some degrees from some project competitions.



If you want more information about me, you can download my CV or surf at this webpage.


EDUCATION

Computer Engineering / Post Graduate
Mersin University
Continuing

Graduation Date 17.06.2019
Continuing

Computer Engineering
Toros University
Graduated

Graduation Date 15.06.2017
Graduated

Business
Anadolu University
Graduated

Graduation Date 15.06.2017
Graduated



EXPERIENCE

Toros University BCI Lab.
Software and Hardware Development

I am working at Toros University BCI Lab. with Associate Professor Yuriy MISHCHENKO. Project is about efficient BCI development and i have worked for 3.5 years to modify EEG machines and signal processing on EEG datas using machine learning methods.

March 2013 - June 2017

Küçükler Akademi / Mersin
Teacher

I teached programming to children who are age rate 9 and 12. At the same time ı worked on a mobile app. to teach programming easily.

January 2016 - July 2016

Telemed BÜN Teknopark
Internship

I improved MRI compatible ECG machine.

June 2015 - July 2015



AWARDS

4. Doğu Akdeniz Üniversiteleri Ar-Ge Proje Pazarı ve Proje Yarışması
5th.

April 2013

SİU2015 Proje Yarışması
2nd.

May 2015

KKB Sosyal Sorumluluk Proje Yarışması
5th.

June 2015


SKILLS

C#


C/C++


Matlab


HTML/CSS


SQL


Photoshop


Latex


Research & Development


Cyber Security



ARTICLES

Detecting the attention state of an operator in continuous attention task using EEG-based brain-computer interface

Modern rapid developments of robotic and automated systems created novel operating environments for machinery and industrial process operators in which the reduction of the level of control exercised by operators can lead to their losing attention during important machinery or process operation tasks. Loss of attention is currently one of the most important causes of work and traffic related accidents. The problem of detecting the loss of operator's attention attracted substantial attention in recent years. In this work, an EEG and SVM-based Brain-Computer Interface system was developed for determining an operator's attention state. Using a virtual continuous attention vehicle control task, the ability of the system to detect different operator attention states with high degree of reliability was demonstrated.
LINK


EEG, BCI

Developing computational infrastructure for an EEG-based brain computer

Brain-computer interfaces is a new field of research aimed at developing technologies for the control of external robotic and computer devices by interfacing directly with the human nervous system. EEG-based brain-computer interfaces is a type of such devices that uses for brain activity imaging noninvasive technique of electroencephalography. Development of flexible yet comprehensive computational infrastructure is important for enabling the development of such devices in a laboratory as well as facilitating brain-computer interfaces research more generally. Here we present a set of Matlab tools created in our laboratory as a part of the development of EEG-based brain-computer interface. The toolbox supports data acquisition in different experimental formats using EMOTIV EPOC and Nihon Kohden EEG-1200, basic data processing, preselection of features, and SVM-based trial classification.
LINK


SVM,BCI

Beyin bilgisayar arayüzü için DVM makine öğrenme yöntemi kullanılarak EEG verilerinden sağ ve sol el hareket düşüncelerinin tespiti

Beyin-bilgisayar arayüzleri (BBA) insan beyni ile bilgisayar arasında kurulan doğrudan iletişim yollarını oluşturur. BBA verimli protezler ve iletişim teknolojileri gibi alanlarda kullanılırken, günümüzde insanların cihazlarla doğrudan iletişim kurmasına olanak sağlamaktadır. Bu çalışmada, Destek Vektör Makineleri makine öğrenme yöntemi ve uyarlanan Epoc Emotiv portatif EEG görüntüleme cihazı kullanılarak sağ ve sol hareket düşüncelerinin tespitinden BBA uygulanmaktadır. Uygulanan BBA, tek olay bazında çalışarak yaklaşık olarak %80 doğrululukla, sağ/sol hareket düşüncesinin ayrılmasını sağladı. Tek olay bazında %80-85 doğruluk oranıyla çalışan, geliştirilen BBA yöntemi, eylemi belirtmek için iki olay kullanıldığında %90-95 doğrulukla çalışıp zihinsel süreçlere bağlı hariç cihazların kontrolünü sağlayabilmektedir.

Turkish research for BCI



CONTACT

Email
ug.murat.kaya@toros.edu.tr / muratkaya.de@gmail.com

Social Network

Created by Murat KAYA