Yardımcı Sistem Olarak BCI ve EEG Sinyallerinin BCI Sistemlerde Kullanım Şekilleri

Year 2018, Volume: 10 Issue: 3, 72 - 79, 31.12.2018

Atilla Ergüzen , Kadir Haltaş Erdal Erdal , MURAT Lüy

https://doi.org/10.29137/umagd.487930

Cited By: 1

Abstract

Günümüzde halen insan anatomisi ve buna bağlı olarak
hastalıkların incelenmesi süregelmektedir. İnsanoğlunun en çok ilgisini çeken
anatomik kısımlardan bir tanesi de şüphesiz ki beyindir. Günümüz çalışmaları
beyin sinirsel faaliyetlerini izleyerek çeşitli alanlarda ilerlemeler
göstermektedir. Beyin sinyallerinin izlenmesi için kullanılan en yaygın
yöntemlerden biri EEG (Elektroansefalogram) olarak bilinmektedir. Günümüzde EEG
tıbbi alanda tanı ve tedaviye yardımcı olarak kullanıldığı gibi aynı zamanda
disiplinler arası olarak bilgisayar bilimlerinde BCI (Beyin Bilgisayar Arayüzü)
sistemlerinde kullanılmaktadır. Beyin Bilgisayar Arayüzü (Brain Computer
Interface (BCI) ) sistemlerinin temelinde birey beyin sinyallerinin toplanarak
bireyin dış dünyayla iletişime geçmesi için uygun şekilde kullanımı söz
konusudur. BCI sistemlerinin kullanım alanları; kısmi motor hareket kayıpları,
ağır felçli bireyler, ağır konuşma güçlükleri vb. olarak sıralanabilir. Bu
çalışmada günümüzde BCI sistem tasarımlarında gelinen nokta hakkında derleme
yapılmıştır. Bu sayede BCI sistemi çalışmalarının durumu izlenebilecek ve BCI
alanında gelişmelerin doğrultusu görülebilecektir.

Keywords

EEG, BCI, Beyin, Kontrol, Sistem

Using of BCI as An Assistant System and EEG Signals in BCI Systems

Abstract

Currently, human anatomy and related diseases
are still under investigation. One of the most interesting anatomical parts of
human beings is undoubtedly the brain. Today's studies show progress in various
areas by monitoring the neural activity of the brain. One of the most common
methods for monitoring brain signals is known as EEG (Electroencephalogram).
Today, EEG is used as an adjunct to medical diagnosis and treatment, and is
also used in interdisciplinary computer science systems in BCI (Brain Computer
Interface) systems. Brain Computer Interface (BCI) systems are based on the use
of individual brain signals and the proper use of the individual to communicate
with the outside world. Usage areas of BCI systems; partial motor movement
loss, severe paralyzed individuals, severe speech difficulties and so on can be
listed as. In this study, the current point of BCI system design is compiled.
In this way, the status of BCI system studies can be monitored and the
direction of developments in the BCI field can be seen.

Keywords

EEG, BCI, Brain, Control, System

References

• AIQattan, D., & Sepulveda, F. (2017). Towards Sign Language Recognition Using EEG-Based Motor Imagery Brain Computer Interface. 2017 5th International Winter Conference on Brain-Computer Interface (BCI). Sabuk.
• Anupama, H., N.K., C., & Lingaraju, G. (2014). Real-time EEG based Object Recognition System Using Brain Computer Interface. 2014 International Conference on Contemporary Computing and Informatics (IC3I). Mysore.
• Bora, İ., & Yeni, S. (2012). EEG ATLASI. NOBEL TIP KİTABEVLERİ.
• Camacho, J., & Manian, V. (2016). Real-Time Single Channel EEG Motor Imagery based Brain Computer Interface. 2016 World Automation Congress (WAC). Rio Grande.
• Chan, A., & Dascalu, S. (2017). Using Brain Computer Interface Technology in Connection with Google Street View. 2017 21st International Conference on Control Systems and Computer Science (CSCS). Bucharest.
• Ernest, T., Smitha, K., & Vinod, A. (2015). Detection of Familiar and Unfamiliar Images using EEG-based Brain-Computer Interface. 2015 IEEE International Conference on Systems, Man, and Cybernetics. Kowloon.
• Hsieh, K., Sun, K., Yeh, J., & Pan, Y. (2017). Home Care by Auditory Brain Computer Interface for the Blind with Severe Physical Disabilities. 2017 International Conference on Applied System Innovation (ICASI). Sapporo.
• Ilyas, M. Z., Saad, P., & Ahmad, M. I. (2015). A Survey of Analysis and Classification of EEG Signals for Brain-Computer Interfaces. 2nd International Conference on Biomedical Engineering (ICoBE). Penang.
• Jadav, G. M., Batistić, L., Vlahinić, S., & Vrankić, M. (2017). Brain Computer Interface Communicator : A Response to Auditory Stimuli Experiment. 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). Opatija.
• Katona, J., Ujbanyi, T., Sziladi, G., & Kovari, A. (2016). Speed control of Festo Robotino mobile robot using NeuroSky MindWave EEG headset based Brain-Computer Interface. 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2016). Wrocław.
• Liu, D., Chen, W., Lee, K., Pei, Z., & Mill´an, J. (2017). An EEG-based brain-computer interface for gait training. 2017 29th Chinese Control And Decision Conference (CCDC). Chongqing.
• Lotte, F., Congedo, M., Lécuyer, A., Lamarche,, F., & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering(4).
• Maleki, M., Manshouri, N., & Kayıkçıoğlu, T. (2015). A Novel Brain -Computer Interface based on the Gaze Rotating Vane Independent EEG. 2015 23nd Signal Processing and Communications Applications Conference (SIU). Malatya.
• Maracine, M., Radu, A., Ciobanu, V., & Popescu, N. (2017). Brain Computer Interface Architectures and Classification Approaches. 21st International Conference on Control Systems and Computer Science (CSCS), 217-222.
• Pan, J., Li, Y., & Wang, J. (2016). An EEG-Based Brain-Computer Interface for Emotion Recognition. 2016 International Joint Conference on Neural Networks (IJCNN). Vancouver.
• Siuly, S., Li, Y., & Zhang, Y. (2016). EEG Signal Analysis and Classification Techniques and Applications. Springer International Publishing.
• Smitha , K., Vinod, A., & K, M. (2016). Voice Familiarity Detection using EEG-based Brain-Computer Interface. 2016 IEEE International Conference on Systems, Man, and Cybernetics. Budapest.
• Tatum, W. (2017). AMBULATORY EEG MONITORING. New York: demosMEDICAL.
• Teplan, M. (2002). FUNDAMENTALS OF EEG MEASUREMENT. MEASUREMENT SCIENCE REVIEW , 2(2).