EEG Sinyallerini Kullanarak Basitleştirilmiş İnsan Bilgisayar Arayüzü Tasarımı

Yıl 2021, Cilt: 12 Sayı: 2, 201 - 210, 30.03.2021

Hakan Üstünel , Selma Büyükgöze , Doğan Ünal , Emre Zengin , İlhan Umut

https://doi.org/10.24012/dumf.803784

Öz

Beyin Bilgisayar Arayüzleri (BCI), kullanıcıların beynin normal yolları olan kas ve sinir hücrelerini kullanmadan beyin aktivitesindeki değişiklikleri analiz ederek doğrudan harici cihazlarla iletişim kurmalarına ve kontrol etmelerine olanak sağlayan uygulamalardır. BCI'lerin, elektroensefalografi (EEG) cihazları ile ölçülebilen beyin aktivitesinin elektriksel aktivitelerine dayalı olarak insan beyni ile dış dünya arasında alternatif bir iletişim aracı olduğu da söylenebilir. İnsan beyninden ölçülen EEG'de, kişi bir uzvunu hareket ettirmek istediğinde olayla ilişkili potansiyeller EEG'de gözlemlenir. Bu, insan beyninin bilişsel veya hareket karar verme sürecindeki aktivitelerindeki değişiklikler hakkındaki bilgilerin, gözlemlenen EEG'de tespit edilebileceğini göstermektedir. Bu çalışmada, dört kanallı bir EEG kayıt cihazı kullanılarak elde edilen sinyallerin nitelikleri çıkarılmış ve sınıflandırılmıştır. Deneysel çalışma kullanıcı uyanıkken yapıldığından beta sinyallerini işledi. Eserler dikkate alınarak, işlenen veriler çevrimdışı ve çevrimiçi deneme gerçekleştirilerek arayüz için girdi verisi olarak kullanılmıştır. EEG cihazından elde edilen veriler bir bilgisayarda işlendi ve model aracı kontrol etmek için kullanılan bir mikrodenetleyiciye iletildi. Veri iletişimi kablosuz olarak gerçekleştirilir. Model aracın ileri-geri / sağ-sola ve çapraz olarak hareket etmesine izin verilmektedir.

Anahtar Kelimeler

Beyin Bilgisayar Arayüzü, Tıbbi Bilişim, Gömülü Sistemler, EEG

Simplified Human Computer Interface Design Using EEG Signals

Öz

Brain Computer Interfaces (BCI) are applications that allow users to communicate and control external devices directly by analyzing changes in brain activity without using muscle and nerve cells, which are normal pathways of the brain. It can also be said that BCIs are an alternative means of communication between the human brain and the outside world based on the electrical activities of brain activity, which can be measured by electroencephalography (EEG) devices. In the EEG measured from the human brain, when a person wants to move a limb, the potentials associated with the event are observed in the EEG. This suggests that information about changes in the activity of the human brain in the cognitive or movement decision process can be detected in the observed EEG. In this study, the attributes of the signals obtained using a four-channel EEG recorder are extracted and classified. Because the experimental study was performed while the user was awake, it processed beta signals. Considering the artifacts, the processed data was used as input data for the interface by realizing offline and online trial. The data obtained from the EEG device was processed in a computer and transmitted to a microcontroller used to control the model vehicle. Data communication is carried out wirelessly. The model vehicle is allowed to move forward-backward / right-left and diagonally.

Anahtar Kelimeler

Brain Computer Interface, Medical Informatics, Embedded Systems, EEG

Kaynakça

• [1] Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. “Brain–computer interfaces for communication and control”. Clinical neurophysiology, 113(6), 767-791, 2002. https://doi.org/10.1016/S1388-2457(02)00057-3
• [2] Leuthardt EC, Schalk G, Wolpaw JR, Ojemann JG, Moran DW. “A brain–computer interface using electrocorticographic signals in humans”. Journal of neural engineering, 1(2), 63, 2004.
• [3] Banik BC, Ghosh M, Das A, Banerjee D, Paul S, Neogi, B. “Design of mind-controlled vehicle (MCV) & study of EEG signal for three mental states”. Devices for Integrated Circuit (DevIC), Kalyani, Nadia, India, 23-24 March, 2017. https://doi.org/10.1109/DEVIC.2017.8074065
• [4] Öztürk N, Yilmaz B, Önver AY. “Real-Time Robotic Car Control Using Brainwaves and Head Movement”. Medical Technologies National Congress (TIPTEKNO), Magusa, Cyprus, 8-10 November 2018. https://doi.org/10.1109/TIPTEKNO.2018.8596956
• [5] Sevgili, Z., & Mehmet, A. K. I. N. (2019). İmleç Hareketlerine Ait EEG Sinyallerinin Sınıflandırılmasında Adaptif ve Adaptif Olmayan Filtrelerin Uygulamaları. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(1), 57-67. https://doi.org/10.24012/dumf.584345
• [6] Li, Y., Zhou, R., Xu, R., Luo, J., & Jiang, S. X. (2020). A quantum mechanics-based framework for EEG signal feature extraction and classification. IEEE Transactions on Emerging Topics in Computing. https://doi.org/10.1109/TETC.2020.3000734
• [7] Ergün E, Aydemir Ö. “Etkin epoklar ile motor hayaline dayalı EEG işaretlerinin sınıflandırma doğruluğunun artırılması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(5), 817-823, 2018.
• [8] Sezer E. “EEG signal analysis for the diagnosis of epilepsy”. Doctoral dissertation, Selçuk University,Konya, Turkey, 2008.
• [9] Wolpaw JR. “Brain-computer interfaces: signals, methods, and goals”. In First International IEEE EMBS Conference on Neural Engineering, Capri Island, Italy, 20-22 March 2003. https://doi.org/10.1109/CNE.2003.1196894
• [10] Abhang, P. A., Gawali, B. W., & Mehrotra, S. C. (2016). Introduction to EEG-and speech-based emotion recognition. Academic Press. ISBN: 9780128044902
• [11] Lebedev MA, Nicolelis MA. “Brain–machine interfaces: past, present and future”. TRENDS in Neurosciences, 29(9), 536-546, 2006. https://doi.org/10.1016/j.tins.2006.07.004
• [12] Ramadan RA, Refat S, Elshahed MA, Ali RA. Basics of brain computer interface. Brain-Computer Interfaces Editors: Hassanien AE, Azar AT. Basics of Brain Computer Interface (pp. 31-50). Springer, Cham, 2015.
• [13] Novák D, Lhotská L, Eck V, Sorf M. “EEG and VEP signal processing”. Cybernetics, Faculty of Electrical Eng, 50-53, 2004.
• [14] Anupama HS, Cauvery NK, Lingaraju GM. “Brain computer interface and its types-a study”. International Journal of Advances in Engineering & Technology, 3(2), 739, 2012.
• [15] Ang KK, Chua KSG, Phua KS, Wang C, Chin ZY, Kuah CWK, Guan C. “A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke”. Clinical EEG and neuroscience, 46(4), 310-320, 2015. https://doi.org/10.1177/1550059414522229
• [16] Mane R, Chew E, Phua KS, Ang KK, Robinson N, Vinod AP, Guan C. “Prognostic and Monitory EEG-Biomarkers for BCI Upper-limb Stroke Rehabilitation”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019. https://doi.org/10.1109/TNSRE.2019.2924742
• [17] González M, Rojas E, Bolaños W, Segura JP, Murillo L, Solano A, Yu L. “Auditory imagery classification with a non-invasive Brain Computer Interface”. 9th International IEEE/EMBS Conference on Neural Engineering (NER), CA, USA, 20-23 March 2019. https://doi.org/10.1109/NER.2019.8716946
• [18] Argunşah AÖ, Çürüklü AB, Çetin M, Erçil A. “EEG Tabanlı Beyin-Bilgisayar Arayüzü Sistemlerinde Sınıflandırmayı Etkileyen Faktörler”. IEEE 15th Signal Processing and Communications Applications Conference, Eskisehir, Turkey, 11 - 13 June 2007
• [19] Behm A, Kollotzek MA, Hüske F. “Brain Computer Interfaces–Controlling computers by thoughts”, 2006. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9534&rep=rep1&type=pdf (4th May 2019).
• [20] Hyvärinen A, Oja E. “Independent component analysis: algorithms and applications”. Neural networks, 13(4-5), 411-430, 2000. https://doi.org/10.1016/S0893-6080(00)00026-5
• [21] Chatterjee R, Bandyopadhyay T, Sanyal DK, Guha D. “Comparative analysis of feature extraction techniques in motor imagery EEG signal classification”. In Proceedings of First International Conference on Smart System, Innovations and Computing, Jaipur, RJ, India, 15 - 16 April 2017. Springer, Singapore.