Muammer GÖKBULUT, Nevzat OLGUN, Erkan TANYILDIZI

SSVEP Tabanlı Beyin Bilgisayar Arayüzü Tasarımı ve Sistem Otomasyonu

Beyin Bilgisayar Arayüzü (BBA), çeşitli sebeplerden felç geçirmiş veya Amyotrofik Lateral Skleroz (ALS) gibihastalıklar yüzünden mevcut kas sistemlerini ve sinir sistemini kullanamayan bireylerin dış dünya ileetkileşimini sağlayan bir iletişim sistemidir. BBA sisteminde felçli bireyin sadece beyin aktivitesi yorumlanarakbilgisayar, elektronik sistemler veya tekerlekli sandalye gibi dış ortam cihazları ile iletişim kurmasısağlanmaktadır. Bu çalışmada kararlı durum görsel uyarılmış potansiyeller (Steady-State Visual EvokedPotentials, SSVEP) tabanlı BBA sistemi ile DC motorun hız ve yön kontrolü gerçekleştirilmiştir. BBAsisteminde DC motorun sola dönmesini, sağa dönmesini, hızını arttırmasını, hızını azaltmasını ve durmasınıtemsil eden 5 adet düşük frekans gurubundaki dama desenli görsel uyaranlar tasarlanmıştır. Görsel uyaranlar vekullanıcıya geribildirim için LCD ekran kullanılır. Kullanıcıların elektroansefalografi (EEG) kayıtları için 14kanallı kablosuz Emotiv EEG başlık kiti kullanılmıştır. Elde edilen kayıtlar, sınıflandırma öncesi 5-40 Hzarasında bant geçiren filtreden geçirilmiştir. BBA sisteminde sınıflayıcı olarak Kanonik Korelasyon Analizi(KKA) kullanılmıştır. BBA otomasyon sistemi, çevrimiçi 10 snlik örnekler kullanılarak deneye katılan 2sağlıklı gönüllü ile test edilmiş ve tasarlanan BBA sisteminin iyi bir performansa sahip olduğu görülmüştür.

SSVEP Based Brain Computer Interface Design and System Automation

Brain-Computer Interface (BCI) is a communication system used for interaction with outside world byindividuals who are not able to use their muscular system and nervous system due to paralysis or AmyotrophicLateral Sclerosis (ALS) diseases for various reasons. In the BCI system, communication of paralyzed individualwith external equipment such as computer, electronics devices and wheelchair is provided by interpreting brainactivity of individual. In this paper, speed and direction control of direct current (DC) motor is implemented byusing Steady-State Visual Evoked Potentials (SSVEP) based BCI system. In the BCI system, 5 visual stimuli inthe form of checkerboard pattern in low frequency band are designed to represent the motor speed and directionsuch as turn the left, turn the right, increase the speed, decrease the speed and stop. Visual stimuli and the LCDscreen are used for feedback to user. 14 channel wireless Emotive EEG headset is used forelectroencephalography (EEG) recordings of users. Before classification, recordings are filtered using a 5-40 Hzband pass filter. In BCI System, Canonical Correlation Analysis (CCA) is used as classifier. BCI automationsystem is tested with two healthy volunteers using the online samplings of 10 seconds and very highperformance is obtained.

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1. ALS(Amyotrophic Lateral Sclerosis) Fact Sheet, http://www.ninds.nih.gov/disorders/amyotrophicla teralsclerosis/detail_amyotrophiclateralsclerosis.ht m, 12 Temmuz 2011 2. Wolpaw R. J., Birbaumer N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., "Brain- computer interfaces for communication and control", Clinical Neurophysiology, 113, 2002, 767-791 3. Mason S.G., Birch G.E., A General Framework for Brain-Computer Interface Design, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 11, No. 1, March 2003 4. Axel Gra¨ser and Ivan Volosyak. BRAINROBOT - Methods and Applications for Brain-Computer Interfaces. Aachen: Shaker Verlag, 2010. 5. Hoffmann, U., Vesin, J., Ebrahimi, T., Diserens, K.: An efficient P300-based braincomputer interface for disabled subjects. Journal of Neuroscience Methods 167(1), 115 125 (2008) 6. Lin, Z., Zhang, C., Wu, W., Gao, X.: Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based 7. Nijholt, A., Plass-Oude Bos, D., Reuderink, B.: Turning shortcomings into challenges: Brain- computer interfaces for games. Entertainment Computing 1(2), 8594 (2009) 8. B. Blankertz, G. Dornhege, M. Krauledat, K. M¨uller, and G. Curio, The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects, NeuroImage, vol. 37, no. 2, pp. 539550, 2007. 9. Y. J. Wang, R. P. Wang, X. R. Gao, B. Hong, and S. K. Gao, A practical VEP-based brain- computer interface, IEEE Trans. on Neural Syst. Rehabil. Eng., vol. 14, no. 2, pp. 234-240, Jun. 2006. 10.Volosyak I (2011). SSVEP-based Bremen BCI interface--boosting information transfer rates. J Neural Eng 8 (3): 036020. 11. B. Z. Allison, T. Lu¨th, D. Valbuena, A. Teymourian, Volosyak, and A. Gr¨aser. BCI demographics: How many (and what kinds of) people can use an SSVEP BCI? IEEE Trans Neural Syst Rehabil Eng, 2010. 12.H. Cecotti, I. Volosyak, and A. Gr¨aser. Evaluation of an SSVEP based brain-computer interface on the command and application levels. 4th IEEE EMBS International Conference on Neural Engineering, 2009. 13.D. Valbuena, I. Sugiarto, and A. Gr¨aser. Spelling with the bremen brain-computer interface and the integrated SSVEP stimulator. Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course, pages 291296, 2008. 14.I. Volosyak, H. Cecotti, D. Valbuena, and A. Gr¨aser. Evaluation of the bremen SSVEP based BCI in real world conditions. 11th International IEEE Con- ference on Rehabilitation Robotics, pages 322331, 2009. 15.Sugiarto, Indar, Brendan Allison, and A. Graser. "Optimization strategy for SSVEP-based BCI in spelling program application." Computer Engineering and Technology, 2009. ICCET'09. International Conference on. Vol. 1. IEEE, 2009. 16.Vilic, Adnan, et al. "DTU BCI speller: An SSVEP-based spelling system with dictionary support." Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013. 17.M. Cheng, X. R. Gao, S. K. Gao, and D. Xu, Design and implementation of a brain computer interface with high transfer rates, IEEE Trans Biomed Eng., vol. 49. No. 10, pp. 1181-1186, 2002. 18.D. Regan, Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. New York: Elsevier, 1989. 19.Crossman, A.R. ve Neary, D., Neuroanatomy: An Ilustared Colour Text,Churchill Livingstone, Edinburgh, 2000. 20.Emotiv Research Plus Specification, Şubat 2012. 21.Zhu, Danhua, et al. "Online BCI implementation of high-frequency phase modulated visual stimuli." Universal Access in Human-Computer Interaction. Users Diversity. Springer Berlin Heidelberg, 2011. 645-654. 22.Wu, Chi-Hsu, and Heba Lakany. "Impact of Stimulus Configuration on Steady State Visual Evoked Potentials (SSVEP) Response." COGNITIVE 2012, The Fourth International Conference on Advanced Cognitive Technologies and Applications. 2012. 23.MATLAB version 7.10.0. Natick, Massachusetts: The MathWorks Inc., 2010. 24.Kleiner, Mario, et al. "Whats new in Psychtoolbox-3." Perception 36.14 (2007): 1-1. 25.Arduino, http://www.arduino.cc/en/Main/arduino BoardUno, 18 Eylül 2012 26.H. Hotelling, Relations between two sets of variates, Biometrika, vol. 28, pp. 321377, 1936. 27.G. Bin, X. Gao, Z. Yan, B. Hong, and S. Gao, An online multi-channel SSVEP-based brain computer interface using a canonical correlation analysis method, Journal of Neural Engineering, 6 (2009), p. 046002.