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BEYİN BİLGİSAYAR ARAYÜZÜ İÇİN DVM MAKİNE ÖĞRENME YÖNTEMİ KULLANILARAK EEG VERİLERİNDEN SAĞ VE SOL EL HAREKET DÜŞÜNCELERİNİN TESPİTİ

Year 2017, Volume: 10 Issue: 3, 1 - 20, 12.09.2017

Abstract



 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. 


References

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Year 2017, Volume: 10 Issue: 3, 1 - 20, 12.09.2017

Abstract

References

  • [1] E. E. Fetz, Operant conditioning of cortical unit activity, Science, 163(3870), 955–958, 1969.
  • [2] E. E. Fetz, D. B. Finocchio, Operant conditioning of specific patterns of neural and muscular activity, Science, 174(4007), 431–435, 1971.
  • [3] E. E. Fetz, D. V Finocchio, Operant conditioning of isolated activity in specific muscles and precentral cells, Brain Research, 40(1), 19–23, 1972.
  • [4] E. E. Fetz, M. A. Baker, Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles, Journal of Neurophysiology, 36(2), 179–204, 1973.
  • [5] E. M. Schmidt, J. S. McIntosh, L. Durelli, M. J. Bak, Fine control of operantly conditioned firing patterns of cortical neurons, Experimental Neurology, 61(2), 349–369, 1978.
  • [6] E. M. Schmidt, Single neuron recording from motor cortex as a possible source of signals for control of external devices, Annals of Biomedical Engineering, 8(4-6), 339–349, 1980.
  • [7] B.Z. Allison ve J.A. Pineda, Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system, International journal of psychophysiology, 59(2), 127- 140, 2006.
  • [8] S. Sur, V. K. Sinha, Event-related potential: an overview, Industrial Psychiatry Journal, 18(1), 70–73, 2009.
  • [9] L. A. Farwell, E. Donchin, Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials., Electroencephalography and clinical neurophysiology, 70(6), 510–523, 1988.
  • [10] E. Donchin, K. M. Spencer, R. Wijesinghe, The mental prosthesis: assessing the speed of a P300- based braincomputer interface, IEEE TRansactions on Rehabilitation Engineering, 8(2), 174–179, 2000.
  • [11] F. Piccione, F. Giorgi, P. Tonin, K. Priftis, S. Giove, S. Silvoni, G. Palmas, F. Beverina, P300-based brain computer interface: reliability and performance in healthy and paralysed participants, Clinical Neurophysiology, 117(3), 531–537, 2006.
  • [12] J. R. Wolpaw, D. J. McFarland, Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans., Proceedings of the National Academy of Sciences of the United States of America, 101(51), 17849–17854, 2004.
  • [13] G. Santhanam, S. I Ryu., B. M. Yu, A. Afshar, K. V Shenoy, A high-performance brain-computer interface., Natur, 442(7099), 195–198, 2006.
  • [14] J.M. Carmena, M.A. Lebedev, R.E. Crist, J.E. O’Doherty, D.M. Santucci, D.F. Dimitrov, P.G. Patil, C.S. Henriquez, M.A.L. Nicolelis, Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates, PLoS Biology, 1(2), e42, 2003.
  • [15] S. Musallam, B. D. Corneil, B. Greger, H. Scherberger, R. A. Andersen, Cognitive Control Signals for Neural Prosthetics, Science, 305(5681), 258–262, 2004.
  • [16] M. A. Lebedev, Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface, Journal of Neuroscience, 25(19), 4681–4693, 2005.
  • [17] D. M. Santucci, J. D. Kralik, M. A. Lebedev, M. AL. Nicolelis, Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates, European Journal of Neuroscience, 22(6), 1529–1540, 2005.
  • [18] M. D. Serruya, J. P. Donoghue Chapter III: Design Principles of a Neuromotor Prosthetic Device in Neuroprosthetics, Theory and Practice, ed. Kenneth W. Horch, Gurpreet S. Dhillon, 1158-1196 , 2003.
  • [19] J. Wessberg, C.R. Stambaugh, J.D. Kralik, P.D. Beck, M. Laubach, J.K. Chapin, J. Kim, S.J. Biggs, M.A. Srinivasan, M. AL. Nicolelis, Real-time prediction of hand trajectory by ensembles of cortical neurons in primates., Nature, 408(6810), 361–365, 2000.
  • [20] A. Jackson, C.T. Moritz, J. Mavoori, T.H. Lucas, E.E. Fetz, The Neurochip BCI: towards a neural prosthesis for upper limb function, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 187-190, 2006.
  • [21] M. Velliste, S. Perel, M. C. Spalding, A. S. Whitford, A. B. Schwartz, Cortical control of a prosthetic arm for selffeeding., Nature, 453(7198), 1098–1101, 2008.
  • [22] J.L. Collinger, B. Wodlinger, J.E. Downey, W.. Wang, E.C. Tyler-Kabara, D.J. Weber, A.JC. McMorland, M.. Velliste, M.L. Boninger, A.B. Schwartz, High-performance neuroprosthetic control by an individual with tetraplegia, The Lancet, 381(9866), 557–564, 2012.
  • [23] O. Fukuda, T. Tsuji, M. Kaneko, A. Otsuka, A human-assisting manipulator teleoperated by EMG signals and arm motions, IEEE Transactions on Robotics and Automation, 19(2), 210-222, 2003.
  • [24] N. Jiang, J. Vest-Nielsen, S. Muceli, D. Farina, in Front. Comput. Neurosci. Conference Abstract: BC11 :Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, 2011, p. doi: 10.3389/conf.fncom.2011.53.00081.
  • [25] J. P. Giuffrida, Synergistic neural network control of FES elbow extension after spinal cord injury using EMG, Doktora Tezi, Case Western Reserve University, Cleveland, 2004.
  • [26] J. P. Giuffrida , P. E. Crago, Functional restoration of elbow extension after spinal-cord injury using a neural network-based synergistic FES controller, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(2), 147–152, 2005.
  • [27] J.P. Giuffrida, P.E. Crago, Utilizing remaining voluntary muscle synergies to control FES elbow extension after spinal cord injury, Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE, San Francisco 4118-4121, 2004
  • [28] G. C. Matrone, C. Cipriani, M. C. Carrozza, G. Magenes, Real-time myoelectric control of a multifingered hand prosthesis using Principal Components Analysis, Journal of neuroengineering and rehabilitation , 9(1), 40, 2012.
  • [29] F. V. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-Cummings, N.V. Thakor, Decoding of Individuated Finger Movements Using Surface Electromyography, IEEE Transactions on Biomedical Engineering, 56(5), 1427–1434, 2009. [30] Advanced Arm Dynamics. Smart Prothesis. http://armdynamics.com/. Yayın tarihi Temmuz 22, 1998. Erişim tarihi Ağustos 30, 2016. [31] Bebionic. Smart Prothesis Hands. http://bebionic.com/. Yayın tarihi Mart 16, 2007. Erişim tarihi Ağustos 30, 2016. [32] Touchbionics. Smart Prothesis. http://touchbionics.com/. Yayın tarihi Temmuz 29, 2005. Erişim tarihi Ağustos 30, 2016. [33] Utaharm. Smart Prothesis. http://utaharm.com/. Yayın tarihi Mayıs 16, 1997. Erişim tarihi Ağustos 30, 2016. [34] N. Weiskopf, K. Mathiak, F. Bock S.W.Scharnowski, R. Veit, W. Grodd, R. Goebel, N. Birbaumer, Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI), IEEE transactions on biomedical engineering, 51(6), 966-970, 2004. [35] Pittsburg Brain Activity Interpretation Competition (PBAIC) 2007, http://www.lrdc.pitt.edu/ebc/2007/competition.html [36] Y. Miyawaki, H. Uchida, O. Yamashita, M. Sato, Y. Morito, H. C. Tanabe, N. Sadato, Y. Kamitani, Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders, Neuron , 60(5), 915–929, 2008. [37] S. Nishimoto, A. T. Vu, T. Naselaris, Y. Benjamini, B. Yu, J. L. Gallant, Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology , 21(19), 1641–1646, 2011. [38] D. J. McFarland, W. a Sarnacki, J. R. Wolpaw, Electroencephalographic (EEG) control of threedimensional movement., Journal of neural engineering, 7(3), 036007, 2010. [39] D. J. McFarland, D. J. Krusienski, W. a Sarnacki, J. R. Wolpaw, Emulation of computer mouse control with a noninvasive brain-computer interface., Journal of neural engineering, 5(2), 101–110, 2008. [40] D. J. Mcfarland, J. R. Wolpaw, Sensorimotor rhytm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis, Journal of Neural Engineering, 5(2), 155–162, 2008. [41] E. V. Friedrich, D. J. McFarland, C. Neuper, T. M. Vaughan, P. Brunner, J. R. Wolpaw, A scanning protocol for sensorimotor rhytm-based brain computer interface, Biological Psychology, 80(2), 169– 175, 2009. [42] D. J. McFarland, J. R. Wolpaw, Brain-Computer Interface Operation of Robotic and Prosthetic Devices, Computer, 41(10), 52–56, 2008. [43] D. J. McFarland, J. R. Wolpaw, Brain – Computer Interfaces for the Operation of Robotic and Prosthetic Devices, Advances in computers, 79, 169–187, 2010. [44] T. J. Bradberry, R. J. Gentili, J. L. Contreras-Vidal, Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals, The Journal of Neuroscience, 30(9), 3432–3437, 2010. [45] T. J. Bradberry, R. J. Gentili, J. L. Contreras-Vidal, Fast attainment of computer cursor control with noninvasively acquired brain signals., Journal of neural engineering, 8(3), 036010, 2011. [46] B. Blankertz, G. Dornhege, M. Krauledat, K. Müller, G. Curio, The non-invasive Berlin Brain – Computer Interface: Fast acquisition of effective performance in untrained subjects, Neuroimage, 37(2), 539–550, 2007. [47] B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Müller, G. Curio, The Berlin Brain-Computer Interface: Machine learning based detection of user specific brain states, J. UCS, 12(6), 581-607, 2006.
There are 29 citations in total.

Details

Subjects Engineering
Journal Section Naturel Sciences
Authors

Yuriy Mıshchenko This is me

Murat Kaya

Mustafa Cömert This is me

Publication Date September 12, 2017
Acceptance Date July 28, 2017
Published in Issue Year 2017 Volume: 10 Issue: 3

Cite

APA Mıshchenko, Y., Kaya, M., & Cömert, M. (2017). BEYİN BİLGİSAYAR ARAYÜZÜ İÇİN DVM MAKİNE ÖĞRENME YÖNTEMİ KULLANILARAK EEG VERİLERİNDEN SAĞ VE SOL EL HAREKET DÜŞÜNCELERİNİN TESPİTİ. TÜBAV Bilim Dergisi, 10(3), 1-20.
ISSN: 1308 - 4941