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.
[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.
[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.
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.