Year 2017,
Volume: 2 Issue: 2, 37 - 49, 20.06.2017
Apdullah Yayık
,
Yakup Kutlu
References
- Ashari, R. B., Al-Bidewi, I. A., & Kamel, M. I. (2011). Design and simulation of virtual telephone keypad control based on brain computer interface (BCI) with very high transfer rates. Alexandria Engineering Journal, 50(1), 49–56.
- Association, W. M., & others. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bulletin of the World Health Organization, 79(4), 373.
- Bai, L., Yu, T., & Li, Y. (2015). A brain computer interface-based explorer. Journal of Neuroscience Methods, 244, 2–7.
Berlad, I., & Pratt, H. (1995). P300 in response to the subject’s own name. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 96(5), 472–474.
- Blasco, J. L. S., Iáñez, E., Ubeda, A., & Azor’\in, J. M. (2012). Visual evoked potential-based brain--machine interface applications to assist disabled people. Expert Systems with Applications, 39(9), 7908–7918.
- Borghetti, D., Bruni, A., Fabbrini, M., Murri, L., & Sartucci, F. (2007). A low-cost interface for control of computer functions by means of eye movements. Computers in Biology and Medicine, 37(12), 1765–1770.
- Bougrain, L., Duvinage, M., & Klein, E. (2012). Inverse reinforcement learning to control a robotic arm using a Brain-Computer Interface. Research Report, 1–6.
- Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436.
- Butterworth, S. (1930). On the theory of filter amplifiers. Wireless Engineer, 7(6), 536–541.
- Citi, L., Poli, R., Cinel, C., & Sepulveda, F. (2008). P300-Based BCI Mouse With Genetically-Optimized Analogue Control. IEEE Transactions On Neural Systems And Rehabilitation Engineering, 16(1), 51–61.
- Combaz, A., & Van Hulle, M. M. (2015). Simultaneous Detection of P300 and Steady-State Visually Evoked Potentials for Hybrid Brain-Computer Interface. Plos One, 10(3), e0121481.
- Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.
- Donnerer, M., & Steed, A. (2010). Using a P300 Brain-Computer Interface in an Immersive Virtual Environment. Presence Teleoperators and Virtual Environments, 19(1), 12–24.
- Duda, R., Hart, P., & D.G., S. (2012). Pattern Classification.
- Esfahani, E. T., & Sundararajan, V. (2012). Classification of primitive shapes using brain--computer interfaces. Computer-Aided Design, 44(10), 1011–1019.
- Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prothesis utilizing event-relatedpotencials. Electroencephalographic Clinical Neurophysiology, 70(6), 510–523.
- Fazel-Rezai, R. (2007). Human error in P300 speller paradigm for brain-computer interface. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 2516–2519).
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.
- Handy, T. C. (2005). Event-related potentials: A methods handbook. MIT press.
- Hoffmann, U., Vesin, J., Ebrahimi, T., & Diserens, K. (2007). An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods, 167(1), 115–125.
- Hyvärinen, A., Karhunen, J., & Oja, E. (2004). Independent component analysis (Vol. 46). John Wiley & Sons.
- İcscan, Z., & Dokur, Z. (2014). A novel steady-state visually evoked potential-based brain--computer interface design: character plotter. Biomedical Signal Processing and Control, 10, 145–152.
- Jin, J., Allison, B. Z., Wang, X., & Neuper, C. (2012). A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials. Journal of Neuroscience Methods, 205(2), 265–276. https://doi.org/10.1016/j.jneumeth.2012.01.004.
- Kaplan, A. Y., Shishkin, S. L., Ganin, I. P., Basyul, I. A., & Zhigalov, A. Y. (2013). Adapting the P300-based brain--computer interface for gaming: a review. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 141–149.
- Kaufmann, T., & Hammer, E. M. (n.d.). ERPs contributing to classification in the ” P300 ” BCI, 49–52.
- Kolev, V., Demiralp, T., Yordanova, J., Ademoglu, A., & Isoglu-Alkaç, Ü. (1997). Time-frequency analysis reveals multiple functional components during oddball P300. NeuroReport, 8(8), 2061–2065.
- Koo, B., Nam, Y., & Choi, S. (2014). A hybrid EOG-P300 BCI with dual monitors. 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014.
- Kutlu, Y., Yayik, A., Yildirim, E., & Yildirim, S. (2015). Orthogonal Extreme Learning Machine Based P300 Visual Event-Related BCI. In International Conference on Neural Information Processing (pp. 284–291). İstanbul: Springer International Publishing.
- Long, J., Li, Y., Yu, T., & Gu, Z. (2012). Target selection with hybrid feature for BCI-based 2-D cursor control. IEEE Transactions on Biomedical Engineering, 59(1), 132–140.
- Makeig, S., Bell, A. J., Jung, T.-P., Sejnowski, T. J., & others. (1996). Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151.
- Masson, P., & Berger, L. (1924). Epitheliomas a double metaplasie de la parotide. Bull Assoc Fr Etude Cancer, 13, 366–373.
- Mesulam, M., & others. (2000). Principles of behavioral and cognitive neurology. Oxford University Press.
- Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop. (pp. 41–48).
- Postelnicu, C. C., Talaba, D., & Toma, M. I. (2011). Controlling a robotic arm by brainwaves and eye movement. IFIP Advances in Information and Communication Technology, 349 AICT, 157–164.
- Potentials, A. B. (2011). Robot Control Using Anticipatory Brain Potentials. Automatika--Journal for Control, Measurement, Electronics, Computing and Communications, 52(1), 20–30.
- Rakotomamonjy, A., & Guigue, V. (2008). BCI Competition III : Dataset II - Ensemble of SVMs for BCI P300 Speller. Biomedical Engineering, IEEE Transactions on, 55(3), 1147–1154.
- Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., … Lécuyer, A. (2010). OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators and Virtual Environments, 19(1), 35–53.
- Rivet, B., Souloumiac, A., Gibert, G., & Attina, V. (2008). “P300 Speller” Brain-Computer Interface: Enhancement of P300 Evoked Potential By Spatial Filters. In 16th European Signal Processing Conference (pp. 1–5).
Ron-angevin, R., & Silva-sauer, L. (2006). Proposal of a P300-based BCI speller using a predictive text system. Scriperpress.
- Shi, J., Shen, J., Ji, Y., & Du, F. (2012). A submatrix-based P300 brain-computer interface stimulus presentation paradigm. Journal of Zhejiang University-Science C-Computers & Electronics, 13(6), 452–459. https://doi.org/10.1631/jzus.C1100328
- Su, Y., Dai, J., Liu, X., Xu, Q., Zhuang, Y., Chen, W., & Zheng, X. (2010). EEG Channel Evaluation and Selection by Rough Set in P300 BCI. Journal of Computational Information Systems, 6, 1727–1735.
- Subaşıcıoğlu, F. (2008). A Research on “Disability Awareness” in Information and Records Management Departments of Universities. Bilgi Düyası, 9(2), 399–430.
- Surdilovic, T., & Zhang, Y.-Q. (2006). Convenient intelligent cursor control web systems for Internet users with severe motor-impairments. International Journal of Medical Informatics, 75(1), 86–100.
- Sutton, S., Braten, M., Zubin, J., & John, E. R. (1965). Evoked-potentials correlates of stimulus uncertainty. Science, 150(3700), 1187–1188.
- Townsend, G., LaPallo, B. K., Boulay, C. B., Krusienski, D. J., Frye, G. E., Hauser, Ck., … Sellers, E. W. (2010). A novel P300-based brain--computer interface stimulus presentation paradigm: moving beyond rows and columns. Clinical Neurophysiology, 121(7), 1109–1120.
- Tsuda, M., Lang, Y., & Wu, H. (2014). Analysis and Identification of the EEG Signals from Visual Stimulation. Procedia Computer Science, 35, 1292–1299.
- Velasco-Álvarez, F., Ron-Angevin, R., da Silva-Sauer, L., & Sancha-Ros, S. (2013). Audio-cued motor imagery-based brain--computer interface: Navigation through virtual and real environments. Neurocomputing, 121, 89–98.
- Vourvopoulos, A., & Liarokapis, F. (2014). Evaluation of commercial brain--computer interfaces in real and virtual world environment: A pilot study. Computers & Electrical Engineering, 40(2), 714–729.
- Walter, W., Cooper, R., Aldridge, V., & McCallum, W. (1964). Contingent negative variation: an electric sign of sensori-motor association and expectancy in the human brain. Nature.
Online LDA based brain-computer interface system to aid disabled people
Year 2017,
Volume: 2 Issue: 2, 37 - 49, 20.06.2017
Apdullah Yayık
,
Yakup Kutlu
Abstract
This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance.
References
- Ashari, R. B., Al-Bidewi, I. A., & Kamel, M. I. (2011). Design and simulation of virtual telephone keypad control based on brain computer interface (BCI) with very high transfer rates. Alexandria Engineering Journal, 50(1), 49–56.
- Association, W. M., & others. (2001). World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bulletin of the World Health Organization, 79(4), 373.
- Bai, L., Yu, T., & Li, Y. (2015). A brain computer interface-based explorer. Journal of Neuroscience Methods, 244, 2–7.
Berlad, I., & Pratt, H. (1995). P300 in response to the subject’s own name. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 96(5), 472–474.
- Blasco, J. L. S., Iáñez, E., Ubeda, A., & Azor’\in, J. M. (2012). Visual evoked potential-based brain--machine interface applications to assist disabled people. Expert Systems with Applications, 39(9), 7908–7918.
- Borghetti, D., Bruni, A., Fabbrini, M., Murri, L., & Sartucci, F. (2007). A low-cost interface for control of computer functions by means of eye movements. Computers in Biology and Medicine, 37(12), 1765–1770.
- Bougrain, L., Duvinage, M., & Klein, E. (2012). Inverse reinforcement learning to control a robotic arm using a Brain-Computer Interface. Research Report, 1–6.
- Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436.
- Butterworth, S. (1930). On the theory of filter amplifiers. Wireless Engineer, 7(6), 536–541.
- Citi, L., Poli, R., Cinel, C., & Sepulveda, F. (2008). P300-Based BCI Mouse With Genetically-Optimized Analogue Control. IEEE Transactions On Neural Systems And Rehabilitation Engineering, 16(1), 51–61.
- Combaz, A., & Van Hulle, M. M. (2015). Simultaneous Detection of P300 and Steady-State Visually Evoked Potentials for Hybrid Brain-Computer Interface. Plos One, 10(3), e0121481.
- Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.
- Donnerer, M., & Steed, A. (2010). Using a P300 Brain-Computer Interface in an Immersive Virtual Environment. Presence Teleoperators and Virtual Environments, 19(1), 12–24.
- Duda, R., Hart, P., & D.G., S. (2012). Pattern Classification.
- Esfahani, E. T., & Sundararajan, V. (2012). Classification of primitive shapes using brain--computer interfaces. Computer-Aided Design, 44(10), 1011–1019.
- Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prothesis utilizing event-relatedpotencials. Electroencephalographic Clinical Neurophysiology, 70(6), 510–523.
- Fazel-Rezai, R. (2007). Human error in P300 speller paradigm for brain-computer interface. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 2516–2519).
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.
- Handy, T. C. (2005). Event-related potentials: A methods handbook. MIT press.
- Hoffmann, U., Vesin, J., Ebrahimi, T., & Diserens, K. (2007). An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods, 167(1), 115–125.
- Hyvärinen, A., Karhunen, J., & Oja, E. (2004). Independent component analysis (Vol. 46). John Wiley & Sons.
- İcscan, Z., & Dokur, Z. (2014). A novel steady-state visually evoked potential-based brain--computer interface design: character plotter. Biomedical Signal Processing and Control, 10, 145–152.
- Jin, J., Allison, B. Z., Wang, X., & Neuper, C. (2012). A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials. Journal of Neuroscience Methods, 205(2), 265–276. https://doi.org/10.1016/j.jneumeth.2012.01.004.
- Kaplan, A. Y., Shishkin, S. L., Ganin, I. P., Basyul, I. A., & Zhigalov, A. Y. (2013). Adapting the P300-based brain--computer interface for gaming: a review. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 141–149.
- Kaufmann, T., & Hammer, E. M. (n.d.). ERPs contributing to classification in the ” P300 ” BCI, 49–52.
- Kolev, V., Demiralp, T., Yordanova, J., Ademoglu, A., & Isoglu-Alkaç, Ü. (1997). Time-frequency analysis reveals multiple functional components during oddball P300. NeuroReport, 8(8), 2061–2065.
- Koo, B., Nam, Y., & Choi, S. (2014). A hybrid EOG-P300 BCI with dual monitors. 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014.
- Kutlu, Y., Yayik, A., Yildirim, E., & Yildirim, S. (2015). Orthogonal Extreme Learning Machine Based P300 Visual Event-Related BCI. In International Conference on Neural Information Processing (pp. 284–291). İstanbul: Springer International Publishing.
- Long, J., Li, Y., Yu, T., & Gu, Z. (2012). Target selection with hybrid feature for BCI-based 2-D cursor control. IEEE Transactions on Biomedical Engineering, 59(1), 132–140.
- Makeig, S., Bell, A. J., Jung, T.-P., Sejnowski, T. J., & others. (1996). Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151.
- Masson, P., & Berger, L. (1924). Epitheliomas a double metaplasie de la parotide. Bull Assoc Fr Etude Cancer, 13, 366–373.
- Mesulam, M., & others. (2000). Principles of behavioral and cognitive neurology. Oxford University Press.
- Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop. (pp. 41–48).
- Postelnicu, C. C., Talaba, D., & Toma, M. I. (2011). Controlling a robotic arm by brainwaves and eye movement. IFIP Advances in Information and Communication Technology, 349 AICT, 157–164.
- Potentials, A. B. (2011). Robot Control Using Anticipatory Brain Potentials. Automatika--Journal for Control, Measurement, Electronics, Computing and Communications, 52(1), 20–30.
- Rakotomamonjy, A., & Guigue, V. (2008). BCI Competition III : Dataset II - Ensemble of SVMs for BCI P300 Speller. Biomedical Engineering, IEEE Transactions on, 55(3), 1147–1154.
- Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., … Lécuyer, A. (2010). OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence: Teleoperators and Virtual Environments, 19(1), 35–53.
- Rivet, B., Souloumiac, A., Gibert, G., & Attina, V. (2008). “P300 Speller” Brain-Computer Interface: Enhancement of P300 Evoked Potential By Spatial Filters. In 16th European Signal Processing Conference (pp. 1–5).
Ron-angevin, R., & Silva-sauer, L. (2006). Proposal of a P300-based BCI speller using a predictive text system. Scriperpress.
- Shi, J., Shen, J., Ji, Y., & Du, F. (2012). A submatrix-based P300 brain-computer interface stimulus presentation paradigm. Journal of Zhejiang University-Science C-Computers & Electronics, 13(6), 452–459. https://doi.org/10.1631/jzus.C1100328
- Su, Y., Dai, J., Liu, X., Xu, Q., Zhuang, Y., Chen, W., & Zheng, X. (2010). EEG Channel Evaluation and Selection by Rough Set in P300 BCI. Journal of Computational Information Systems, 6, 1727–1735.
- Subaşıcıoğlu, F. (2008). A Research on “Disability Awareness” in Information and Records Management Departments of Universities. Bilgi Düyası, 9(2), 399–430.
- Surdilovic, T., & Zhang, Y.-Q. (2006). Convenient intelligent cursor control web systems for Internet users with severe motor-impairments. International Journal of Medical Informatics, 75(1), 86–100.
- Sutton, S., Braten, M., Zubin, J., & John, E. R. (1965). Evoked-potentials correlates of stimulus uncertainty. Science, 150(3700), 1187–1188.
- Townsend, G., LaPallo, B. K., Boulay, C. B., Krusienski, D. J., Frye, G. E., Hauser, Ck., … Sellers, E. W. (2010). A novel P300-based brain--computer interface stimulus presentation paradigm: moving beyond rows and columns. Clinical Neurophysiology, 121(7), 1109–1120.
- Tsuda, M., Lang, Y., & Wu, H. (2014). Analysis and Identification of the EEG Signals from Visual Stimulation. Procedia Computer Science, 35, 1292–1299.
- Velasco-Álvarez, F., Ron-Angevin, R., da Silva-Sauer, L., & Sancha-Ros, S. (2013). Audio-cued motor imagery-based brain--computer interface: Navigation through virtual and real environments. Neurocomputing, 121, 89–98.
- Vourvopoulos, A., & Liarokapis, F. (2014). Evaluation of commercial brain--computer interfaces in real and virtual world environment: A pilot study. Computers & Electrical Engineering, 40(2), 714–729.
- Walter, W., Cooper, R., Aldridge, V., & McCallum, W. (1964). Contingent negative variation: an electric sign of sensori-motor association and expectancy in the human brain. Nature.