Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 8 Sayı: 1, 108 - 113, 31.01.2020
https://doi.org/10.17694/bajece.654288

Öz

Kaynakça

  • R. Prueckl, C. Guger, “A Brain-Computer Interface Based onSteady State Visual Evoked Potentials for Controlling a Robot.” 10th International Work-Conference on Artificial Neural Networks, vol.10, no. 12 June, Salamanca,Spain. 2009. doi:https://doi.org/10.1007/978-3-642-02478-8_86
  • A.Luo, T.J. Sullivan, “A User-Friendly SSVEP-Based Brain-Computer Interface using a Time-Domain Classifier.” Journal of Neural Engineering, vol. 7, no. 2, pp. 026010,2010. doi:10.1088/1741-2560/7/2/026010
  • E.C. Lalor, S.P. Kell,C. Finucane, R. Burke,R. Smith, R.B. Reilly, G. McDarby, “Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment.” EURASIP Journal on Applied Signal Processing, vol. 2005, no. 19, pp. 3156-3164,2005. doi: https://doi.org/10.1155/ASP.2005.3156
  • S.P.Kelly,E.C. Lalor, C. Finucane,G. McDarby, R.B. Reilly,“Visual Spatial Attention Control in an Independent Brain-Computer Interface.” IEEE Transactions on Biomedical Engineering, vol. 52 np. 9, pp. 1588-1596, 2005. doi: 10.1109/TBME.2005.851510
  • G.R. Muller-Putz, G. Pfurtscheller,“Control of an Electrical Prosthesis with an SSVEP-Based BCI.” IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 361-364, 2008. doi: 10.1109/TBME.2007.897815
  • G. Bin, X. Gao, Z. Yan, B. Hong,S. Gao,“An Online Multi-Channel SSVEP-Based Brain-Computer Interface using a Canonical Correlation Analysis Method. Journal of Neural Engineering”, vol. 6, no. 4, pp. 046002, 2009. doi: 10.1088/1741-2560/6/4/046002
  • I. Volosyak, “SSVEP-Based Bremen-BCI Interface – Boosting Information Transfer Rates.” Journal of Neural Engineering, vol. 8, no.3, pp. 036020, 2011. doi: 10.1088/1741-2560/8/3/036020
  • J. Long,Y.Li, H. Wang, T. Yu., J. Pan, F. Li,“A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 5, pp. 720-729, 2012. doi: 10.1109/TNSRE.2012.2197221
  • P. Lee, H. Chang, T. Hsieh, H. Deng, C. Sun,“A Brain-Wave- Actuated Small Robot Car using Ensemble Empirical Mode Decomposition- Based Approach.” IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems And Humans, vol. 42, no. 5, pp. 1053-1064, 2012. doi:10.1109/TSMCA.2012.2187184
  • Y. Zhang, G. Zhou, J. Jin, X. Wang, A. Cichocki,“SSVEP Recognition using Common Feature Analysis in Brain-Computer Interface.”Journal of Neuroscience Methods, vol. 244, pp. 8-15, 2015. doi: 10.1016/j.jneumeth.2014.03.012
  • T. Sakurada, T. Kawase, T. Komatsu, K. Kansaku,“Use of High-Frequency Visual Stimuli Above the Critical Flicker Frequency in a SSVEPBased BMI.” Clinical Neurophysiology, vol. 126, no. 10, pp. 1972-1978, 2015. doi: 10.1016/j.clinph.2014.12.010
  • K. Hasan, S. Hossain, T.K. Ghosh, M. Ahmad,“A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention.” American Journal of Bioscience and Bioengineering, vol. 3, no. 3-1, pp. 27-33, 2015. doi: 10.11648/j.bio.s.2015030301.15
  • F. Gembler, P.Stawicki, I.Volosyak,“A Comparison of SSVEP-Based BCI-Performance Between Different Age Groups.” 13th International Work-Conference on Artificial Neural Networks, 10-12 June, Palma de Mallorca, Spain, 2015. doi: https://doi.org/10.1007/978-3-319-19258-1_6
  • A. Widmann, E. Schröger, B. Maess,“Digital Filter Design for Electrophysiological Data - A Practical Approach. Journal of Neuroscience Methods”, vol.250, pp. 34-46, 2014. doi: 10.1016/j.jneumeth.2014.08.002
  • R. Vanrullen,“Four Common Conceptual Fallacies in Mapping the Time Course of Recognition.” Frontiers in Psychology, vol. 2, Article365, 2011.doi: 10.3389/fpsyg.2011.00365
  • L.F. Nicolas-Alonso, J. Gomez-Gil, “Brain Computer Interfaces, a Review.” Sensors, vol. 12, no. 2, pp. 1211-1279, 2012. doi: 10.3390/s120201211
  • F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, B. Arnaldi,“A Review of Classification Algorithms for EEG-Based Brain-ComputerInterfaces.” Journal of Neural Engineering, vol. 4 no. 2, R1, 2007. doi: 10.1088/1741-2552/aab2f2
  • I. Volosyak,“SSVEP-Based Bremen-BCI Interface - Boosting Information Transfer Rates.” Journal of Neural Engineering, vol. 8, no. 3, pp. 036020, 2011. doi: 10.1088/1741-2560/8/3/036020
  • E.C. Lalor, S.P. Kelly, C. Finucane, R. Burke, R. Smith, R.B. Reilly, G. McDarby,“Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment.”EURASIP Journalon Applied Signal Processing, vol. 2005 no. 19, pp. 3156-3164, 2005. doi:10.1155/ASP.2005.3156
  • P. Lee, H. Chang, T. Hsieh, H. Deng, C. Sun, “A Brain-Wave- Actuated Small Robot Car using Ensemble EmpiricalModeDecomposition-Based Approach.” IEEE Transactions on Systems, Man, and Cybernetics -PartA: Systems And Humans, vol. 42, no. 5, pp. 1053-1064, 2012. doi: 10.1109/TSMCA.2012.2187184
  • J.R. Wolpaw, E.W. Wolpaw, “Brain-Computer Interfaces Principles and Practice, Oxford University Press, Inc.”, New York,USA, 2012. doi: 10.1093/acprof:oso/9780195388855.001.0001
  • Y. Wang, R. Wang, X. Gao, B. Hong, S. Gao,“A Practical VEP- Based Brain-Computer Interface.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, 234-239, 2006. doi: 10.1109/TNSRE.2006.875576
  • H. Gollee, I. Volosyak, A.J. McLachlan, K.J. Hunt, A. Graser,“An SSVEP-Based Brain-Computer Interface for the Control of Functional Electrical Stimulation.” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, pp. 1847-1855, 2010. doi:10.1109/TBME.2010.2043432

Effects of Digital Filtering on the Classification Performance of Steady-State Visual Evoked Potential Based Brain-Computer Interfaces

Yıl 2020, Cilt: 8 Sayı: 1, 108 - 113, 31.01.2020
https://doi.org/10.17694/bajece.654288

Öz

The electrical activity that occurs during the communication of neurons is recorded by a method called electroencephalography. Brain computer interfaces utilize various electrophysiological sources obtained from different regions of the brain. The electrophysiological source used in this study is the electrical activity seen in the occipital lobes as a result of visual stimuli that flicker at certain frequencies, and is called steady-state visual evoked potential. The main goal in this work is not to try to improve the classification performance but to investigate the effects of different digital filtering algorithms on classification performance. The effects of the high pass and low pass filtering on the classification performance in steady-state visual evoked potential based brain computer interfaces are investigated. As a result of this study, no significant change in the classification performances of designs with only high pass filtering, and high and low pass filtering, has been observed. In addition, it has been observed that only the designs include a high-pass filter implementation give better classification performance in many cases. Consequently, it is concluded that low-pass filtering in steady-state visual evoked potential based brain-computer interfaces does not provide the desired contribution to classification performance.

Kaynakça

  • R. Prueckl, C. Guger, “A Brain-Computer Interface Based onSteady State Visual Evoked Potentials for Controlling a Robot.” 10th International Work-Conference on Artificial Neural Networks, vol.10, no. 12 June, Salamanca,Spain. 2009. doi:https://doi.org/10.1007/978-3-642-02478-8_86
  • A.Luo, T.J. Sullivan, “A User-Friendly SSVEP-Based Brain-Computer Interface using a Time-Domain Classifier.” Journal of Neural Engineering, vol. 7, no. 2, pp. 026010,2010. doi:10.1088/1741-2560/7/2/026010
  • E.C. Lalor, S.P. Kell,C. Finucane, R. Burke,R. Smith, R.B. Reilly, G. McDarby, “Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment.” EURASIP Journal on Applied Signal Processing, vol. 2005, no. 19, pp. 3156-3164,2005. doi: https://doi.org/10.1155/ASP.2005.3156
  • S.P.Kelly,E.C. Lalor, C. Finucane,G. McDarby, R.B. Reilly,“Visual Spatial Attention Control in an Independent Brain-Computer Interface.” IEEE Transactions on Biomedical Engineering, vol. 52 np. 9, pp. 1588-1596, 2005. doi: 10.1109/TBME.2005.851510
  • G.R. Muller-Putz, G. Pfurtscheller,“Control of an Electrical Prosthesis with an SSVEP-Based BCI.” IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 361-364, 2008. doi: 10.1109/TBME.2007.897815
  • G. Bin, X. Gao, Z. Yan, B. Hong,S. Gao,“An Online Multi-Channel SSVEP-Based Brain-Computer Interface using a Canonical Correlation Analysis Method. Journal of Neural Engineering”, vol. 6, no. 4, pp. 046002, 2009. doi: 10.1088/1741-2560/6/4/046002
  • I. Volosyak, “SSVEP-Based Bremen-BCI Interface – Boosting Information Transfer Rates.” Journal of Neural Engineering, vol. 8, no.3, pp. 036020, 2011. doi: 10.1088/1741-2560/8/3/036020
  • J. Long,Y.Li, H. Wang, T. Yu., J. Pan, F. Li,“A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 5, pp. 720-729, 2012. doi: 10.1109/TNSRE.2012.2197221
  • P. Lee, H. Chang, T. Hsieh, H. Deng, C. Sun,“A Brain-Wave- Actuated Small Robot Car using Ensemble Empirical Mode Decomposition- Based Approach.” IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems And Humans, vol. 42, no. 5, pp. 1053-1064, 2012. doi:10.1109/TSMCA.2012.2187184
  • Y. Zhang, G. Zhou, J. Jin, X. Wang, A. Cichocki,“SSVEP Recognition using Common Feature Analysis in Brain-Computer Interface.”Journal of Neuroscience Methods, vol. 244, pp. 8-15, 2015. doi: 10.1016/j.jneumeth.2014.03.012
  • T. Sakurada, T. Kawase, T. Komatsu, K. Kansaku,“Use of High-Frequency Visual Stimuli Above the Critical Flicker Frequency in a SSVEPBased BMI.” Clinical Neurophysiology, vol. 126, no. 10, pp. 1972-1978, 2015. doi: 10.1016/j.clinph.2014.12.010
  • K. Hasan, S. Hossain, T.K. Ghosh, M. Ahmad,“A SSVEP Based EEG Signal Analysis to Discriminate the Effects of Music Levels on Executional Attention.” American Journal of Bioscience and Bioengineering, vol. 3, no. 3-1, pp. 27-33, 2015. doi: 10.11648/j.bio.s.2015030301.15
  • F. Gembler, P.Stawicki, I.Volosyak,“A Comparison of SSVEP-Based BCI-Performance Between Different Age Groups.” 13th International Work-Conference on Artificial Neural Networks, 10-12 June, Palma de Mallorca, Spain, 2015. doi: https://doi.org/10.1007/978-3-319-19258-1_6
  • A. Widmann, E. Schröger, B. Maess,“Digital Filter Design for Electrophysiological Data - A Practical Approach. Journal of Neuroscience Methods”, vol.250, pp. 34-46, 2014. doi: 10.1016/j.jneumeth.2014.08.002
  • R. Vanrullen,“Four Common Conceptual Fallacies in Mapping the Time Course of Recognition.” Frontiers in Psychology, vol. 2, Article365, 2011.doi: 10.3389/fpsyg.2011.00365
  • L.F. Nicolas-Alonso, J. Gomez-Gil, “Brain Computer Interfaces, a Review.” Sensors, vol. 12, no. 2, pp. 1211-1279, 2012. doi: 10.3390/s120201211
  • F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, B. Arnaldi,“A Review of Classification Algorithms for EEG-Based Brain-ComputerInterfaces.” Journal of Neural Engineering, vol. 4 no. 2, R1, 2007. doi: 10.1088/1741-2552/aab2f2
  • I. Volosyak,“SSVEP-Based Bremen-BCI Interface - Boosting Information Transfer Rates.” Journal of Neural Engineering, vol. 8, no. 3, pp. 036020, 2011. doi: 10.1088/1741-2560/8/3/036020
  • E.C. Lalor, S.P. Kelly, C. Finucane, R. Burke, R. Smith, R.B. Reilly, G. McDarby,“Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment.”EURASIP Journalon Applied Signal Processing, vol. 2005 no. 19, pp. 3156-3164, 2005. doi:10.1155/ASP.2005.3156
  • P. Lee, H. Chang, T. Hsieh, H. Deng, C. Sun, “A Brain-Wave- Actuated Small Robot Car using Ensemble EmpiricalModeDecomposition-Based Approach.” IEEE Transactions on Systems, Man, and Cybernetics -PartA: Systems And Humans, vol. 42, no. 5, pp. 1053-1064, 2012. doi: 10.1109/TSMCA.2012.2187184
  • J.R. Wolpaw, E.W. Wolpaw, “Brain-Computer Interfaces Principles and Practice, Oxford University Press, Inc.”, New York,USA, 2012. doi: 10.1093/acprof:oso/9780195388855.001.0001
  • Y. Wang, R. Wang, X. Gao, B. Hong, S. Gao,“A Practical VEP- Based Brain-Computer Interface.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, 234-239, 2006. doi: 10.1109/TNSRE.2006.875576
  • H. Gollee, I. Volosyak, A.J. McLachlan, K.J. Hunt, A. Graser,“An SSVEP-Based Brain-Computer Interface for the Control of Functional Electrical Stimulation.” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, pp. 1847-1855, 2010. doi:10.1109/TBME.2010.2043432
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Volkan Çetin 0000-0003-3864-9246

Serhat Özekes 0000-0002-7432-0272

Huseyin Selcuk Varol 0000-0002-3968-4230

Yayımlanma Tarihi 31 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

APA Çetin, V., Özekes, S., & Varol, H. S. (2020). Effects of Digital Filtering on the Classification Performance of Steady-State Visual Evoked Potential Based Brain-Computer Interfaces. Balkan Journal of Electrical and Computer Engineering, 8(1), 108-113. https://doi.org/10.17694/bajece.654288

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