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EEG Verileri Kullanılarak Fiziksel El Hareketleri ve Bu Hareketlerin Hayalinin Yapay Sinir Ağları İle Sınıflandırılması

Year 2018, Volume: 1 Issue: 2, 1 - 9, 01.08.2018

Mustafa Tosun , Mustafa Erginli , Ömer Kasım , Burak Uğraş Şems Tanrıverdi Tayfun Kavak

https://doi.org/10.35377/saucis.01.02.443999
Cited By: 3

Abstract

Son yıllarda teknolojinin gelişmesi sonucunda beyin bilgisayar arayüzü ile ilgili çalışmalar artmıştır. Beyin Bilgisayar Arayüzü (Brain Computer Interface-BCI) yöntemlerinde Elektroansefalogram (Electroencephalogram-EEG) işaretleri yaygın olarak kullanılmaktadır. EEG verileri kullanılarak fiziksel hareketle hareketin hayali sınıflandırılabilmektedir. Bu çalışmada sağ elini kullanan ve hastalık durumu olmayan 21 yaşındaki bir erkeğe ait EEG verileri kullanılmıştır. Bu verilerin bir kısmı sol ve sağ elin ileri-geri hareketi esnasında kaydedilen EEG verileridir. Diğer veriler ise herhangi bir fiziksel hareket yapılmadan, hareketin hayal edilmesi durumu ile ilgili kayıtlardır. Welch metodu kullanılarak EEG verilerinin 1-48 Hz arasındaki frekanslarının güç yoğunlukları hesaplanmıştır. Elde edilen veri setleri tasarlanan Geri Yayılımlı Sinir Ağı (Backpropagation Neural Network- BPNN) ‘ na uygulanmıştır. Ağın eğitimi sonunda 4.6731x10-23 ortalama karesel hata (Mean Squared Error -MSE) değerine ulaşılmıştır. Hayal ile hareket verilerinden oluşan test veri seti eğitilen ağa uygulandığında, hayal ile hareket verileri % 99.9975 doğrulukla sınıflandırılmıştır.

References

  • [1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain–computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, no. 6, pp. 767-791, 2002/06/01/ 2002.
  • [2] J. B. F. Van Erp, F. Lotte, and M. Tangermann, "Brain-computer interfaces: Beyond medical applications," Computer, Article vol. 45, no. 4, pp. 26-34, 2012, Art. no. 6165246.
  • [3] S. N. Abdulkader, A. Atia, and M.-S. M. Mostafa, "Brain computer interfacing: Applications and challenges," Egyptian Informatics Journal, vol. 16, no. 2, pp. 213-230, 2015/07/01/ 2015.
  • [4] R. A. Andersen, S. Musallam, and B. Pesaran, "Selecting the signals for a brain–machine interface," Current opinion in neurobiology, vol. 14, no. 6, pp. 720-726, 2004.
  • [5] E. Yazgan and M. Korürek, Tıp elektroniği. İTÜ, 1996.
  • [6] M. Tosun and R. Güntürkün, "Anesthetic gas control with neuro-fuzzy system in anesthesia," Expert Systems with Applications, vol. 37, no. 3, pp. 2690-2695, 2010.
  • [7] S. K. Bashar, A. R. Hassan, and M. I. H. Bhuiyan, "Identification of motor imagery movements from eeg signals using dual tree complex wavelet transform," in Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on, 2015, pp. 290-296: IEEE.
  • [8] Y.-H. Liu, C.-A. Cheng, and H.-P. Huang, "Novel feature of the EEG based motor imagery BCI system: Degree of imagery," in System Science and Engineering (ICSSE), 2011 International Conference on, 2011, pp. 515-520: IEEE.
  • [9] K. Saka, Ö. Aydemir, and M. Öztürk, "Classification of EEG signals recorded during right/left hand movement imagery using Fast Walsh Hadamard Transform based features," in Telecommunications and Signal Processing (TSP), 2016 39th International Conference on, 2016, pp. 413-416: IEEE.
  • [10] S. Bhattacharyya, A. Khasnobish, A. Konar, D. Tibarewala, and A. K. Nagar, "Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms," in Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on, 2011, pp. 1-8: IEEE.
  • [11] A. H. Do, P. T. Wang, C. E. King, A. Abiri, and Z. Nenadic, "Brain-computer interface controlled functional electrical stimulation system for ankle movement," Journal of neuroengineering and rehabilitation, vol. 8, no. 1, p. 49, 2011.
  • [12] M. S. Mabrouk, "Non-Invasive EEG-based BCI system for Left or Right Hand Movement," Majlesi Journal of Electrical Engineering, vol. 5, no. 3, 2011.
  • [13] C. Liu, H. Wang, H. Pu, Y. Zhang, and L. Zou, "EEG feature extraction and pattern recognition during right and left hands motor imagery in brain-computer interface," in 2012 5th International Conference on BioMedical Engineering and Informatics, 2012, pp. 506-510.
  • [14] N. Robinson and A. P. Vinod, "Bi-Directional Imagined Hand Movement Classification Using Low Cost EEG-Based BCI," in 2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015, pp. 3134-3139.
  • [15] M. Hajibabazadeh and V. Azimirad, "Brain-robot interface: Distinguishing left and right hand EEG signals through SVM," in 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), 2014, pp. 813-816.
  • [16] E. Öztemel, "Yapay Sinir Ağlari," PapatyaYayincilik, Istanbul, 2003.
  • [17] A. Abraham, "129: Artificial Neural Networks," Handbook of Measuring System Design, 2005.
  • [18] M. Tosun, A. Ferikoğlu, R. Güntürkün, and C. Ünal, "Control of sevoflurane anesthetic agent via neural network using electroencephalogram signals during anesthesia," Journal of medical systems, vol. 36, no. 2, pp. 451-456, 2012.
  • [19] P. Welch, "The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms," IEEE Transactions on audio and electroacoustics, vol. 15, no. 2, pp. 70-73, 1967.
  • [20] A. S. Al-Fahoum and A. A. Al-Fraihat, "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains," ISRN neuroscience, vol. 2014, 2014.
  • [21] J. G. Proakis and D. G. Manolakis, "Power spectrum estimation," Digital signal processing; principles, algorithms, and applications, 3rd edn. Prentice Hall, New Jersey, 1996.
  • [22] (15.06.2018). Project BCI - EEG Motor Activity Data Set. Available: https://sites.google.com/site/projectbci/

EEG Verileri Kullanılarak Fiziksel El Hareketleri ve Bu Hareketlerin Hayalinin Yapay Sinir Ağları İle Sınıflandırılması

Year 2018, Volume: 1 Issue: 2, 1 - 9, 01.08.2018

Mustafa Tosun , Mustafa Erginli , Ömer Kasım , Burak Uğraş Şems Tanrıverdi Tayfun Kavak

https://doi.org/10.35377/saucis.01.02.443999
Cited By: 3

Abstract

In recent years, as a result of the technological development, there has been a significance improvement on the computer interface. Electroencephalogram (EEG) signals are widely used in Brain Computer Interface (BCI) methods. By using EEG data, the imagination of movement with physical motion can be classified. In this study, EEG data of a 21-years-old man who used his right hand and who didn’t show any disease symptom was used. Part of this EEG data demonstrates the recordings of forward and backward movement of the left and right hand. The other data indicates the records of imagination of motion without any physical movement. Using the Welch method, the power densities of the frequencies of 1-48 Hz of the EEG data were calculated. The obtained data sets were applied to the designed Back Propagation Neural Network (BPNN). At the end of the network training, the Mean Squared Error (MSE) value of 4.6731x10-23 has been reached. When the test data set, which consists of imaginary and motion data is applied to the trained network, imagination and motion data are classified with accuracy of 99.9975%.

Keywords

EEG backpropagation neural network welch method signal processing

References

  • [1] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain–computer interfaces for communication and control," Clinical Neurophysiology, vol. 113, no. 6, pp. 767-791, 2002/06/01/ 2002.
  • [2] J. B. F. Van Erp, F. Lotte, and M. Tangermann, "Brain-computer interfaces: Beyond medical applications," Computer, Article vol. 45, no. 4, pp. 26-34, 2012, Art. no. 6165246.
  • [3] S. N. Abdulkader, A. Atia, and M.-S. M. Mostafa, "Brain computer interfacing: Applications and challenges," Egyptian Informatics Journal, vol. 16, no. 2, pp. 213-230, 2015/07/01/ 2015.
  • [4] R. A. Andersen, S. Musallam, and B. Pesaran, "Selecting the signals for a brain–machine interface," Current opinion in neurobiology, vol. 14, no. 6, pp. 720-726, 2004.
  • [5] E. Yazgan and M. Korürek, Tıp elektroniği. İTÜ, 1996.
  • [6] M. Tosun and R. Güntürkün, "Anesthetic gas control with neuro-fuzzy system in anesthesia," Expert Systems with Applications, vol. 37, no. 3, pp. 2690-2695, 2010.
  • [7] S. K. Bashar, A. R. Hassan, and M. I. H. Bhuiyan, "Identification of motor imagery movements from eeg signals using dual tree complex wavelet transform," in Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on, 2015, pp. 290-296: IEEE.
  • [8] Y.-H. Liu, C.-A. Cheng, and H.-P. Huang, "Novel feature of the EEG based motor imagery BCI system: Degree of imagery," in System Science and Engineering (ICSSE), 2011 International Conference on, 2011, pp. 515-520: IEEE.
  • [9] K. Saka, Ö. Aydemir, and M. Öztürk, "Classification of EEG signals recorded during right/left hand movement imagery using Fast Walsh Hadamard Transform based features," in Telecommunications and Signal Processing (TSP), 2016 39th International Conference on, 2016, pp. 413-416: IEEE.
  • [10] S. Bhattacharyya, A. Khasnobish, A. Konar, D. Tibarewala, and A. K. Nagar, "Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms," in Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on, 2011, pp. 1-8: IEEE.
  • [11] A. H. Do, P. T. Wang, C. E. King, A. Abiri, and Z. Nenadic, "Brain-computer interface controlled functional electrical stimulation system for ankle movement," Journal of neuroengineering and rehabilitation, vol. 8, no. 1, p. 49, 2011.
  • [12] M. S. Mabrouk, "Non-Invasive EEG-based BCI system for Left or Right Hand Movement," Majlesi Journal of Electrical Engineering, vol. 5, no. 3, 2011.
  • [13] C. Liu, H. Wang, H. Pu, Y. Zhang, and L. Zou, "EEG feature extraction and pattern recognition during right and left hands motor imagery in brain-computer interface," in 2012 5th International Conference on BioMedical Engineering and Informatics, 2012, pp. 506-510.
  • [14] N. Robinson and A. P. Vinod, "Bi-Directional Imagined Hand Movement Classification Using Low Cost EEG-Based BCI," in 2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015, pp. 3134-3139.
  • [15] M. Hajibabazadeh and V. Azimirad, "Brain-robot interface: Distinguishing left and right hand EEG signals through SVM," in 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), 2014, pp. 813-816.
  • [16] E. Öztemel, "Yapay Sinir Ağlari," PapatyaYayincilik, Istanbul, 2003.
  • [17] A. Abraham, "129: Artificial Neural Networks," Handbook of Measuring System Design, 2005.
  • [18] M. Tosun, A. Ferikoğlu, R. Güntürkün, and C. Ünal, "Control of sevoflurane anesthetic agent via neural network using electroencephalogram signals during anesthesia," Journal of medical systems, vol. 36, no. 2, pp. 451-456, 2012.
  • [19] P. Welch, "The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms," IEEE Transactions on audio and electroacoustics, vol. 15, no. 2, pp. 70-73, 1967.
  • [20] A. S. Al-Fahoum and A. A. Al-Fraihat, "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains," ISRN neuroscience, vol. 2014, 2014.
  • [21] J. G. Proakis and D. G. Manolakis, "Power spectrum estimation," Digital signal processing; principles, algorithms, and applications, 3rd edn. Prentice Hall, New Jersey, 1996.
  • [22] (15.06.2018). Project BCI - EEG Motor Activity Data Set. Available: https://sites.google.com/site/projectbci/
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Mustafa Tosun 0000-0001-7167-4561

Mustafa Erginli

Ömer Kasım

Burak Uğraş

Şems Tanrıverdi

Tayfun Kavak

Publication Date August 1, 2018
Submission Date July 15, 2018
Acceptance Date July 21, 2018
Published in Issue Year 2018Volume: 1 Issue: 2

Cite

IEEE M. Tosun, M. Erginli, Ö. Kasım, B. Uğraş, Ş. Tanrıverdi, and T. Kavak, “EEG Verileri Kullanılarak Fiziksel El Hareketleri ve Bu Hareketlerin Hayalinin Yapay Sinir Ağları İle Sınıflandırılması”, SAUCIS, vol. 1, no. 2, pp. 1–9, 2018, doi: 10.35377/saucis.01.02.443999.

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