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Determining Effective Features to Use the EOG Sign as a Source Sign

Year 2019, , 134 - 144, 31.12.2019

İlhan Zengin , Mehmet Recep Bozkurt , Muhammed Kürşad Uçar

https://doi.org/10.35377/saucis.02.03.649002
Cited By: 1

Abstract

In this study, the processing of the
Electrooculogram (EOG) signal for human machine interface (IMA) was performed
to facilitate the lives of paralyzed patients. Eye movements were detected with
Biopac MP30 instrument using 5 Ag-AgCl electrodes. The marks were pre-processed
and their attributes were made to be extracted in Matlab environment. The
obtained biopotential signals were extracted and labeled according to the
system to be controlled. These labels are right, left, up, down, and blink. The
extracted features were classified using artificial neural networks. Following
the classification, feature reduction methods were used to eliminate the
features that had little effect on the results and test the performance of the
classification with fewer features.

Keywords

Human Machine Interface HMI Electrooculogram EOG Artificial Neural Network Feature Reduction

References

  • [1] T. Öztürk, “Hawking’in Konuşma Cihazı ve P300 - Toprak Öztürk - Medium,” 2018. [Online]. Available: https://medium.com/@toprakozt/hawkingin-konuşma-cihazı-ve-p300-b38f938733dd. [Accessed: 13-Dec-2019].
  • [2] Tobii, “Tobii Eye Tracker 4C for PC Gaming. Buy Now at €169 - Tobii Gaming,” 2019. [Online]. Available: https://gaming.tobii.com/tobii-eye-tracker-4c/. [Accessed: 13-Dec-2019].
  • [3] J.-S. Lin and W.-C. Yang, “Wireless brain-computer interface for electric wheelchairs with EEG and eye-blinking signals,” Int J Innov Comput Inf Control, vol. 8, pp. 6011–6024, Sep. 2012.
  • [4] A. B. Usakli and S. Gurkan, “Design of a novel efficient humancomputer interface: An electrooculagram based virtual keyboard,” IEEE Trans. Instrum. Meas., vol. 59, no. 8, pp. 2099–2108, Aug. 2010.
  • [5] M. Y. Esas and F. Latifoglu, “Computer-Based design with dual channel device electrooculography and eye movement tracking,” in 2015 Medical Technologies National Conference (TIPTEKNO), 2015, pp. 1–4.
  • [6] J. Heo, H. Yoon, and K. Park, “A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces,” Sensors, vol. 17, no. 7, p. 1485, Jun. 2017.
  • [7] S. Gül, Z. Tiryaki, E. Kuzu, M. Milligüney, and G. Çetinel, “Move Your Wheelchair with Your Eyes,” Int. J. Appl. Math. Electron. Comput., vol. 1, no. SpecialIssue, pp. 5–8, Aug. 2017.
  • [8] H. Ö. Ülkütaş, “Eog’nin Kodlanmasina Dayanan Bilgisayar Tabanli Gözle Yazi Yazma Sistemi Geliştirilmesi,” Başkent Üniversitesi, 2015.
  • [9] M. K. Uçar, K. Polat, M. R. Bozkurt, and C. Bilgin, “Effect of Tıme and Frequency Domaın Features ın Sleep Eeg and Eog Sıgnals Classıfıcatıon,” in Tıp Teknolojileri ulusal Kongresi, 2014, vol. 1, pp. 0–5.
  • [10] G. Gündüz and İ. H. Cedimoğlu, “Derin Öğrenme Algoritmalarını Kullanarak Görüntüden Cinsiyet Tahmini,” Sak. Univ. J. Comput. Inf. Sci., vol. 2, no. 1, pp. 9–17, Apr. 2019.
  • [11] K. Serbest, M. R. Bozkurt, and O. Eldoğan, “Classification of cardiac arrhythmias with artificial neural networks according to gender differences,” J. Eng. Sci. Technol., vol. 10, no. 9, pp. 1144–1149, 2015.
  • [12] G. D. Çetin, Ö. Çetin, and M. R. Bozkurt, “The Detectionof Normal and Epileptic EEG Signals using ANN Methods with Matlab-based GUI,” Int. J. Comput. Appl., vol. 114, no. 12, pp. 45–50, 2015.
  • [13] M. R. Bozkurt, A. Subaşı, E. Köklükaya, and M. Yilmaz, “Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1547–1559, 2016.
  • [14] M. R. Bozkurt, M. K. Uçar, F. Bozkurt, and C. Bilgin, “In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability,” Australas. Phys. Eng. Sci. Med., pp. 1–21, 2019.
  • [15] M. K. Uçar, M. R. Bozkurt, K. Polat, and C. Bilgin, “Investigation of effects of time domain features of the photoplethysmography (PPG) signal on sleep respiratory arrests,” in 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 124–127.
  • [16] K. Polat and S. Güneş, “A new feature selection method on classification of medical datasets: Kernel F-score feature selection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10367–10373, Sep. 2009.
  • [17] A. R. Kavsaoğlu, K. Polat, and M. R. Bozkurt, “An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1782–1796, 2016.
  • [18] V. Gerla et al., “Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness,” Biomed. Signal Process. Control, vol. 31, pp. 381–390, 2017.
  • [19] J. G. Colonna, M. Cristo, M. Salvatierra, and E. F. Nakamura, “An incremental technique for real-time bioacoustic signal segmentation,” Expert Syst. Appl., vol. 42, no. 21, pp. 7367–7374, Nov. 2015.
  • [20] N. A. B. Baharom, “Analysıs of Electrooculography (Eog) for Controllıng Wheelchaır Motion.” 2015.
  • [21] M. K. Uçar, “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm,” Zeki Sist. Teor. ve Uygulamaları Derg., vol. 2, no. 1, pp. 7–12, 2019.
  • [22] E. Şahin, “Makine Öğrenme Yöntemleri ve Kelime Kümesi Tekniği ile İstenmeyen e-Postasınıflandırması,” Hacettepe Üniversitesi, 2018.
  • [23] J. B. Ahire, “The Artificial Neural Networks Handbook: Part 4,” 2018. [Online]. Available: https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e. [Accessed: 17-Nov-2019].
  • [24] 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,” Sak. Univ. J. Comput. Inf. Sci., vol. 1, no. 2, pp. 1–9, Aug. 2018.
  • [25] C. Beleites, R. Salzer, and V. Sergo, “Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues,” Jan. 2013.
  • [26] M. Gök, “Makine Öğrenmesi Yöntemleri ile Akademik Başarının Tahmin Edilmesi,” Gazi Üniversitesi Fen Bilim. Derg. Part C Tasarım ve Teknol., vol. 5, no. 3, pp. 139–148, Sep. 2017.
  • [27] A. Dirican, “Tani Testi Performanslarinin Değerlendirilmesi Ve Kiyaslanmasi,” Cerrahpaşa Tıp Derg., vol. 32, no. 1, 2001.
  • [28] L. H. Geyer and C. G. DeWald, “Feature lists and confusion matrices,” Percept. Psychophys., vol. 14, no. 3, pp. 471–482, Oct. 1973.

EOG İşaretini Kaynak İşaret Olarak Kullanmak Üzere Etkin Özelliklerin Belirlenmesi

Year 2019, , 134 - 144, 31.12.2019

İlhan Zengin , Mehmet Recep Bozkurt , Muhammed Kürşad Uçar

https://doi.org/10.35377/saucis.02.03.649002
Cited By: 1

Abstract

Bu
çalışmada, özellikle felçli hastaların yaşamlarını kolaylaştırmak için Elektrookülogram
(EOG) işaretinin insan makine arabirimi (İMA) için işlenmesi
gerçekleştirilmiştir. Göz hareketleri, Biopac MP30 cihazıyla, 5 adet Ag-AgCl
elektrot kullanılarak algılanmıştır. Alınan işaretler ön işlemeye tabi
tutularak Matlab ortamında öznitelikleri çıkarılacak hale getirilmiştir. Elde
edilen biyopotansiyel sinyallerden öznitelikler çıkarılarak kontrol edilecek
sisteme göre etiketlenmiştir. Bu etiketler sağ, sol, yukarı, aşağı ve göz
kırpmasıdır. Çıkarılan özellikler yapay sinir ağları kullanılarak
sınıflandırılmıştır. Sınıflandırmanın ardından özellik azaltım yöntemleri
kullanılarak sonuç üzerinde etkisi az olan öznitelikler elenmiş ve daha az
özellik ile sınıflandırma başarımları test edilmiştir. 

Keywords

İnsan Makine Arayüzü Elektrookülogram Yapay Sinir Ağları Özellik Azaltımı İMA EOG

References

  • [1] T. Öztürk, “Hawking’in Konuşma Cihazı ve P300 - Toprak Öztürk - Medium,” 2018. [Online]. Available: https://medium.com/@toprakozt/hawkingin-konuşma-cihazı-ve-p300-b38f938733dd. [Accessed: 13-Dec-2019].
  • [2] Tobii, “Tobii Eye Tracker 4C for PC Gaming. Buy Now at €169 - Tobii Gaming,” 2019. [Online]. Available: https://gaming.tobii.com/tobii-eye-tracker-4c/. [Accessed: 13-Dec-2019].
  • [3] J.-S. Lin and W.-C. Yang, “Wireless brain-computer interface for electric wheelchairs with EEG and eye-blinking signals,” Int J Innov Comput Inf Control, vol. 8, pp. 6011–6024, Sep. 2012.
  • [4] A. B. Usakli and S. Gurkan, “Design of a novel efficient humancomputer interface: An electrooculagram based virtual keyboard,” IEEE Trans. Instrum. Meas., vol. 59, no. 8, pp. 2099–2108, Aug. 2010.
  • [5] M. Y. Esas and F. Latifoglu, “Computer-Based design with dual channel device electrooculography and eye movement tracking,” in 2015 Medical Technologies National Conference (TIPTEKNO), 2015, pp. 1–4.
  • [6] J. Heo, H. Yoon, and K. Park, “A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces,” Sensors, vol. 17, no. 7, p. 1485, Jun. 2017.
  • [7] S. Gül, Z. Tiryaki, E. Kuzu, M. Milligüney, and G. Çetinel, “Move Your Wheelchair with Your Eyes,” Int. J. Appl. Math. Electron. Comput., vol. 1, no. SpecialIssue, pp. 5–8, Aug. 2017.
  • [8] H. Ö. Ülkütaş, “Eog’nin Kodlanmasina Dayanan Bilgisayar Tabanli Gözle Yazi Yazma Sistemi Geliştirilmesi,” Başkent Üniversitesi, 2015.
  • [9] M. K. Uçar, K. Polat, M. R. Bozkurt, and C. Bilgin, “Effect of Tıme and Frequency Domaın Features ın Sleep Eeg and Eog Sıgnals Classıfıcatıon,” in Tıp Teknolojileri ulusal Kongresi, 2014, vol. 1, pp. 0–5.
  • [10] G. Gündüz and İ. H. Cedimoğlu, “Derin Öğrenme Algoritmalarını Kullanarak Görüntüden Cinsiyet Tahmini,” Sak. Univ. J. Comput. Inf. Sci., vol. 2, no. 1, pp. 9–17, Apr. 2019.
  • [11] K. Serbest, M. R. Bozkurt, and O. Eldoğan, “Classification of cardiac arrhythmias with artificial neural networks according to gender differences,” J. Eng. Sci. Technol., vol. 10, no. 9, pp. 1144–1149, 2015.
  • [12] G. D. Çetin, Ö. Çetin, and M. R. Bozkurt, “The Detectionof Normal and Epileptic EEG Signals using ANN Methods with Matlab-based GUI,” Int. J. Comput. Appl., vol. 114, no. 12, pp. 45–50, 2015.
  • [13] M. R. Bozkurt, A. Subaşı, E. Köklükaya, and M. Yilmaz, “Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1547–1559, 2016.
  • [14] M. R. Bozkurt, M. K. Uçar, F. Bozkurt, and C. Bilgin, “In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability,” Australas. Phys. Eng. Sci. Med., pp. 1–21, 2019.
  • [15] M. K. Uçar, M. R. Bozkurt, K. Polat, and C. Bilgin, “Investigation of effects of time domain features of the photoplethysmography (PPG) signal on sleep respiratory arrests,” in 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 124–127.
  • [16] K. Polat and S. Güneş, “A new feature selection method on classification of medical datasets: Kernel F-score feature selection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10367–10373, Sep. 2009.
  • [17] A. R. Kavsaoğlu, K. Polat, and M. R. Bozkurt, “An innovative peak detection algorithm for photoplethysmography signals: an adaptive segmentation method,” Turkish J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1782–1796, 2016.
  • [18] V. Gerla et al., “Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness,” Biomed. Signal Process. Control, vol. 31, pp. 381–390, 2017.
  • [19] J. G. Colonna, M. Cristo, M. Salvatierra, and E. F. Nakamura, “An incremental technique for real-time bioacoustic signal segmentation,” Expert Syst. Appl., vol. 42, no. 21, pp. 7367–7374, Nov. 2015.
  • [20] N. A. B. Baharom, “Analysıs of Electrooculography (Eog) for Controllıng Wheelchaır Motion.” 2015.
  • [21] M. K. Uçar, “Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm,” Zeki Sist. Teor. ve Uygulamaları Derg., vol. 2, no. 1, pp. 7–12, 2019.
  • [22] E. Şahin, “Makine Öğrenme Yöntemleri ve Kelime Kümesi Tekniği ile İstenmeyen e-Postasınıflandırması,” Hacettepe Üniversitesi, 2018.
  • [23] J. B. Ahire, “The Artificial Neural Networks Handbook: Part 4,” 2018. [Online]. Available: https://medium.com/@jayeshbahire/the-artificial-neural-networks-handbook-part-4-d2087d1f583e. [Accessed: 17-Nov-2019].
  • [24] 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,” Sak. Univ. J. Comput. Inf. Sci., vol. 1, no. 2, pp. 1–9, Aug. 2018.
  • [25] C. Beleites, R. Salzer, and V. Sergo, “Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues,” Jan. 2013.
  • [26] M. Gök, “Makine Öğrenmesi Yöntemleri ile Akademik Başarının Tahmin Edilmesi,” Gazi Üniversitesi Fen Bilim. Derg. Part C Tasarım ve Teknol., vol. 5, no. 3, pp. 139–148, Sep. 2017.
  • [27] A. Dirican, “Tani Testi Performanslarinin Değerlendirilmesi Ve Kiyaslanmasi,” Cerrahpaşa Tıp Derg., vol. 32, no. 1, 2001.
  • [28] L. H. Geyer and C. G. DeWald, “Feature lists and confusion matrices,” Percept. Psychophys., vol. 14, no. 3, pp. 471–482, Oct. 1973.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Articles
Authors

İlhan Zengin 0000-0002-6446-0933

Mehmet Recep Bozkurt 0000-0003-0673-4454

Muhammed Kürşad Uçar 0000-0002-0636-8645

Publication Date December 31, 2019
Submission Date November 20, 2019
Acceptance Date December 18, 2019
Published in Issue Year 2019

Cite

IEEE İ. Zengin, M. R. Bozkurt, and M. K. Uçar, “EOG İşaretini Kaynak İşaret Olarak Kullanmak Üzere Etkin Özelliklerin Belirlenmesi”, SAUCIS, vol. 2, no. 3, pp. 134–144, 2019, doi: 10.35377/saucis.02.03.649002.

Cited By

https://doi.org/

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