KaydedilenlerDerginizi ekleyin

Dergiler / Ejovoc (Electronic Journal of Vocational Colleges) / 2018 / Cilt: 8 Sayı: 2 Özel Sayı

A STATISTICAL FEATURE EXTRACTION IN WAVELET DOMAIN FOR MOVEMENT CLASSIFICATION: A CASE STUDY FOR EYES OPEN, EYES CLOSED, AND OPEN/CLOSED FIST TASKS

Şükriye Kara
Semih Ergin
Sayfa
158–162
Yayın Tarihi
2018-11-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The best result is obtained by using k-NN.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities.
📊
Temel Bulgular & Veri:For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments.
💡
Sonuç & Katkı:The best result is obtained by using k-NN.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:EEG · electroencephalogram · wavelet transform

Özet

Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities. For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments. Brain-Computer Interface Systems turns these signals into device signals that are controllable at the level of thought. In this paper, we classify diverse tasks according to EEG (electroencephalogram) signals. Then pre-processing, feature extraction and classification steps are hold. For classification, we use FLDA, Linear SVM, Quadratic SVM, PCA, and k-NN methods. The best result is obtained by using k-NN.

Abstract

Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities. For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments. Brain-Computer Interface Systems turns these signals into device signals that are controllable at the level of thought. In this paper, we classify diverse tasks according to EEG (electroencephalogram) signals. Then pre-processing, feature extraction and classification steps are hold. For classification, we use FLDA, Linear SVM, Quadratic SVM, PCA, and k-NN methods. The best result is obtained by using k-NN.

🔖

Bu Makaleye Atıf Yap

Kara, Ş., Ergin, S. (2018). A STATISTICAL FEATURE EXTRACTION IN WAVELET DOMAIN FOR MOVEMENT CLASSIFICATION: A CASE STUDY FOR EYES OPEN, EYES CLOSED, AND OPEN/CLOSED FIST TASKS. Ejovoc (Electronic Journal of Vocational Colleges), Cilt: 8 Sayı: 2 Özel Sayı, 158–162. https://ajindex.com/articles/1266331

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (30)

  1. Farwell, L. A. and Donchin, E. (Dec. 1988). Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroen- ceph. Clin. Neurophysiol., vol. 70, no. 6, pp. 510–523, Dec. 1988
  2. Wolpaw, McFarland, D. J., Neat, G. W. and, Forneris, C. A. (Mar. 1991). An EEG-based brain-computer interface for cursor control. Electroenceph. Clin. Neurophysiol., vol. 78, no. 3, pp. 252–259
  3. Sutter, E. E. (1992). The brain response interface: Communication through visually guided electrical brain responses. J. Microcomput. Applicat., vol. 15, pp. 31–45
  4. McFarland, D. J., Neat, G. W. and, Wolpaw, J. R. (1993). An EEG-based method for graded cursor control. Psychobiol., vol. 21, pp. 77–81
  5. Pfurtscheller, Flotzinger, D. and, Kalcher, J. (1993). Brain-computer inter- face—A new communication device for handicapped persons. J. Mi- crocomput. Applicat., vol. 16, pp. 293–299
  6. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., Perelmouter, J., Taub, E. and, Flor, H. (Mar. 1999). A spelling device for the paralyzed. Nature, vol. 398, no. 6725, pp. 297–298
  7. Kubler, A., Kotchoubey, B., Hinterberger, T., Ghanayim, N., Perel- mouter, J., Schauer, M., Fritsch, C., Taub, E. and, Birbaumer, N. (Jan. 1999) The thought translation device: A neurophysiological approach to communication in total motor paralysis. Exp. Brain Res., vol. 124, no. 2, pp. 223–232
  8. Kennedy, P. R., Bakay, R. A., Moore, M. M., and Goldwaithe, J. (June 2000). Direct control of a computer from the human central nervous system. IEEE Trans. Rehab. Eng., vol. 8, pp. 198–202
  9. Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R., (2004). BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering 51(6): 1034-1043
  10. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, PCh., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C-K, Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23): e215-e220 [Circulation Electronic Pages]
  11. Cengiz, Y., Doç, Y., Ariöz, U. (2016). Ayrik dalgacik dönüsümü kullanarak konusma sinyallerinin gürültüden arindirilmasi için uygulama: An application for speech denoising using discrete wavelet transform [pdf]. Retrieved from
  12. Misiti, M., Misiti, Y., Oppenheim G. and, Poggi, J. (2007). Wavelets and their Applications.
  13. Gupta, V., Mahle, R. and, Shriwas, R. S. (2013). Image denoising using wavelet transform method. Tenth International Conference on Wireless and Optical Communications Networks (WOCN), Bhopal, pp. 1-4, 2013
  14. Shannon, Claude E. (July-October 1948). A mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423
  15. Sen, B., Peker, M., Cavusoglu, A., Celebi, F.V. (2014). A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms. Journal of Medical Systems, 38(3
  16. Farwell, L. A. and Donchin, E. (Dec. 1988). Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroen- ceph. Clin. Neurophysiol., vol. 70, no. 6, pp. 510–523, Dec. 1988
  17. Wolpaw, McFarland, D. J., Neat, G. W. and, Forneris, C. A. (Mar. 1991). An EEG-based brain-computer interface for cursor control. Electroenceph. Clin. Neurophysiol., vol. 78, no. 3, pp. 252–259
  18. Sutter, E. E. (1992). The brain response interface: Communication through visually guided electrical brain responses. J. Microcomput. Applicat., vol. 15, pp. 31–45
  19. McFarland, D. J., Neat, G. W. and, Wolpaw, J. R. (1993). An EEG-based method for graded cursor control. Psychobiol., vol. 21, pp. 77–81
  20. Pfurtscheller, Flotzinger, D. and, Kalcher, J. (1993). Brain-computer inter- face—A new communication device for handicapped persons. J. Mi- crocomput. Applicat., vol. 16, pp. 293–299
  21. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., Perelmouter, J., Taub, E. and, Flor, H. (Mar. 1999). A spelling device for the paralyzed. Nature, vol. 398, no. 6725, pp. 297–298
  22. Kubler, A., Kotchoubey, B., Hinterberger, T., Ghanayim, N., Perel- mouter, J., Schauer, M., Fritsch, C., Taub, E. and, Birbaumer, N. (Jan. 1999) The thought translation device: A neurophysiological approach to communication in total motor paralysis. Exp. Brain Res., vol. 124, no. 2, pp. 223–232
  23. Kennedy, P. R., Bakay, R. A., Moore, M. M., and Goldwaithe, J. (June 2000). Direct control of a computer from the human central nervous system. IEEE Trans. Rehab. Eng., vol. 8, pp. 198–202
  24. Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R., (2004). BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE Transactions on Biomedical Engineering 51(6): 1034-1043
  25. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, PCh., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C-K, Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23): e215-e220 [Circulation Electronic Pages]
  26. Cengiz, Y., Doç, Y., Ariöz, U. (2016). Ayrik dalgacik dönüsümü kullanarak konusma sinyallerinin gürültüden arindirilmasi için uygulama: An application for speech denoising using discrete wavelet transform [pdf]. Retrieved from
  27. Misiti, M., Misiti, Y., Oppenheim G. and, Poggi, J. (2007). Wavelets and their Applications.
  28. Gupta, V., Mahle, R. and, Shriwas, R. S. (2013). Image denoising using wavelet transform method. Tenth International Conference on Wireless and Optical Communications Networks (WOCN), Bhopal, pp. 1-4, 2013
  29. Shannon, Claude E. (July-October 1948). A mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423
  30. Sen, B., Peker, M., Cavusoglu, A., Celebi, F.V. (2014). A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms. Journal of Medical Systems, 38(3)