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Journals / 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
Pages
158–162
Publication Date
2018-11-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The best result is obtained by using k-NN.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities.
📊
Key Findings & Data:For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments.
💡
Conclusion & Impact:The best result is obtained by using k-NN.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:EEG · electroencephalogram · wavelet transform

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.

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.

🔖

Cite This Article

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

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References (30)

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  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)