Coefficient of Subband Discrete Wavelet Transforms for Feature Extraction of Electro Encephalo Graph (EEG) Signals

  • Authors

    • H Hindarto
    • Arif Muntasa
    https://doi.org/10.14419/ijet.v7i2.12.14676
  • EEG, Mean, Maximum, Discrete Wavelet Transformation, BackPropagation

  • This study focuses on feature extraction for Electro Encephalo Graph (EEG) signals using the Discrete Wavelet Transform method. The EEG signal is used to move the cursor up and down the cursor. In each sub band of the Electro Encephalo Graph (EEG) signal waves the means and Maximum value are taken to characterize the EEG signals. Backpropagation Neural Network is used as an EEG signal classification to determine whether the cursor moves up or the cursor moves down. The data used in this study are EEG data derived from BCI competition 2003 (BCI Competition 2003). Decision-making is done in two stages. In the first stage, the mean and maximum values of each wavelet subband is used as a feature extraction of the EEG signal data. This feature is an input to the Backpropagation Neural Network. In the second stage of the classification process into two classes of class 0 (for cursor up) and class 1 (for the cursor down), there are 260 training data files of EEG and 293 signals from EEG signal data testing files, so the whole becomes 553 data files of EEG signals. The result obtained for EEG signal classification is 75.8% of the tested signal data

     

     

  • References

    1. [1] J. R. Wolpaw et al., “Brain-computer interface technology: a review of the first international meeting.,†IEEE Trans. Rehabil. Eng., 2000.

      [2] P. A. Pour, T. Gulrez, O. AlZoubi, G. Gargiulo, and R. a Calvo, “Brain-Computer Interface: Next Generation Thought Controlled Distributed Video Game Development Platform,†2008 Ieee Symp. Comput. Intell. Games, 2008.

      [3] N. Kurniasih, A. S. Ahmar, D. R. Hidayat, H. Agustin, and E. Rizal, “Forecasting Infant Mortality Rate for China: A Comparison Between α-Sutte Indicator, ARIMA, and Holt-Winters,†J. Phys. Conf. Ser., vol. 1028, no. 1, p. 012195, 2018.

      [4] A. S. Ahmar, “A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia,†Int. J. Eng. Technol., vol. 7, no. 1.6, pp. 20–22, 2018.

      [5] A. Rahman and A. S. Ahmar, “Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models,†in AIP Conference Proceedings, 2017, vol. 1885.

      [6] A. S. Ahmar et al., “Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO),†J. Phys. Conf. Ser., vol. 954, 2018.

      [7] D. U. Sutiksno, A. S. Ahmar, N. Kurniasih, E. Susanto, and A. Leiwakabessy, “Forecasting Historical Data of Bitcoin using ARIMA and α-Sutte Indicator,†J. Phys. Conf. Ser., vol. 1028, no. 1, p. 012194, 2018.

      [8] A. S. Ahmar, A. Rahman, A. N. M. Arifin, and A. A. Ahmar, “Predicting movement of stock of ‘Y’ using sutte indicator,†Cogent Econ. Financ., vol. 5, no. 1, 2017.

      [9] A. S. Ahmar, A. Rahman, and U. Mulbar, “α- Sutte Indicator: a new method for time series forecasting,†J. Phys. Conf. Ser., vol. 1040, no. 1, p. 012018, 2018.

      [10] I. Güler and E. D. Übeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,†J. Neurosci. Methods, 2005.

      [11] E. R. Policy, “Mp r a,†2016.

      [12] Y. Li and P. Wen, “Classification of EEG Signals Using Sampling Techniques and Least Square Support Vector Machines,†LNCS, 2009.

      [13] L. Guo, D. Rivero, J. A. Seoane, and A. Pazos, “Classification of EEG signals using relative wavelet energy and artificial neural networks,†in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC ’09, 2009.

      [14] B. D. Mensh, J. Werfel, and H. S. Seung, “BCI competition 2003 - Data set Ia: Combining gamma-band power with slow cortical potentials to improve single-trial classification of electrolencephallographic signals,†IEEE Trans. Biomed. Eng., 2004.

      [15] “BCI Competition II.†.

      [16] Y. . Shin, “Wavelet transform for time-frequency analysis of vibrational signature and its application,†1993.

      [17] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,†Neural Networks, 1989.

      [18] Haviluddin, F. Agus, M. Azhari, and A. S. Ahmar, “Artificial Neural Network Optimized Approach for Improving Spatial Cluster Quality of Land Value Zone,†Int. J. Eng. Technol., vol. 7, no. 2.2, pp. 80–83, 2018.

      [19] T. Özel and A. Nadgir, “Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools,†Int. J. Mach. Tools Manuf., 2002.

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  • How to Cite

    Hindarto, H., & Muntasa, A. (2018). Coefficient of Subband Discrete Wavelet Transforms for Feature Extraction of Electro Encephalo Graph (EEG) Signals. International Journal of Engineering & Technology, 7(2.14), 208-211. https://doi.org/10.14419/ijet.v7i2.12.14676