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

  • Abstract

    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

     

     

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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

    Received date: 2018-06-26

    Accepted date: 2018-06-26