Comparison between Butterworth Bandpass and Stationary Wavelet Transform Filter for Electroencephalography Signal Classification

EasyChair Preprint 4404

Abstract

The Electroencephalography (EEG) signals able to obtain the information from the brain signals. Reduce the noise of the raw EEG data can improve the accuracy of the result. The pre-processing step of the raw EEG data can generate a clean signal and improve the accuracy. The purpose of this paper is to compare the Butterworth bandpass (BB) and stationary wavelet transform (SWT) method for the pornography addiction EEG data. The data was collected from Yayasan Kita dan Buah Hati (YKBH), Jakarta, Indonesia, using the Brain Maker EEG machine with 19 channels. We used mean square error (MSE) and peak-to-noise ratio (PSNR) to compare the quantitative value for the filtered EEG signals. The result shows that the BB filter is more effective in removing the noise and keep the original information.

Keyphrases: Butterworth bandpass filter, EEG signal with porn addiction, MSE, PSNR, SWT filter

@booklet{EasyChair:4404,
  author    = {Kang Xiaoxi and Dini Handayani and Hamwira Yaacob},
  title     = {Comparison between Butterworth Bandpass and Stationary Wavelet Transform Filter for Electroencephalography Signal Classification},
  howpublished = {EasyChair Preprint 4404},
  year      = {EasyChair, 2020}}