Butterworth Bandpass and Stationary Wavelet Transform Filter Comparison for Electroencephalography Signal
Suzairi Daud, Rubita Sudirman · 2015
Electroencephalography (EEG) is a non-invasive process that record and capture brain signal. Raw EEG signal contaminated with a lot of noise such as power line interference, muscle movement (electromyography artifacts) and eye blinking (electrooculography artifacts). Removing this types of noise can produce a clean signal. The noise that contaminated with EEG signal will affect the actual result during analyzing stage. This paper aims to shows the comparison when EEG signal filter with 4thorder Butterworth band pass and stationary wavelet transform (SWT). Two parameters are used to compare the effect of filter on EEG signal, that are mean square error (MSE) and peak-to-noise ratio (PSNR). This types of parameter was use because it able to compare the quantitative value for two signals. The result shows that the stationary wavelet transform is more effective in removing the noise without losing the original information.