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.
Highlights
Brain activities have been used by the scientist to understand human behaviours
The result shows that the Butterworth bandpass (BB) filter is more effective in removing the noise and keep the original information
mean square error (MSE) and PNSR Figure 8 and Table 4 display the MSE for BB filter and stationary wavelet transform (SWT) at channel Fp1, Fz and Pz for addiction and non-addiction subjects
Summary
Brain activities have been used by the scientist to understand human behaviours. Potenza [1] proved that brain activities reflected with addictive behaviours. The raw EEG signals are consist of noise and artefacts. To process the EEG signals, people usually transfer the raw data into different frequencies before analyzing the data. According to Caglar [3], there are four steps to process the EEG signals after collecting the raw EEG data. 1. The first step is the pre-processing, which reduce the noise of the data. The MSE and PSNR aim to measure the quality of the signal. M et al, 2020, suggested that the bandpass is more suitable for the EEG signals filtering process [6]. Material The EEG signal data was collected from YKBH, Jakarta, Indonesia. The data was collected using the Brain Maker EEG machine with 19 channels. The signals will be collected when the participants were doing the tasks
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