Savitzky-Golay And Wiener Filtering Performance Analysis In Electroencephalography Signal Processing Of Autistic Children

Electroencephalography (EEG) measures electrical activity in the brain area by placing several electrodes on the scalp that can be used to diagnose autism spectrum disorder (ASD) and various abnormalities in the brain nerves. During the EEG signal recording process, the measured signal is often contaminated by various types of noise, which causes difficulties in analyzing the signal. Therefore, an effective method is needed to reduce these artifacts. This research applied wiener filter (WF) and savitzky-golay filter (SG) methods in reducing noise in the EEG signals of autistic people. This method will be combined with another method, namely Butterworth Band-Pass Filter, to concentrate the frequency in the range of 0.5-40 Hz. Based on the comparison of performance accuracy values using three calculation parameters, namely mean square errors (MSE), Mean absolute errors (MAE), and signal to noise ratio (SNR), this study proves that WF is superior to SG in producing EEG signals of autistic and normal people free from noise. WF shows an SNR value of 34.773 "dB" compared to 22.157 "dB" in SG, as well as lower MAE and MSE values of 0.521 μV and 0.616 μV2 compared to 1.875 μV and 16.990 μV2 in SG. These results confirm that WF is more effective in reducing noise interference and producing more accurate signal estimation in EEG data analysis.