EEG Signal Filtering Method and Comparison Between Modern to Classical Filter

Abstract

In neuroscience and therapeutic applications, electroencephalography (EEG) has become a crucial technique for gaining understanding of brain activity and cognitive processes. In order to improve signal quality, lower noise, and extract useful information from EEG data, preprocessing is essential. In the context of EEG signal processing, this study compares the effectiveness of three frequently used filtering methods: the median filter, average filter, and Gaussian filter.This study's main goal is to assess and compare how well these filters work to reduce the various forms of noise that may be found in EEG data, such as physiological artifacts, environmental interference, and electrode noise. The study makes use of a large dataset of EEG recordings that spans a range of experimental settings and mental states. The filtering methods are methodically applied to the unprocessed EEG data, and then the effects on signal fidelity and quality are thoroughly examined.The findings of this investigation shed important light on the advantages and disadvantages of each filtering strategy used in EEG data processing. In the end, the conclusions from this comparison study help offer criteria for choosing a suitable filtering approach based on the unique properties of EEG data and the research goals.The findings of this investigation offer important new understandings of the advantages and disadvantages of each filtering strategy used in EEG data processing. Finally, depending on the unique properties of the EEG data and the study goals, the findings from this comparison analysis help offer criteria for choosing an acceptable filtering approach.