Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and ex...

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Bibliographic Details
Main Authors: Aleksandra Kawala-Sterniuk, Michal Podpora, Mariusz Pelc, Monika Blaszczyszyn, Edward Jacek Gorzelanczyk, Radek Martinek, Stepan Ozana
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/3/807
Description
Summary:This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky−Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.
ISSN:1424-8220