AI Article Synopsis
- - This paper reviews the pros and cons of using smoothing filters in analyzing EEG data, which is often affected by various artifacts and signal distortions.
- - Three different smoothing filters—smooth filter, median filter, and Savitzky-Golay filter—were compared for their effectiveness in improving the clarity of EEG data for medical diagnostics.
- - Although the results showed promise in enhancing data readability, the search for optimal filtering methods continues to be an ongoing area of research.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038754 | PMC |
| http://dx.doi.org/10.3390/s20030807 | DOI Listing |
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