Open AccessRemoving Artifacts From EEG Signal Using Wavelet Transform and Conventional Filters
Author(s) -
Meryem Felja,
Asmae Bencheqroune,
Mohammed Karim,
Ghita Bennis Limas
Publication year - 2021
Publication title -
wseas transactions on information science and applications
Language(s) - English
Resource type - Journals
eISSN - 2224-3402
pISSN - 1790-0832
DOI - 10.37394/23209.2020.17.22
Subject(s) - artifact (error) , electroencephalography , computer science , signal (programming language) , wavelet transform , artificial intelligence , wavelet , pattern recognition (psychology) , computer vision , speech recognition , neuroscience , psychology , programming language
the electroencephalogram (EEG) is a signal of an electrical nature reflecting the neuronal activities of the brain. It is used for the diagnosis of certain cerebral pathologies. However, it becomes more difficult to identify and analyze it when it is corrupted by artifacts of non-cerebral origin such as eye movements, cardiac activities ..., therefore, it is essential to remove these parasitic signals. In literature, there are different techniques for removing artifacts. This paper proposes and discusses a new EEG de-noising technique, based on a combination of wavelet transforms and conventional filters. The experimental results demonstrate that the proposed approach can be an effective tool for removing artifact without suppression of any signal components.