Open AccessArtefact Removal from EEG Signals u sing Total Variation De noising
Author(s) -
Yogesh Kumar,
Devendra Jha
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2703.039520
Subject(s) - wavelet , electroencephalography , variation (astronomy) , signal (programming language) , pattern recognition (psychology) , noise (video) , wavelet transform , computer science , artificial intelligence , root mean square , speech recognition , statistics , mathematics , psychology , engineering , image (mathematics) , physics , psychiatry , astrophysics , programming language , electrical engineering
Artefacts removing (de-noising) from EEG signals has been an important aspect for medical practitioners for diagnosis of health issues related to brain. Several methods have been used in last few decades. Wavelet and total variation based de-noising have attracted the attention of engineers and scientists due to their de-noising efficiency. In this article, EEG signals have been de-noised using total variation based method and results obtained have been compared with the results obtained from the celebrated wavelet based methods . The performance of methods is measured using two parameters: signal-to-noise ratio and root mean square error. It has been observed that total variation based de-noising methods produce better results than the wavelet based methods.