An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals

Loading...
Thumbnail Image
File version

Version of Record (VoR)

Author(s)
Nayak, AB
Shah, A
Maheshwari, S
Anand, V
Chakraborty, S
Kumar, TS
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2024
Size
File type(s)
Location
Abstract

Motion artifacts reduce the quality of information in the electroencephalogram (EEG) signals. In this study, we have developed an effective approach to mitigate the motion artifacts in EEG signals by using empirical wavelet transform (EWT) technique. Firstly, we decompose EEG signals into narrowband signals called intrinsic mode functions (IMFs). These IMFs are further processed to suppress the artifacts. In our first approach, principal component analysis (PCA) is employed to suppress the noise from these decomposed IMFs. In the second approach, the IMFs with noisy components are identified using the variance measure, which are then removed to obtain the artifact-suppressed EEG signal. Our experiments are conducted on a publicly available Physionet dataset of EEG signals to demonstrate the effectiveness of our approach in suppressing motion artifacts. More importantly, the IMF-variance-based approach has provided significantly better performance than the EWT-PCA based approach. Also, the IMF-variance based approach is computationally more efficient than the EWT-PCA based approach. Our proposed IMF-variance based approach achieved an average signal to noise ratio (ΔSNR) of 28.26 dB and surpassed the existing methods developed for motion artifact removal.

Journal Title

Decision Analytics Journal

Conference Title
Book Title
Edition
Volume

10

Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Item Access Status
Note
Access the data
Related item(s)
Subject
Persistent link to this record
Citation

Nayak, AB; Shah, A; Maheshwari, S; Anand, V; Chakraborty, S; Kumar, TS, An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals, Decision Analytics Journal, 2024, 10, pp. 100420

Collections