Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis

Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis Electroencephalogram (EEG) signal is usually suffered from motion artifacts, generated randomly during signal acquisition timings. These artifacts sturdily affect the investigation and therefore, diagnosis of neural diseases from EEG signal. The artifact removal may cause loss of important information from the signal. Therefore, it is required to remove the motion artifacts and simultaneously preserve the desired information, which makes EEG artifact removal a vital task. Enhanced Empirical Mode Decomposition (EEMD) is the most widespread method used for artifact removal, as it is a data-driven based feature extraction method. In this research work the efficiency of various EEMD with different interpolation based artifact removal method have been compared. The EEMD is used to convert input single channel EEG signal to a multichannel signal, and in order to remove the randomness of motion artifact, CCA and DWT filtering were used successively. The performance of different interpolation based artifact removal methods have evaluated and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use as it offers improvements in DSNR and various other performance parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis

Loading next page...
 
/lp/springer_journal/effective-eeg-motion-artifacts-elimination-based-on-comparative-obL00tM5F9
Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4846-3
Publisher site
See Article on Publisher Site

Abstract

Electroencephalogram (EEG) signal is usually suffered from motion artifacts, generated randomly during signal acquisition timings. These artifacts sturdily affect the investigation and therefore, diagnosis of neural diseases from EEG signal. The artifact removal may cause loss of important information from the signal. Therefore, it is required to remove the motion artifacts and simultaneously preserve the desired information, which makes EEG artifact removal a vital task. Enhanced Empirical Mode Decomposition (EEMD) is the most widespread method used for artifact removal, as it is a data-driven based feature extraction method. In this research work the efficiency of various EEMD with different interpolation based artifact removal method have been compared. The EEMD is used to convert input single channel EEG signal to a multichannel signal, and in order to remove the randomness of motion artifact, CCA and DWT filtering were used successively. The performance of different interpolation based artifact removal methods have evaluated and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use as it offers improvements in DSNR and various other performance parameters.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Aug 19, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off