The University of Southampton
University of Southampton Institutional Repository

Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG

Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG
Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG
Background: Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective.

New method: In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms.

Results: Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)—an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp.

Comparison with existing method(s): Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact.

Conclusions: Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.
0165-0270
89-107
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Bono, Valentina
1cb487d9-7af0-421b-8207-a0e785e0c9dd
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Bono, Valentina, Das, Saptarshi, Jamal, Wasifa and Maharatna, Koushik (2016) Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG. Journal of Neuroscience Methods, 267, 89-107. (doi:10.1016/j.jneumeth.2016.04.006). (PMID:27102040)

Record type: Article

Abstract

Background: Electroencephalogram (EEG) signals are often corrupted with unintended artifacts which need to be removed for extracting meaningful clinical information from them. Typically a priori knowledge of the nature of the artifacts is needed for such purpose. Artifact contamination of EEG is even more prominent for pervasive EEG systems where the subjects are free to move and thereby introducing a wide variety of motion-related artifacts. This makes hard to get a priori knowledge about their characteristics rendering conventional artifact removal techniques often ineffective.

New method: In this paper, we explore the performance of two hybrid artifact removal algorithms: Wavelet Packet Transform followed by Independent Component Analysis (WPTICA) and Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in pervasive EEG recording scenario, assuming existence of no a priori knowledge about the artifacts and compare their performance with two existing artifact removal algorithms.

Results: Artifact cleaning performance has been measured using Root Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR)—an index similar to traditional Signal to Noise Ratio (SNR), and also by observing normalized power distribution topography over the scalp.

Comparison with existing method(s): Comparison has been made first using semi-simulated signals and then with real experimentally acquired EEG data with commercially available 19-channel pervasive EEG system Enobio corrupted by eight types of artifact.

Conclusions: Our explorations show that WPTEMD consistently gives best artifact cleaning performance not only in semi-simulated scenario but also in the case of real EEG data containing artifacts.

Text
Manuscript_Bono_eprints.pdf - Accepted Manuscript
Download (3MB)

More information

Accepted/In Press date: 6 April 2016
e-pub ahead of print date: 19 April 2016
Published date: 15 July 2016
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 393783
URI: https://eprints.soton.ac.uk/id/eprint/393783
ISSN: 0165-0270
PURE UUID: 835cb6c5-a847-4bef-bf49-e3e33e2dc6bb

Catalogue record

Date deposited: 09 May 2016 13:48
Last modified: 05 Nov 2019 06:37

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×