University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Artifact removal in magnetoencephalogram background activity with independent component analysis.

Escudero, J, Hornero, R, Abásolo, D, Fernández, A and López-Coronado, M (2007) Artifact removal in magnetoencephalogram background activity with independent component analysis. IEEE Trans Biomed Eng, 54 (11). 1965 - 1973. ISSN 0018-9294

[img]
Preview
PDF
Escudero_et_al_IEEETBiomedEng_final_version_2007.pdf - Accepted Version
Available under License : See the attached licence file.

Download (997kB)
[img] Plain Text (licence)
licence.txt

Download (1kB)

Abstract

The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method.

Item Type: Article
Additional Information:

Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Uncontrolled Keywords: Algorithms, Artifacts, Brain, Computer Simulation, Data Interpretation, Statistical, Diagnosis, Computer-Assisted, Humans, Magnetoencephalography, Models, Neurological, Models, Statistical, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity
Divisions: Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Depositing User: Symplectic Elements
Date Deposited: 25 Jan 2012 01:08
Last Modified: 23 Sep 2013 18:55
URI: http://epubs.surrey.ac.uk/id/eprint/39614

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800