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A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case

Authors:

Miguel A. Mañanas,

Manel J. BarbanojAuthors Info & Claims

Computers in Biology and Medicine, Volume 38, Issue 3

Pages 348 - 360

https://doi.org/10.1016/j.compbiomed.2007.12.001

Published: 01 March 2008 Publication History

Abstract

Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies. In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than 5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40dB at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI) provided an effective technique for eye movement removal even when EOG recordings were not available or when data length was short.

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Cited By

Shukla PRoy VShukla PGupta AGoel VShukla S(2021)Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare ApplicationsJournal of Organizational and End User Computing10.4018/JOEUC.202101010233:1(19-46)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.4018/JOEUC.2021010102
Ceballos Dominguez ESubathra MSairamya NThomas George S(2019)Detection of focal epilepsy in brain maps through a novel pattern recognition techniqueNeural Computing and Applications10.1007/s00521-019-04544-832:14(10143-10157)Online publication date: 26-Oct-2019
https://dl.acm.org/doi/10.1007/s00521-019-04544-8
Mannan MKamran MKang SJeong M(2018)Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer InterfaceComplexity10.1155/2018/48537412018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/4853741
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A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case

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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 38, Issue 3

March, 2008

129 pages

ISSN:0010-4825

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Copyright © Elsevier Ltd © 2007.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2008

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Cited By

Shukla PRoy VShukla PGupta AGoel VShukla S(2021)Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare ApplicationsJournal of Organizational and End User Computing10.4018/JOEUC.202101010233:1(19-46)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.4018/JOEUC.2021010102
Ceballos Dominguez ESubathra MSairamya NThomas George S(2019)Detection of focal epilepsy in brain maps through a novel pattern recognition techniqueNeural Computing and Applications10.1007/s00521-019-04544-832:14(10143-10157)Online publication date: 26-Oct-2019
https://dl.acm.org/doi/10.1007/s00521-019-04544-8
Mannan MKamran MKang SJeong M(2018)Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG-Based Brain-Computer InterfaceComplexity10.1155/2018/48537412018Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1155/2018/4853741
Oosugi NKitajo KHasegawa NNagasaka YOkanoya KFujii N(2017)A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signalNeural Networks10.1016/j.neunet.2017.01.00593:C(1-6)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.neunet.2017.01.005
Kanoga SNakanishi MMitsukura Y(2016)Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogramNeurocomputing10.1016/j.neucom.2016.01.057193:C(20-32)Online publication date: 12-Jun-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.01.057
Yu THo H(2014)Designing an efficient electroencephalography system using database with embedded images management approachComputers in Biology and Medicine10.5555/2800050.280012344:C(27-36)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.5555/2800050.2800123
Mateo JTorres AGarcía MSánchez CCervigon R(2013)Robust Volterra Filter Design for Enhancement of Electroencephalogram Signal ProcessingCircuits, Systems, and Signal Processing10.1007/s00034-012-9447-532:1(233-253)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s00034-012-9447-5
White DCongedo MCiorciari JSilberstein R(2012)Brain oscillatory activity during spatial navigationJournal of Cognitive Neuroscience10.1162/jocn_a_0009824:3(686-697)Online publication date: 1-Mar-2012
https://dl.acm.org/doi/10.1162/jocn_a_00098
Akhtar MMitsuhashi WJames C(2012)Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG dataSignal Processing10.1016/j.sigpro.2011.08.00592:2(401-416)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.sigpro.2011.08.005

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