A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques

Manousos A Klados, Panagiotis D Bamidis

Research output: Contribution to journalArticle

Abstract

Artifact rejection techniques are used to recover the brain signals underlying artifactual electroencephalographic (EEG) segments. Although over the last few years many different artifact rejection techniques have been proposed (http://dx.doi.org/10.1109/JSEN.2011.2115236[1], http://dx.doi.org/10.1016/j.clinph.2006.09.003[2], http://dx.doi.org/10.3390/e16126553[3]), none has been established as a gold standard so far, because assessing their performance is difficult and subjective (http://dx.doi.org/10.1109/ITAB.2009.5394295[4], http://dx.doi.org/10.1016/j.bspc.2011.02.001[5], http://dx.doi.org/10.1007/978-3-540-89208-3_300. [6]). This limitation is mainly based on the fact that the underlying artifact-free brain signal is unknown, so there is no objective way to measure how close the retrieved signal is to the real one. This article solves the aforementioned problem by presenting a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts, using a realistic head model. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed.

LanguageEnglish
Pages1004-6
Number of pages3
JournalData in Brief
Volume8
Early online date29 Jun 2016
DOIs
Publication statusPublished - 1 Sep 2016

Fingerprint

Electrooculography
Artifacts
Brain
Datasets
Head

Bibliographical note

© 2016 The Authors. Published by Elsevier Inc. This is an open
access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).

Keywords

  • EEG
  • EOG
  • Artifact Rejection

Cite this

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abstract = "Artifact rejection techniques are used to recover the brain signals underlying artifactual electroencephalographic (EEG) segments. Although over the last few years many different artifact rejection techniques have been proposed (http://dx.doi.org/10.1109/JSEN.2011.2115236[1], http://dx.doi.org/10.1016/j.clinph.2006.09.003[2], http://dx.doi.org/10.3390/e16126553[3]), none has been established as a gold standard so far, because assessing their performance is difficult and subjective (http://dx.doi.org/10.1109/ITAB.2009.5394295[4], http://dx.doi.org/10.1016/j.bspc.2011.02.001[5], http://dx.doi.org/10.1007/978-3-540-89208-3_300. [6]). This limitation is mainly based on the fact that the underlying artifact-free brain signal is unknown, so there is no objective way to measure how close the retrieved signal is to the real one. This article solves the aforementioned problem by presenting a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts, using a realistic head model. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed.",
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A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. / Klados, Manousos A; Bamidis, Panagiotis D.

In: Data in Brief, Vol. 8, 01.09.2016, p. 1004-6.

Research output: Contribution to journalArticle

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