Review article
Removal of ocular artifact from the EEG: a reviewRetrait de l'artefact oculaire de l'EEG : synthèse

https://doi.org/10.1016/S0987-7053(00)00055-1Get rights and content

Summary

Eye movements cause changes to the electric fields around the eyes, and consequently over the scalp. As a result, EEG recordings are often significantly distorted, and their interpretation problematic. A number of methods have been proposed to overcome this problem, ranging from the rejection of data corresponding temporally to large eye movements, to the removal of the estimated effect of ocular activity from the EEG (EOG correction). This paper reviews a number of such methods of dealing with ocular artifact in the EEG, focusing on the relative merits of a variety of EOG correction procedures. Issues discussed include the distinction between frequency and time domain approaches, the number of EOG channels required for adequate correction, estimating correction coefficients from raw versus averaged data, differential correction of different types of eye movement, the most suitable statistical procedure for estimating correction coefficients, the use of calibration trials for the estimation of correction coefficients, and the distinction between ‘coefficient estimation’ and ‘correction phase’ error. A suggested EOG correction algorithm is also described.

Résumé

Les mouvements oculaires produisent des changements de champ électrique autour des yeux, et par conséquent sur le scalp (électrooculogramme, EOG) ; cela entraîne des distorsions, souvent significatives, du signal EEG, rendant son interprétation problématique. Plusieurs méthodes ont été proposées pour pallier ce problème ; elles vont du simple rejet des données EEG contemporaines à des mouvements oculaires de grande amplitude, à l'élimination plus sélective de l'effet spécifique de l'activité oculaire sur le signal EEG (correction de l'EOG). Cet article passe en revue la plupart de ces méthodes de correction, en soulignant leurs avantages et inconvénients relatifs. Parmi les items discutés figurent la distinction entre les approches fréquentielles et temporelles, le nombre de canaux nécessaires pour une correction adéquate, les procédures statistiques les plus appropriées pour l'estimation des coefficients de correction, et la distinction entre les erreurs dues à l'estimation des coefficients et celles qui sont liées à la correction de phase. Enfin, nous décrivons également un algorithme de correction qui tâche de minimiser les différents problèmes soulevés.

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