Autoreject: Automated artifact rejection for MEG and EEG data

Abstract : We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject. In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.
Liste complète des métadonnées

Littérature citée [52 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01562403
Contributeur : Alexandre Gramfort <>
Soumis le : lundi 17 juillet 2017 - 09:52:58
Dernière modification le : vendredi 31 août 2018 - 09:03:57
Document(s) archivé(s) le : vendredi 26 janvier 2018 - 19:56:33

Fichier

1612.08194_hal.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, Alexandre Gramfort. Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage, Elsevier, 2017, 〈10.1016/j.neuroimage.2017.06.030〉. 〈hal-01562403〉

Partager

Métriques

Consultations de la notice

437

Téléchargements de fichiers

168