Abstract : We present an automated solution for detecting bad trials in magneto-/electroencephalography (M/EEG). Bad trials are commonly identified using peak-to-peak rejection thresholds that are set manually. This work proposes a solution to determine them automatically using cross-validation. We show that automatically selected rejection thresholds perform at par with manual thresholds, which can save hours of visual data inspection. We then use this automated approach to learn a sensor-specific rejection threshold. Finally, we use this approach to remove trials with finer precision and/or partially repair them using interpolation.
We illustrate the performance on three public datasets. The method clearly performs better than a competitive benchmark on a 19-subject Faces dataset.
https://hal.archives-ouvertes.fr/hal-01313458
Contributeur : Mainak Jas <>
Soumis le : mardi 10 mai 2016 - 01:57:03
Dernière modification le : samedi 18 février 2017 - 01:17:31
Document(s) archivé(s) le : mardi 15 novembre 2016 - 23:52:31
Mainak Jas, Denis Engemann, Federico Raimondo, Yousra Bekhti, Alexandre Gramfort. Automated rejection and repair of bad trials in MEG/EEG. 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), Jun 2016, Trento, Italy. <hal-01313458>
