Paper published in a book (Scientific congresses and symposiums)
Automated rejection and repair of bad trials in MEG/EEG
Jas, Mainak; Engemann, Denis; Raimondo, Federico et al.
2016In 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)
Peer reviewed
 

Files


Full Text
automated-rejection-repair.pdf
Publisher postprint (1.33 MB)
Download

All rights reserved


All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
artifact rejection; automation; electroencephalogra-; electroencephalography; machine learn-; Machine learning; magnetoencephalography; phy; preprocessing
Abstract :
[en] © 2016 IEEE. 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.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Jas, Mainak
Engemann, Denis
Raimondo, Federico ;  Université de Liège - ULiège > Consciousness-Coma Science Group
Bekhti, Yousra
Gramfort, Alexandre
Language :
English
Title :
Automated rejection and repair of bad trials in MEG/EEG
Publication date :
2016
Event name :
6th International Workshop on Pattern Recognition in Neuroimaging (PRNI)
Event place :
Trento, Italy
Event date :
Jun 2016
Audience :
International
Main work title :
2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)
Publisher :
IEEE
ISBN/EAN :
978-1-4673-6530-7
Pages :
1-4
Peer reviewed :
Peer reviewed
Available on ORBi :
since 17 January 2020

Statistics


Number of views
38 (1 by ULiège)
Number of downloads
147 (1 by ULiège)

Scopus citations®
 
25
Scopus citations®
without self-citations
23
OpenCitations
 
9

Bibliography


Similar publications



Contact ORBi