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1 LTCI - Laboratoire Traitement et Communication de l-Information 2 Télécom ParisTech 3 UPMC - Université Pierre et Marie Curie - Paris 6 4 Departamento de Computación Buenos Aires

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.


Keywords : magnetoencephalography electroencephalography preprocessing artifact rejection automation machine learning





Autor: Mainak Jas - Denis Engemann - Federico Raimondo - Yousra Bekhti - Alexandre Gramfort -

Fuente: https://hal.archives-ouvertes.fr/



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