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Conference papers
Automated rejection and repair of bad trials in MEG/EEG
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.
Cited literature [19 references]
https://hal.archives-ouvertes.fr/hal-01313458
Contributor : Mainak Jas <>
Submitted on : Tuesday, May 10, 2016 - 1:57:03 AM
Last modification on : Thursday, March 5, 2020 - 3:54:16 PM
Document(s) archivé(s) le : Tuesday, November 15, 2016 - 11:52:31 PM
Contributor : Mainak Jas <>
Submitted on : Tuesday, May 10, 2016 - 1:57:03 AM
Last modification on : Thursday, March 5, 2020 - 3:54:16 PM
Document(s) archivé(s) le : Tuesday, November 15, 2016 - 11:52:31 PM
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automated-rejection-repair.pdf
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