Journal
NEUROIMAGE
Volume 159, Issue -, Pages 417-429Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2017.06.030
Keywords
Magnetoencephalography (MEG); Electroencephalogram (EEG); Preprocessing; Statistical learning; Cross-validation; Automated analysis; Human Connectome Project (HCP)
Funding
- French National Research Agency [ANR-14-NEUC-0002-01]
- National Institutes of Health [R01 MH106174]
- ERC [SLAB ERC-YStG-676943, StG 263584]
- Amazon Webservices Research Grant
- Agence Nationale de la Recherche (ANR) [ANR-14-NEUC-0002] Funding Source: Agence Nationale de la Recherche (ANR)
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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.
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