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We present an automated algorithm for unified rejection and repair of badtrials in magnetoencephalography (MEG) and electroencephalography (EEG)signals. Our method capitalizes on cross-validation in conjunction with arobust evaluation metric to estimate the optimal peak-to-peak threshold -- aquantity commonly used for identifying bad trials in M/EEG. This approach isthen extended to a more sophisticated algorithm which estimates this thresholdfor each sensor yielding trial-wise bad sensors. Depending on the number of badsensors, the trial is then repaired by interpolation or by excluding it fromsubsequent analysis. All steps of the algorithm are fully automated thuslending itself to the name Autoreject.  In order to assess the practical significance of the algorithm, we conductedextensive validation and comparison with state-of-the-art methods on fourpublic datasets containing MEG and EEG recordings from more than 200 subjects.Comparison include purely qualitative efforts as well as quantitativelybenchmarking against human supervised and semi-automated preprocessingpipelines. The algorithm allowed us to automate the preprocessing of MEG datafrom the Human Connectome Project (HCP) going up to the computation of theevoked responses. The automated nature of our method minimizes the burden ofhuman inspection, hence supporting scalability and reliability demanded by dataanalysis in modern neuroscience.

Autoreject: Automated artifact rejection for MEG and EEG data