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. 2020 Jun 30;14:246. doi: 10.3389/fnhum.2020.00246

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Copyright © 2020 Brandmeyer and Delorme.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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For artifact rejection, each day an initial EEG baseline was measured for 1 min (start baseline), followed by six training blocks of 5 min each (block 1–6). This initial baseline was used by the ASR artifact rejection algorithm in order to optimize the filtering all feedback sessions for the day using the asr_calibrate function (default parameters of the ASR algorithm were used; variance rejection cut off of 5; block size of 10 sample to calculate covariance matrix; window size of 500 ms and window overlap of 330 ms.