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Autoreject: Automated artifact rejection for MEG and EEG data
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 quan...
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| Pubblicato in: | Neuroimage |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2017
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7243972/ https://ncbi.nlm.nih.gov/pubmed/28645840 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.neuroimage.2017.06.030 |
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