Manuscripts on this dataset

1. 

   CMS Manager @ 5:55 am on 07 Feb 2024

   Manuscripts on this dataset, "ds002718"

2. 

   CMS Manager @ 5:55 am on 07 Feb 2024

   1. J Wilson, Quantify consciousness, Available at SSRN, 2024, Cited by 0, https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4765453
   2. M Klug, T Berg, K Gramann, Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments, Scientific Reports, 2024, Cited by 7, https://www.nature.com/articles/s41598-024-64919-3
   3. Z Lu, Y Ku, Facial representation comparisons between human brain and deep convolutional neural network reveal a fatigue repetition suppression mechanism, BioRxiv, 2023, Cited by 1, https://www.biorxiv.org/content/10.1101/2023.01.02.522298.abstract
   4. A Delorme, EEG is better left alone, Scientific reports, 2023, Cited by 150, https://www.nature.com/articles/s41598-023-27528-0
   5. V Litvak, A Delorme, F Tadel, A Gramfort, R Oostenveld, From raw MEG/EEG to publication: How to perform MEG/EEG group analysis with free academic software, 2023, Cited by 0, https://books.google.com/books?hl=en&lr=&id=c-vDEAAAQBAJ&oi=fnd&pg=PP1&dq=ds002718&ots=FOqm5rAD4S&sig=JGc-kTr8KKxJOmRJGPOJi8w7C9Q
   6. Z Lu, Y Ku, Bridging the Gap: Exploring Neural Mechanisms of Facial Repetition Suppression through EEG and DCNNs, bioRxiv, 2023, Cited by 1, https://www.biorxiv.org/content/10.1101/2023.01.02.522298.pdf
   7. Z Lu, Y Ku, Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression, IScience, 2023, Cited by 3, https://www.cell.com/iscience/fulltext/S2589-0042(23)02578-6?uuid=uuid%3A3ba31178-9a34-4987-b829-d0ae396e5738
   8. M Klug, T Berg, K Gramann, No need for extensive artifact rejection for ICA-A multi-study evaluation on stationary and mobile EEG datasets, bioRxiv, 2022, Cited by 2, https://www.biorxiv.org/content/10.1101/2022.09.13.507772.abstract
   9. A Delorme, R Oostenveld, F Tadel, A Gramfort, From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software, Frontiers in Neuroscience, 2022, Cited by 8, https://www.frontiersin.org/articles/10.3389/fnins.2022.854471/full
   10. CR Pernet, R Martinez-Cancino, D Truong, From BIDS-formatted EEG data to sensor-space group results: a fully reproducible workflow with EEGLAB and LIMO EEG, Frontiers in neuroscience, 2021, Cited by 44, https://www.frontiersin.org/articles/10.3389/fnins.2020.610388/full
   11. C Pernet, G Rousselet, IS Mas, R Martinez, R Wilcox, Electroencephalography robust statistical linear modelling using a single weight per trial, bioRxiv, 2021, Cited by 7, https://www.biorxiv.org/content/10.1101/2021.04.27.441629.abstract
   12. DG Wakeman, RN Henson, Face processing EEG dataset for EEGLAB, Cited by 3

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