Journal article
Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research.
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Pernet C
Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK. cyril.pernet@ed.ac.uk.
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Garrido MI
Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.
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Gramfort A
Université Paris-Saclay, Inria, CEA, Palaiseau, France.
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Maurits N
University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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Michel CM
Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland.
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Pang E
SickKids Research Institute, Toronto, Ontario, Canada.
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Salmelin R
Department of Neuroscience and Biomedical Engineering, Aalto University, Aalto, Finland.
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Schoffelen JM
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
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Valdes-Sosa PA
Joint China-Cuba Laboratory for Neurotechnology, University of Electronic Science and Technology of China, Chengdu, China.
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Puce A
Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN, United States. ainapuce@indiana.edu.
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Published in:
- Nature neuroscience. - 2020
English
The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this 'living' set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.
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Language
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Open access status
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closed
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Persistent URL
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https://sonar.ch/global/documents/46885
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