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Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, Héjja-Brichard Y, Herbst SK, Jerbi K, Kovic V, Lehongre K, Luck SJ, Mercier M, Mosher JC, Pavlov YG, Puce A, Schettino A, Schön D, Sinnott-Armstrong W, Somon B, Šoškić A, Styles SJ, Tibon R, Vilas MG, van Vliet M, Chaumon M. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage 2022; 257:119056. [PMID: 35283287 PMCID: PMC11236277 DOI: 10.1016/j.neuroimage.2022.119056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/25/2022] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Laurens R Krol
- Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Germany
| | - Etienne Combrisson
- Aix-Marseille University, Institut de Neurosciences de la Timone, France
| | | | | | | | - Yseult Héjja-Brichard
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, EPHE, IRD, Université Montpellier, Montpellier, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, NeuroSpin center, Université Paris-Saclay, Gif/Yvette, France
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Laboratory, Department of Psychology, University of Montreal, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Canada
| | - Vanja Kovic
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia
| | - Katia Lehongre
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France
| | - Steven J Luck
- Center for Mind & Brain, University of California, Davis, CA, USA
| | - Manuel Mercier
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | - John C Mosher
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuri G Pavlov
- University of Tuebingen, Germany; Ural Federal University, Yekaterinburg, Russia
| | - Aina Puce
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Antonio Schettino
- Erasmus University Rotterdam, Rotterdam, the Netherland; Institute for Globally Distributed Open Research and Education (IGDORE), Sweden
| | - Daniele Schön
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Anđela Šoškić
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia; Teacher Education Faculty, University of Belgrade, Serbia
| | - Suzy J Styles
- Psychology, Nanyang Technological University, Singapore; Singapore Institute for Clinical Sciences, A*STAR, Singapore
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; School of Psychology, University of Nottingham, Nottingham, UK
| | - Martina G Vilas
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
| | | | - Maximilien Chaumon
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France..
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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