Results that do not confirm expectations are generally referred to as ‘negative’ results. While essential for scientific progress, they are too rarely reported in the literature – Brain–Machine Interface (BMI) research is no exception. This led us to organize a workshop on BMI negative results during the 2018 International BCI meeting. The outcomes of this workshop are reported herein. First, we demonstrate why (valid) negative results are useful, and even necessary for BMIs. These results can be used to confirm or disprove current BMI knowledge, or to refine current theories. Second, we provide concrete examples of such useful negative results, including the limits in BMI-control for complete locked-in users and predictors of motor imagery BMI performances. Finally, we suggest levers to promote the diffusion of (valid) BMI negative results, e.g. promoting hypothesis-driven research using valid statistical tools, organizing special issues dedicated to BMI negative results, or convincing institutions and editors that negative results are valuable.
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Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research
Fabien Lotte Inria, LaBRI, CNRS/University of Bordeaux/Bordeaux INP, Bordeaux, FranceCorrespondencefabien.lotte@inria.fr
https://orcid.org/0000-0002-6888-9198
https://orcid.org/0000-0002-6888-9198
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, Camille Jeunet CLLE Lab, CNRS, University of Toulouse Jean Jaurès, Toulouse, Francehttps://orcid.org/0000-0001-8619-3082View further author information
, Ricardo Chavarriaga Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerlandhttps://orcid.org/0000-0002-8879-2860View further author information
, Laurent Bougrain Neurosys, University of Lorraine, Nancy, Francehttps://orcid.org/0000-0001-6794-0505View further author information
, Dave E. Thompson Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USAhttps://orcid.org/0000-0002-1897-2743View further author information
, Reinhold Scherer Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UKhttps://orcid.org/0000-0003-3407-9709View further author information
, Md Rakibul Mowla Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USAhttps://orcid.org/0000-0001-5765-8856View further author information
, Andrea Kübler Institute of Psychology, University of Würzburg, Wurzburg, Germanyhttps://orcid.org/0000-0003-4876-0415View further author information
, Moritz Grosse-Wentrup Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, AustriaView further author information
, Karen Dijkstra Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegenhttps://orcid.org/0000-0001-5443-5817View further author information
& Natalie Dayan Intelligent Systems Research Center, Ulster University, Londonderry, Northern Irelandhttps://orcid.org/0000-0002-2112-1253View further author information
show allReceived 30 Apr 2019
Accepted 14 Nov 2019
Published online: 09 Jan 2020
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