• Camille Jeunet, Fabien Lotte, Jean-Marie Batail, Pierre Philip, and Jean-Arthur Micoulaud Franchi.
• Univ. Rennes, Inria, IRISA, CNRS, France; Defitech Chair in Brain Machine Interfaces (CNBI), EPFL, Switzerland.
• Neuroscience. 2018 May 15; 378: 225-233.

AbstractIn their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain-Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.'s review by reporting recent additions to the BCI literature. The object of this paper is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another. By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance-related factors. Since BCIs and NF share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomized controlled trials.Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

10 keywords...

hide…