Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review

• Published in: Neuroscience Vol. 378; pp. 225 - 233
• Main Authors: Jeunet, Camille, Lotte, Fabien, Batail, Jean-Marie, Philip, Pierre, Micoulaud Franchi, Jean-Arthur
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 15.05.2018; Elsevier - International Brain Research Organization
• Subjects: adaptation; Brain-Computer Interfaces; cognitive profile; Cognitive Sciences; Humans; Life Sciences; Mental Disorders - rehabilitation; Neurofeedback; Neurons and Cognition; personality; training; ADHD; RCT; SoA; PMC; BCI; adaptation; brain–computer interfaces; training; personality; MI-BCIs; SNR; SMR; neurofeedback; cognitive profile; EEG; neurofeedback efficacy; Neurofeedback
• ISSN: 0306-4522; 1873-7544
• DOI: 10.1016/j.neuroscience.2018.03.013

•A human-factor-centered standpoint is of interest to improve neurofeedback training procedures.•In BCI, brain activity modulation abilities seem related to task-specific factors, cognition, and technology-acceptance.•Given the similar nature of BCI and neurofeedback, these factors seem to be relevant for clinical neurofeedback training.•Taking into account these factors may provide insight for further adapting the neurofeedback training protocol to each patient. In 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.