Closed-loop brain training: the science of neurofeedback

By Ranganatha Sitaram, T. Ros, Luke E. Stoeckel, S. Haller, F. Scharnowski, Jarrod A. Lewis-Peacock, Nikolaus Weiskopf, M. Blefari, Mohit Rana, Ethan Oblak and 2 more. Nature Reviews Neuroscience, 2017

1,106 citations recorded by the source corpus.

Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain–machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/306a42dd3d56bca19221a99a8fb7128ef065d06a

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In Cognitive Neuroscience

Citations in the corpus

Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Closed-loop BCI with neurofeedback is assumed to contribute to the reorganization of cortical-subcortical neural networks and assist subjects in self-regulating specific brain rhythms; notwithstanding, the underlying mechanisms that alter neural substrates are still not well-understood [90].”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “Such neural modulation is made possible by routing empirical measurements of the user’s neural activity to a screen or other external display device that they can directly observe [41, 75, 98].”