Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments.We hope that this will encourage more researchers to try out this powerful new intervention method.
Pain control by co-adaptive learning in a brain-machine Interface
DOI : 10.1016/j.cub.2020.07.066
Suyi Zhang, Wako Yoshida, Hiroaki Mano, Takufumi Yanagisawa, Flavia Mancini, Kazuhisa Shibata, Mitsuo Kawato, Ben Seymour
Social Cognitive and Affective Neuroscience