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.

中文翻译：

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进行解码的神经反馈研究

闭环神经反馈自 1960 年代后期问世以来就引起了极大的兴趣。然而，该领域历来面临各种方法论挑战。解码的 fMRI 神经反馈可以为其中一些问题提供解决方案。值得注意的是，由于机器学习方法的最新进展，现在可以针对特定多体素表示的无意识事件。在这份贸易文件的工具中，我们讨论了如何在严格的双盲安慰剂对照实验中实施这些干预措施。我们的目标是提供分步指南，以解决一些最常见的方法和分析注意事项。我们还讨论了可用于促进新实验实施的工具。