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Related Experiment Video

Updated: Dec 22, 2025

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Conducting decoded neurofeedback studies.

Vincent Taschereau-Dumouchel^1,2, Aurelio Cortese¹, Hakwan Lau^1,2,3,4,5

¹Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan.

Social Cognitive and Affective Neuroscience

|May 6, 2020

Summary

This summary is machine-generated.

Decoded fMRI neurofeedback offers solutions to methodological challenges in closed-loop neurofeedback. This guide details implementing these advanced techniques in rigorous, double-blind, placebo-controlled experiments for broader research adoption.

Keywords:

decoded neurofeedback multivoxel pattern analysis real-time functional magnetic resonance imaging

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Published on: July 26, 2013

Area of Science:

Neuroscience
Cognitive Science
Psychology

Background:

Closed-loop neurofeedback, developed in the 1960s, faces persistent methodological hurdles.
Recent machine learning advancements enable targeting unconscious neural representations.

Purpose of the Study:

To provide a step-by-step guide for implementing decoded fMRI neurofeedback.
To address methodological and analytical considerations in experimental design.
To encourage wider adoption of this intervention method.

Main Methods:

Implementing decoded fMRI neurofeedback in double-blind, placebo-controlled experimental designs.
Utilizing machine learning for targeting multivoxel representations.
Detailed methodological and analytical guidance.

Main Results:

Not applicable for this 'tools of the trade' paper, which focuses on methodology.
The paper provides a framework for rigorous experimental implementation.

Conclusions:

Decoded fMRI neurofeedback presents a promising solution to historical neurofeedback challenges.
Standardized implementation in controlled experiments can advance the field.