Literature DB >> 27339691

A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback.

A Gaume1, A Vialatte2, A Mora-Sánchez1, C Ramdani3, F B Vialatte4.   

Abstract

We believe that the missing keystone to design effective and efficient biofeedback and neurofeedback protocols is a comprehensive model of the mechanisms of feedback learning. In this manuscript we review the learning models in behavioral, developmental and cognitive psychology, and derive a synthetic model of the psychological perspective on biofeedback. We afterwards review the neural correlates of feedback learning mechanisms, and present a general neuroscience model of biofeedback. We subsequently show how biomedical engineering principles can be applied to design efficient feedback protocols. We finally present an integrative psychoengineering model of the feedback learning processes, and provide new guidelines for the efficient design of biofeedback and neurofeedback protocols. We identify five key properties, (1) perceptibility=can the subject perceive the biosignal?, (2) autonomy=can the subject regulate by himself?, (3) mastery=degree of control over the biosignal, (4) motivation=rewards system of the biofeedback, and (5) learnability=possibility of learning. We conclude with guidelines for the investigation and promotion of these properties in biofeedback protocols.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Biofeedback; Brain plasticity; Development; Executive function; Learning; Neurofeedback; Psychoengineering

Mesh:

Year:  2016        PMID: 27339691     DOI: 10.1016/j.neubiorev.2016.06.012

Source DB:  PubMed          Journal:  Neurosci Biobehav Rev        ISSN: 0149-7634            Impact factor:   8.989


  16 in total

1.  A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.

Authors:  Aldo Mora-Sánchez; Alfredo-Aram Pulini; Antoine Gaume; Gérard Dreyfus; François-Benoît Vialatte
Journal:  Cogn Neurodyn       Date:  2020-03-09       Impact factor: 5.082

Review 2.  Advances in fMRI Real-Time Neurofeedback.

Authors:  Takeo Watanabe; Yuka Sasaki; Kazuhisa Shibata; Mitsuo Kawato
Journal:  Trends Cogn Sci       Date:  2017-10-12       Impact factor: 20.229

Review 3.  EEG Neurofeedback for Anxiety Disorders and Post-Traumatic Stress Disorders: A Blueprint for a Promising Brain-Based Therapy.

Authors:  J A Micoulaud-Franchi; C Jeunet; A Pelissolo; T Ros
Journal:  Curr Psychiatry Rep       Date:  2021-10-29       Impact factor: 5.285

Review 4.  Neurofeedback and neural self-regulation: a new perspective based on allostasis.

Authors:  Arash Mirifar; Andreas Keil; Felix Ehrlenspiel
Journal:  Rev Neurosci       Date:  2022-02-07       Impact factor: 4.703

5.  Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task.

Authors:  Lorraine Perronnet; Anatole Lécuyer; Marsel Mano; Elise Bannier; Fabien Lotte; Maureen Clerc; Christian Barillot
Journal:  Front Hum Neurosci       Date:  2017-04-20       Impact factor: 3.169

6.  Development and Pilot Test of a Virtual Reality Respiratory Biofeedback Approach.

Authors:  Johannes Blum; Christoph Rockstroh; Anja S Göritz
Journal:  Appl Psychophysiol Biofeedback       Date:  2020-05-02

7.  Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback.

Authors:  Vadim Zotev; Ahmad Mayeli; Masaya Misaki; Jerzy Bodurka
Journal:  Neuroimage Clin       Date:  2020-06-27       Impact factor: 4.881

8.  A Pilot Adaptive Neurofeedback Investigation of the Neural Mechanisms of Implicit Emotion Regulation Among Women With PTSD.

Authors:  Shelby S Weaver; Rasmus M Birn; Josh M Cisler
Journal:  Front Syst Neurosci       Date:  2020-07-03

Review 9.  Neurofeedback versus psychostimulants in the treatment of children and adolescents with attention-deficit/hyperactivity disorder: a systematic review.

Authors:  Bashar Razoki
Journal:  Neuropsychiatr Dis Treat       Date:  2018-10-30       Impact factor: 2.570

10.  A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction.

Authors:  Junjie Bu; Chang Liu; Huixing Gou; Hefan Gan; Yan Cheng; Mengyuan Liu; Rui Ni; Zhen Liang; Guanbao Cui; Ginger Qinghong Zeng; Xiaochu Zhang
Journal:  Front Neurosci       Date:  2021-07-06       Impact factor: 4.677

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