Literature DB >> 28007612

Low Motivational Incongruence Predicts Successful EEG Resting-state Neurofeedback Performance in Healthy Adults.

Laura Diaz Hernandez1, Kathryn Rieger2, Thomas Koenig2.   

Abstract

Neurofeedback is becoming increasingly sophisticated and widespread, although predictors of successful performance still remain scarce. Here, we explored the possible predictive value of psychological factors and report the results obtained from a neurofeedback training study designed to enhance the self-regulation of spontaneous EEG microstates of a particular type (microstate class D). Specifically, we were interested in life satisfaction (including motivational incongruence), body awareness, personality and trait anxiety. These variables were quantified with questionnaires before neurofeedback. Individual neurofeedback success was established by means of linear mixed models that accounted for the amount of observed target state (microstate class D contribution) as a function of time and training condition: baseline, training and transfer (results shown in Diaz Hernandez et al.). We found a series of significant negative correlations between motivational incongruence and mean percentage increase of microstate D during the condition transfer, across-sessions (36% of common variance) and mean percentage increase of microstate D during the condition training, within-session (42% of common variance). There were no significant correlations related to other questionnaires, besides a trend in a sub-scale of the Life Satisfaction questionnaire. We conclude that motivational incongruence may be a potential predictor for neurofeedback success, at least in the current protocol. The finding may be explained by the interfering effect on neurofeedback performance produced by incompatible simultaneously active psychological processes, which are indirectly measured by the Motivational Incongruence questionnaire.
Copyright © 2016. Published by Elsevier Ltd.

Entities:  

Keywords:  EEG; microstates; motivational incongruence; neurofeedback success; prediction; resting state

Mesh:

Year:  2016        PMID: 28007612     DOI: 10.1016/j.neuroscience.2016.12.005

Source DB:  PubMed          Journal:  Neuroscience        ISSN: 0306-4522            Impact factor:   3.590


  8 in total

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4.  Individual Sensory Modality Dominance as an Influential Factor in the Prefrontal Neurofeedback Training for Spatial Processing: A Functional Near-Infrared Spectroscopy Study.

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7.  Effects of virtual reality-based feedback on neurofeedback training performance-A sham-controlled study.

Authors:  Lisa M Berger; Guilherme Wood; Silvia E Kober
Journal:  Front Hum Neurosci       Date:  2022-08-12       Impact factor: 3.473

8.  A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence.

Authors:  Erica D Floreani; Silvia Orlandi; Tom Chau
Journal:  Front Hum Neurosci       Date:  2022-09-23       Impact factor: 3.473

  8 in total

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