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Related Experiment Video

Updated: Jun 28, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

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

Published on: August 24, 2017

Predicting Theta/Alpha Neurofeedback Success through Psychological and Personality Profiles: A Hybrid Approach Using

Siminsadat Hasheminia1, Nasrin Sho'ouri1, Maryam Tayefeh Mahmoudi2

  • 1Department of Biomedical Engineering, CT.C., Islamic Azad University, Tehran, Iran.

Journal of Medical Signals and Sensors
|June 19, 2026
PubMed
Summary

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This summary is machine-generated.

Neurofeedback success depends on personality and cognitive factors, not just brainwaves. Understanding these predictors helps tailor neurofeedback training for better outcomes.

Area of Science:

  • Neuroscience
  • Psychology
  • Machine Learning

Background:

  • Investigated psychological, cognitive, and neurophysiological factors in neurofeedback training.
  • Examined personality (MBTI), impulsivity (UPPS), IQ (Raven's), and EEG frequencies.

Purpose of the Study:

  • Identify predictors of success in theta/alpha neurofeedback.
  • Analyze relationships between psychological/cognitive factors and neural self-regulation.

Main Methods:

  • Quantitative descriptive-analytical design.
  • Collected data from six healthy participants over eight neurofeedback sessions.
  • Utilized Multilayer Perceptron (MLP) neural network and Elastic Net regression in Python.

Main Results:

Keywords:
Elastic Net regressionElectroencephalogrammultilayer perceptronneurofeedback trainingrelative power

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  • EEG frequency bands increased consistently; baseline measures predicted outcomes.
  • Elastic Net identified Judging trait, impulsivity, and delta power as key predictors.
  • Negative correlations between theta and alpha bands indicated improved cognitive differentiation.

Conclusions:

  • Neurofeedback responsiveness is influenced by neurophysiological and psychological-cognitive factors.
  • Integrating psychological profiling with neural data optimizes individualized neurofeedback.