Related Experiment Video
Updated: Jun 20, 2026
07:05
A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
Published on: August 24, 2017
Identifying indices of learning for alpha neurofeedback training
1Department of Applied Social Sciences (Psychology Department), Canterbury Christ Church University, Canterbury, Kent CT1 1QU, UK. td31@canterbury.ac.uk
Applied Psychophysiology and Biofeedback
|September 18, 2009
Summary
Neurofeedback training (NFT) showed no significant learning beyond baseline levels. Researchers recommend using baseline measures and focusing on within-session changes for accurate assessment of alpha neurofeedback effectiveness.
Area of Science:
- Neuroscience
- Cognitive Science
- Biofeedback
Background:
- Neurofeedback (NFT) is used in clinical and healthy populations.
- Standardized methods for measuring learning in NFT are lacking.
- Defining successful learning in NFT remains a challenge.
Purpose of the Study:
- To examine changes in alpha neurofeedback learning.
- Investigate three measures: amplitude, percent time, and integrated alpha.
- Analyze changes across four assessment methods: within/across sessions and compared to baseline.
Main Methods:
- Participants underwent 10 weekly sessions of eyes-open alpha (8-12 Hz) neurofeedback training (NFT) at Pz.
- Measured changes in amplitude, percent time, and integrated alpha.
- Compared within-session and across-session changes with and without baseline.
Main Results:
- All three measures showed within-session changes, but these were returns to baseline, not increases.
- Across-session changes were only seen in amplitude.
- Baseline comparisons indicated NFT did not induce changes beyond baseline levels.
Conclusions:
- Baseline measures are crucial for identifying genuine learning in NFT.
- Amplitude and percent time measures should be used independently, not integrated.
- Focusing on within-session changes may be more effective for assessing NFT-induced alterations.
