Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain

Kajal Patel; James Henshaw; Heather Sutherland; Jason R Taylor; Alexander J Casson; Karen Lopez-Diaz; Christopher A Brown; Anthony K P Jones; Manoj Sivan; Nelson J Trujillo-Barreto

doi:10.3389/fnins.2020.620666

Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain

Front Neurosci. 2021 Feb 22:14:620666. doi: 10.3389/fnins.2020.620666. eCollection 2020.

Authors

Affiliations

¹ School of Medicine, University of Manchester, Manchester, United Kingdom.
² Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom.
³ Department of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom.
⁴ Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom.
⁵ Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, United Kingdom.

Abstract

Objective: Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states.

Methods: Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman's Test and correlated with changes in pain scores using Pearson's correlation.

Results: There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy (r = -0.45, p = 0.03), mean dwell time (r = -0.48, p = 0.04) and transition probability from a low to high state (r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants.

Conclusion: There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain.

Keywords: EEG biofeedback; alpha rhythm; alpha states; chronic pain; neurofeedback.

Abstract

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