Identifying Indices of Learning for Alpha Neurofeedback Training

Abstract

Neurofeedback has been around for decades and has applications for both clinical and healthy populations yet there is no standard method for measuring learning or a way of defining successful learning. Thus the aim of this study was to focus on alpha neurofeedback and examine changes in three different measures: amplitude, percent time, and integrated alpha, across four methods: within sessions, across sessions, within sessions compared to baseline, and across sessions compared to baseline. Participants completed 10 weekly sessions of eyes open alpha (8-12 Hz) neurofeedback training (NFT) at Pz. Whilst all three measures showed changes within sessions, the inclusion of baselines revealed that such changes represented a return to baseline levels rather than an increase in alpha. Changes across sessions were only evident in amplitude and inclusion of baseline showed that NFT did not elicit any changes beyond baseline levels. Given this a case is made for incorporating baseline measures when attempting to identify evidence of learning. It is also suggested that both amplitude and percent time measures are used independently rather than incorporate them into a more conservative and less sensitive integrated measure. Finally, focusing on within sessions changes may be a more useful approach in identifying changes resulting from NFT.

A novel self-guided approach to alpha activity training

“…Beta activity did not increase in the alpha training group, indicating that the training elicited a frequency specific effect, and did not simply increase overall EEG power. The present findings add to the growing body of literature on the trainability of alpha activity in relation to relaxation (e.g., Dempster and Vernon, 2009;Egner et al, 2002;Gruzelier, 2002). These specific effects of alpha activity training on alpha activity as opposed to beta activity were evident despite a stringent control condition that included real training of random beta frequencies (Hoedlmoser et al, 2008).…”

Mechanisms of Neurofeedback: A Computation-theoretic Approach

“…For example, Dempster and Vernon (2009) trained participants to increase alpha (8-12 Hz) over Pz. Feedback was analogue and based on the alpha amplitude above the average baseline amplitude.…”

“…They compared amplitude, percent time, and integrated alpha as indices of learning and found that the amplitude measure was superior in showing learning across sessions both with and without baseline correction. Discussion on the appropriate index has not converged and more extensive work is needed, but such an endeavour is very time-consuming (10 weeks/participant in Dempster and Vernon, 2009). Without a measure of effect size to calculate the number of participants needed to obtain a reasonable level of statistical power, such a study could easily suffer from underpowered designs.…”

Predicting Successful Learning of SMR Neurofeedback in Healthy Participants: Methodological Considerations

“…In contrast to Dempster and Vernon (2009), who clearly outlined that the effects of alpha NF training may be better represented by changes within sessions, we were interested in the gradual increase in the EEG amplitudes across the whole course of training. As a matter of fact, the purpose of this report is to present a simple classification scheme, and should be used cautiously.…”