In electroencephalography (EEG) neurofeedback protocols,
trainees receive feedback about the spectral power of the target
brain wave oscillation and are tasked to increase or decrease this
feedback signal compared to a predetermined threshold. In a recent
computational analysis of a neurofeedback protocol it was shown that
the placement of the threshold has a major impact on the learning
rate and that placed too low or too high leads to no learning or even
unlearning, respectively. However, the optimal threshold placement is
not known in real-life scenarios. Here, these analyses were extended
to assess whether an adaptive-mean threshold procedure could lead
to faster learning curves. The results indicate that such a procedure is
indeed superior to a fixed-mean procedure and that the distribution
of asymptotic EEG power values converges to that obtained with
the optimal-threshold procedure. Surprisingly, the adaptive-mean
procedure leads to thresholds that are higher than the optimal one,
which is explained through the increase in threshold lagging behind
the increase in the likelihood of activation of the target neurons. To
date, no computational model was used to compute the cost-efficiency
of EEG neurofeedback procedures. The current simulation (within
the specific reinforcement schedule) demonstrated a 35% reduction
in training time, which could translate into sizeable financial savings.
This study demonstrates the utility of computational methods in
neurofeedback research and opens up further developments that
tackle specific neurofeedback protocols to assess their real-life cost-
efficiency
