A computational approach to developing cost-efficient adaptive-threshold algorithms for EEG neuro feedback

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 costefficiency.