Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation

Abstract

Objective: Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation.

Methods: Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions.

Results: The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy.

Conclusion: Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training.

Significance: Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation.

Keywords: Adaptive feedback; Brain-computer interface; Brain-machine interface; Brain-robot interface; Neurorehabilitation.