Automating visual feedback in H-reflex operant conditioning studies: Feasibility and first steps

Abstract

H-reflex conditioning is a novel targeted-neuroplasticity-based method for the rehabilitation of movement that uses visual feedback to train participants to change the excitability of their reflexes over several months. Present H-reflex conditioning protocols require experimenters to manually control multiple covariates of the H-reflex to ensure the accuracy of the visual feedback. Manual control of these covariates is error prone, labor intensive, and reduces experimenter engagement with participants. Here, as a first step towards a system that automatically ensures the accuracy of visual feedback during H-reflex conditioning, we performed inference and prediction based on multivariate linear regression (MLR) to assess the effect of six covariates of the size of the H-reflex and whether this class of models can be used to predict those effects. Four participants completed experiments where we measured H-reflex size changes in response to changes in each of the six covariates. Inference from the MLR models show that background EMG activity and stimulation current affected H-reflex size in three of the four participants. In addition, our experiments show that MLR models can be used to predict H-reflex size. Compared to an intercept-only model, MLR reduced prediction error by more than 30% $(p < 0.05)$. Our results suggest that automatic adjustment (in response to changes in covariates of the H-reflex) of visual feedback during H-reflex conditioning is possible. With further development, this method could improve feedback during H-reflex operant conditioning, reduce the need for clinicians and researchers to manually control covariates of the H-reflex, and lead to improved H-reflex conditioning protocols for the rehabilitation of movement following neurological injury or illness.