Optimal design of motor learning experiments informed by Monte-Carlo simulation. (24th August 2021) - Parmar, Pritesh N.; Patton, James L.
In our field of neuro-rehabilitation, it is often difficult to have patients endure a long session of training, and we seek an estimate of minimum practice trials. In such cases, motor learning measurements across trials are usually exponentially decaying transient signals. Here we employed Monte-Carlo methods to determine the minimum number of samples required from transient responses. We tracked the accuracy of recovery of synthesised data to reveal a prescription for the minimum number of samples for a robust identification of the underlying learning process, given preliminary estimates of the time constant and noise levels. Our results revealed a systematic relationship for the minimum number of samples required from transient signals that can be used as a stopping criteria for data collection. We also evaluated these results by using a past motor learning study and determining the minimum number of required samples (trials) to best estimate learning curves.