Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing.

The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and feature selection as a means of uncovering feature that are altered between conditions.