Singular Spectrum Analysis as a data-driven approach to the analysis of motor adaptation time series

Motor adaptation is a form of learning to re-establish desired movements in novel situations. To probe motor adaptation, one can replicate such conditions experimentally by imposing sustained perturbation during movement. Exposure perturbations initially causes an abrupt change relevant performance variables, followed gradual return baseline behaviour. The resulting time series exhibit persistent properties related structural changes underlying control and transitory trial-to-trial variations. global trend, signifying the change, often assessed averaging predefined bins or nonlinear model fitting. However, these methods study require priori decisions produce accurate fits. Here, we test data-driven approach called Singular Spectrum Analysis (SSA) assess trend. In SSA, first decompose into components that represent either trend additional variations, secondly, select component(s) corresponding using spectral analysis. this paper, will use simulated data compare reconstruction SSA with applied filter fitting studies apply real obtained split-belt adaptation. simulations, show reconstructed fast-initial component entire trends more accurately than filtering methods. addition, also successfully from data. Therefore, might be useful series.