PLS features improve variance explained in motor commands.

<p>We extracted 10 features for each dimensionality reduction and alignment method (PCA: A-C; PLS: D-F). We plotted the variance in each muscle timing variable (colored lines) explained by each feature from the phase- (A, B) and spike-triggered ensembles (B, E) as well as the cumulative variance for the spike-triggered case (C, F). To determine the variance explained by random features (dashed lines) we resampled the torque waveforms and muscle timings 1000 times. We used the 99.5% quantile as a threshold for how much variation could be explained by chance from a single feature. Some higher-ranked features in the PCA analyses had significant contributions (circled) above chance. We repeated the spike-triggered PLS analysis with Δ<i>t</i> included in the <b><i>U</i></b> matrix to ensure that statistical bias did not change the number of important features (G). To compare across all animals (H, I), we normalized the variance explained by the cumulative variance explained when including 10 features. The box plots indicate the proportion of this maximum explanatory power described by each successive feature (N = 7 animals; 298–620 wingstrokes per animal).