M. Antelis, Javier
Montesano, Luis
Ramos-Murguialday, Ander
Birbaumer, Niels
Minguez, Javier
Illustration of the model and metric properties.
<p>The left panel shows three datasets of temporal signals representing predictor variables at , and . The upper panel shows the predictands variables , which are identical to the predictors (i.e. they correspond to a linear model with ). Each dataset contains signals and 90% of them was used to train a linear regression model while the remaining 10% was used to evaluate performance. The linear regression model was used predict each dataset from itself and the others. For each case, the reconstructed signals and correlation results are shown in the middle panel. The effect of the artifact is revealed in the usage of the regression model and correlation to validate datasets 1 and 2, where the correlation values are approximately 0.23, despite having different frequencies.</p>
Anatomy and physiology;Musculoskeletal system;robotics;Neurological system;Motor systems;electrophysiology;biotechnology;Bioengineering;Biomedical Engineering;Computational biology;Biological data management;computational neuroscience;neuroscience;signal processing;metric
2013-04-17
https://plos.figshare.com/articles/_Illustration_of_the_model_and_metric_properties_/684720

10.1371/journal.pone.0061976.g001