10.1371/journal.pone.0061976.g001
Javier M. Antelis
Luis Montesano
Ander Ramos-Murguialday
Niels Birbaumer
Javier Minguez
Illustration of the model and metric properties.
2013
Public Library of Science
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 01:18:40
article
https://plos.figshare.com/articles/_Illustration_of_the_model_and_metric_properties_/684720
<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>