Supervised Penalty-based Aggregation Applied to Motor-Imagery based Brain-Computer-Interface

In this paper, we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed versions of restricted equivalence and dissimilarity functions instead of a penalty function. We additionally suggest two different alternatives to train an MCA in a supervised classification task in order to adapt the aggregation to each vector of inputs. We apply the proposed MCA in a Motor Imagery-based Brain Com-puter Interface (MI-BCI) system to improve its decision-making phase. We also evaluate the classical aggregation with our new aggregation procedure in two publicly available datasets. We obtained an accuracy of 82.31% for a left vs. right hand in the Clinical BCI challenge (CBCIC) dataset and a performance of 62.43% for the four-class case in the BCI Competition IV 2a dataset compared to 82.15% and 60.56% using the arithmetic mean. Finally, we have also tested the goodness of our proposal against other MI-BCI systems, obtaining better results than those using other decision-making schemes and Deep Learning on the same datasets.