Fumanal Idocin, JavierVidaurre Arbizu, CarmenFernández Fernández, Francisco JavierGómez Fernández, MarisolAndreu-Pérez, JavierPrasad, M.Bustince Sola, Humberto2024-05-222024-05-222024Fumanal-Idocin, J., Vidaurre, C., Fernandez, J., Gómez, M., Andreu-Perez, J., Prasad, M., Bustince, H. (2024) Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface. Pattern Recognition, 145, 1-11. https://doi.org/10.1016/j.patcog.2023.109924.0031-320310.1016/j.patcog.2023.109924https://academica-e.unavarra.es/handle/2454/48167In 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 a 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- Computer 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 obtain 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 a 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.application/pdfeng© 2023 The Author(s). This is an open access article under the CC BY-NC-ND license.Brain-computer interfaceMotor imageryPenalty functionAggregation functionsClassificationSignal processinginfo:eu-repo/semantics/article2024-05-22info:eu-repo/semantics/openAccess