Vidaurre Arbizu, Carmen
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Vidaurre Arbizu
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Carmen
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Ingeniería Eléctrica y Electrónica
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Publication Open Access A generalization of the Sugeno integral to aggregate interval-valued data: an application to brain computer interface and social network analysis(Elsevier, 2022) Fumanal Idocin, Javier; Takáč, Zdenko; Horanská, Lubomíra; Da Cruz Asmus, Tiago; Pereira Dimuro, Graçaliz; Vidaurre Arbizu, Carmen; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Institute of Smart Cities - ISCIntervals are a popular way to represent the uncertainty related to data, in which we express the vagueness of each observation as the width of the interval. However, when using intervals for this purpose, we need to use the appropriate set of mathematical tools to work with. This can be problematic due to the scarcity and complexity of interval-valued functions in comparison with the numerical ones. In this work, we propose to extend a generalization of the Sugeno integral to work with interval-valued data. Then, we use this integral to aggregate interval-valued data in two different settings: first, we study the use of intervals in a brain-computer interface; secondly, we study how to construct interval-valued relationships in a social network, and how to aggregate their information. Our results show that interval-valued data can effectively model some of the uncertainty and coalitions of the data in both cases. For the case of brain-computer interface, we found that our results surpassed the results of other interval-valued functions.Publication Open Access Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface(Elsevier, 2024) Fumanal Idocin, Javier; Vidaurre Arbizu, Carmen; Fernández Fernández, Francisco Javier; Gómez Fernández, Marisol; Andreu-Pérez, Javier; Prasad, M.; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Institute of Smart Cities - ISCIn 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.Publication Open Access Optimizando desviaciones moderadas ponderadas para interfaces cerebro ordenador(Universidad de Málaga, 2021) Fumanal Idocin, Javier; Vidaurre Arbizu, Carmen; Gómez Fernández, Marisol; Urío Larrea, Asier; Pereira Dimuro, Graçaliz; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaLas interfaces cerebro-ordenador (BCI) basadas en el análisis de Electroencefalografía (EEG) están compuestas por varios elementos para procesar y clasificar las señales de entrada del cerebro. Una fase relevante de estos sistemas es el módulo de toma de decisiones, en el que la salida de diferentes clasificadores se fusiona en uno solo. En este trabajo proponemos el uso de funciones basadas en desviaciones moderadas con ponderaciones para la fase de toma de decisiones del sistema de BCI de fusión multimodal mejorado (EMF). Las funciones de agregación basadas en desviación moderada (MD) nos permiten elegir el mejor valor para agregar un vector de puntos utilizando una función de desviación moderada. Usando una MD ponderada, también podemos tener en cuenta la importancia relativa de cada dimensión en los datos multidimensionales que estamos agregando. Utilizando estas funciones en el EMF, podemos ponderar cada una de las diferentes señales cerebrales según su importancia, y utilizando la diferenciación automática, también podemos optimizarlas para el problema concreto a solucionar.