Fumanal-Idocin, Javier and Vidaurre, Carmen and Fernández, Javier and Gómez, Marisol and Andreu-Perez, Javier and Prashad, Mukesh and Bustince, Humberto (2024) Supervised Penalty-based Aggregation Applied to Motor-Imagery based Brain-Computer-Interface. Pattern Recognition, 145. p. 109924. DOI https://doi.org/10.1016/j.patcog.2023.109924
Fumanal-Idocin, Javier and Vidaurre, Carmen and Fernández, Javier and Gómez, Marisol and Andreu-Perez, Javier and Prashad, Mukesh and Bustince, Humberto (2024) Supervised Penalty-based Aggregation Applied to Motor-Imagery based Brain-Computer-Interface. Pattern Recognition, 145. p. 109924. DOI https://doi.org/10.1016/j.patcog.2023.109924
Fumanal-Idocin, Javier and Vidaurre, Carmen and Fernández, Javier and Gómez, Marisol and Andreu-Perez, Javier and Prashad, Mukesh and Bustince, Humberto (2024) Supervised Penalty-based Aggregation Applied to Motor-Imagery based Brain-Computer-Interface. Pattern Recognition, 145. p. 109924. DOI https://doi.org/10.1016/j.patcog.2023.109924
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Brain–computer interface; Motor imagery; Penalty function; Aggregation functions; Classification; Signal processing |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 23 Jan 2023 17:33 |
Last Modified: | 16 May 2024 21:40 |
URI: | http://repository.essex.ac.uk/id/eprint/34608 |
Available files
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Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0