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Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface.
- Source :
-
Pattern Recognition . Jan2024, Vol. 145, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
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 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. • We develop relaxed versions of Restricted Equivalence Functions. • We aggregate information from different classifiers trained on different wave bands. • The new aggregations learn from the input data to discriminate between classes. • The novel aggregation functions can be adapted to the original data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 145
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 172778081
- Full Text :
- https://doi.org/10.1016/j.patcog.2023.109924