Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
Fecha
2021Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
Impacto
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10.1109/TFUZZ.2021.3092824
Resumen
In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two MotorImagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation funct ...
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In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two MotorImagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions
which preserve the width of the input intervals. In order to apply them in a MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation
functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued OWA operators, and obtaining competitive results versus some non aggregation-based frameworks. [--]
Materias
Electroencephalography,
Brain-computer interface,
Moderate deviations,
Interval-valued aggregation,
Motor imagery,
Admissible orders,
Classification,
Signal processing
Editor
IEEE
Publicado en
IEEE Transactions on Fuzzy Systems (2021)
Departamento
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC /
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
Versión del editor
Entidades Financiadoras
Javier Fumanal Idocin’s, Jose Antonio Sanz’s, Javier Fernandez’s, Harkaitz Goyena’s and Humberto Bustince’s research has been supported by the project PID2019-108392GB I00 (AEI/10.13039/501100011033). Z. Takac acknowledges the support of the grant VEGA
1/0545/20.