Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface

Autor:	Fumanal-Idocin, Javier, Takáč, Zdenko, Sanz, Javier Fernández Jose Antonio, Goyena, Harkaitz, Lin, Ching-Teng, Wang, Yu-Kai, Bustince, Humberto
Rok vydání:	2020
Předmět:	Computer Science - Human-Computer Interaction Computer Science - Computer Vision and Pattern Recognition Mathematics - Numerical Analysis
Druh dokumentu:	Working Paper
DOI:	10.1109/TFUZZ.2021.3092824
Popis:	In this work we study the use of moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data. To do so, we introduce the notion of interval-valued moderate deviation function and we study in particular those interval-valued moderate deviation functions which preserve the width of the input intervals. Then, we study how to apply these functions to construct interval-valued aggregation functions. We have applied them in the decision making phase of two Motor-Imagery Brain Computer Interface frameworks, obtaining better results than those obtained using other numerical and intervalar aggregations.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.09831 Zobrazit plný text záznamu View this record from Arxiv