Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface
Human-Computer Interaction
2021-07-02 v2 Computer Vision and Pattern Recognition
Numerical Analysis
Numerical Analysis
Abstract
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.
Cite
@article{arxiv.2011.09831,
title = {Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface},
author = {Javier Fumanal-Idocin and Zdenko Takáč and Javier Fernández Jose Antonio Sanz and Harkaitz Goyena and Ching-Teng Lin and Yu-Kai Wang and Humberto Bustince},
journal= {arXiv preprint arXiv:2011.09831},
year = {2021}
}