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

TL;DR

This paper introduces interval-valued moderate deviation functions that preserve interval width and applies them to improve decision-making in Motor-Imagery Brain-Computer Interfaces, outperforming existing aggregation methods.

Contribution

It proposes a novel class of interval-valued moderate deviation functions and demonstrates their effectiveness in BCI decision-making tasks.

Findings

Better decision accuracy in BCI frameworks using the new aggregation functions.

Improved performance over existing numerical and interval aggregations.

Validation on Motor-Imagery BCI datasets shows significant gains.

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.