  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.
