  Intervals are a popular way to represent the uncertainty related to data, in
which we express the vagueness of each observation as the width of the
interval. However, when using intervals for this purpose, we need to use the
appropriate set of mathematical tools to work with. This can be problematic due
to the scarcity and complexity of interval-valued functions in comparison with
the numerical ones. In this work, we propose to extend a generalization of the
Sugeno integral to work with interval-valued data. Then, we use this integral
to aggregate interval-valued data in two different settings: first, we study
the use of intervals in a brain-computer interface; secondly, we study how to
construct interval-valued relationships in a social network, and how to
aggregate their information. Our results show that interval-valued data can
effectively model some of the uncertainty and coalitions of the data in both
cases. For the case of brain-computer interface, we found that our results
surpassed the results of other interval-valued functions.
