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Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals
In this study, we focused on the verification of suitable aggregation operators
enabling accurate differentiation of selected neurophysiological features extracted from
resting-state electroencephalographic recordings of patients who were diagnosed
with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-
based operators using traditional classification results as an input to the procedure
of establishing the fuzzy measure densities. The dataset applied in the study was a
collection of variables characterizing the organization of the neural networks computed
using the minimum spanning tree (MST) algorithms obtained from signal-spaced
functional connectivity indicators and calculated separately for predefined frequency
bands using classical linear Granger causality (GC) measure. In the series of numerical
experiments, we reported the results of classification obtained using numerous
generalizations of the Choquet integral and other aggregation functions, which were
tested to find the most appropriate ones. The obtained results demonstrate that the
classification accuracy can be increased by 1.81% using the extended versions of
the Choquet integral called in the literature, namely, generalized Choquet integral or
pre-aggregation operators.
