Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals - Plechawska-Wójcik, Małgorzata; Karczmarek, Paweł; Krukow, Paweł; Kaczorowska, Monika; Tokovarov, Mikhail; Jonak, Kamil (Frontiers in Neuroinformatics, 2021)
In this study, we focus on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from electroencephalographic recordings as belonging to patients diagnosed with schizophrenia or healthy controls. We build the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing of the fuzzy measure densities. The dataset applied in the study is a collection of variables characterizing the organization of the neural networks computed with an application of the Minimum Spanning Tree algorithms obtained from signal-spaced functional connectivity indicators, calculated separately for predefined frequency bands with the usage of Classical Linear Granger Causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained with numerous generalizations of the Choquet integral and other aggregation functions which were tested in order to find the most appropriate ones. The obtained results prove that the classification accuracy can be increased by 1.81 percentage point using the extended versions of Choquet integral called in the literature Generalized Choquet integral or pre-aggregation operators.