Use of neural modeling structure for diagnostics of emotional competence of a child with factors including physiological parameters

We consider the practical application of mathematical models of neural networks (multilayer perseptron) for the diagnosis of emotional competence of preschool children on the physiological parameters. Emotional competence was assessed using the psychological test, which consists of 6 scales that determine the recognition and expression of emotion through facial expressions, voice and image. The physiological parameters were used as indicators of EEG spectral density in the standard frequency ranges, as well as indicators of change in autonomic regulation (HR, BP) in response to emotion-generating and intellectual burden. As a result of constructing diagnostic models quality of diagnosis was tested, and describes the possible physiological mechanisms that influence the particular expression of various indicators of emotional competence.