ЭЭГ ДЕРЕКТЕРІ БОЙЫНША БАЛАЛАРДЫҢ ЗЕЙІНІН ОБЪЕКТИВТІ БАҒАЛАУ ҮШІН НЕЙРОНДЫҚ ЖЕЛІЛЕРДІ ҚОЛДАНУ

Abstract

This article considers the problem of objective assessment of attention in primary school children based on the analysis of electroencephalographic (EEG) data using machine learning methods. The relevance of the study lies in the need for early detection of attention disorders and the development of evidence-based approaches to psychological and pedagogical support for children with special needs. The article analyzes the characteristic markers of EEG associated with the level of attention and cognitive load. Based on the identified features, a classification model was created that includes signal preprocessing, spectral characteristics extraction, and the use of neural network algorithms. The results obtained demonstrate the possibility of reliably distinguishing between the states of “attention” and “inattention” with high accuracy. This study confirms the effectiveness of using EEG technologies in combination with modern data analysis methods for objective assessment of attention and can serve as a basis for further development of diagnostic tools in the special and inclusive education system.