Functional organization of the brain in the period of preparation for recognizing fragmented images in seven- to eight-year-old children and adults

Machine learning algorithms for age prediction based on linear and non-linear parameters of electroencephalogram data

“…While Zoubi et al (2018) demonstrated the potential of the SVR model, current research suggests that the Decision Trees model outperforms other machine learning models in accurately forecasting age in specific research contexts.Current research could further be improved to study connectivity between brain networks. According toFarber et al (2014), age-related differences in connectivity during the preparation for recognizing fragmented images, provide valuable insights into the neural mechanisms underlying attention and working memory. Their findings indicated variations in connectivity patterns between adults and children, suggesting age-related differences in the functional organization of the brain during focused attention.…”