Cognitive Brain Age Estimation

Abstract

Cognitive brain age estimation combines computational techniques with health sciences to assess cognitive health. Traditional methods like neuroimaging and clinical evaluations are costly and not scalable. This research proposes a machine learning-based system for cognitive age estimation using non-invasive data, including speech patterns, behavioral metrics, and lifestyle factors. The system follows a modular architecture with data collection, preprocessing, feature extraction, and predictive modelling. By analyzing behavioral logs, speech characteristics, and lifestyle metrics, it generates real-time cognitive age estimates. This scalable and cost-effective approach, free from neuroimaging, enables deployment in healthcare settings and wearable devices. It also supports large-scale applications, such as public health monitoring and aging studies, enhancing accessibility, early detection, and personalized interventions in cognitive health.