The structure and cognitive functions of the brain undergo significant changes throughout the lifespan, making electroencephalography (EEG)-based brain age clustering a powerful method for studying brain functional connectivity. Unfortunately, the application of clustering methods for EEG-based brain age clustering analysis has been extremely rare in recent years. Additionally, existing clustering methods face several challenges, including a strong dependence on predefined cluster numbers. To address these challenges, we propose an adaptive representation learning framework for electroencephalography-based brain age clustering, called ARLC-Net. Specifically, ARLC-Net integrates cluster number determination with unsupervised representation learning into a reinforcement learning framework, dynamically determining the optimal number of clusters using a Markov decision process. It also introduces a clustering-driven reward function to improve the separation between clusters. Experimental validation on both TUAB and CHBMP datasets demonstrates the efficacy of our approach. Spatial analysis further quantifies EEG spectral features and spatial dispersion of functional networks across age groups, elucidating temporal dynamics in neural oscillatory power and functional network topology during brain development and aging. As the first clustering method in this field requiring no predefined cluster numbers, this study provides novel perspectives and tools for exploring relationships between functional brain connectivity and age.

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