Understanding the dynamics that emerge from large-scale brain models is challenging due to the high complexity of the system. In this project, we build upon the study in [1] to investigate the emergence of complex spatiotemporal patterns in a network of 90 interconnected brain regions, each modeled as an excitatory-inhibitory (E-I) network whose dynamics are described by next generation neural mass models. We analyze the homogeneous oscillatory state of the system and study its stability under uniform perturbations. To assess its stability against non-uniform perturbations, we apply the Master Stability Function formalism, which allows us to characterize the emergence of complex spatiotemporal patterns from the unstable directions of the homogeneous state. [1] Clusella, P., Deco, G., Kringelbach, M. L., Ruffini, G., & Garcia-Ojalvo, J. (2023). Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks. PLOS Computational Biology, 19(4), e1010781.