The brain functional connectivity network is complex, generally constructed using correlations between the regions of interest (ROIs) in the brain, corresponding to a parcellation atlas. The brain is known to exhibit a modular organization, referred to as "functional segregation." Generally, functional segregation is extracted from edge-filtered, and optionally, binarized network using community detection and clustering algorithms. Here, we propose the novel use of exploratory factor analysis (EFA) on the correlation matrix for extracting functional segregation, to avoid sparsifying the network by using a threshold for edge filtering. However, the direct usability of EFA is limited, owing to its inherent issues of replication, reliability, and generalizability. In order to avoid finding an optimal number of factors for EFA, we propose a multiscale approach using EFA for node-partitioning, and use consensus to aggregate the results of EFA across different scales. We define an appropriate scale, and discuss the influence of the "interval of scales" in the performance of our multiscale EFA. We compare our results with the state-of-the-art in our case study. Overall, we find that the multiscale consensus method using EFA performs at par with the state-of-the-art.Clinical relevance: Extracting modular brain regions allows practitioners to study spontaneous brain activity at resting state.