Evaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. The multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a \textit{time-frequency} model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: \textit{Common Spatial Patterns}, \textit{Functional Connectivity}, and \textit{Event-Related De/Synchronization}. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: \textit{i}) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; \textit{ii}) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand versus right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses.