Understanding how information flows between different brain regions during cognitive processes is fundamental to neuroscience. In this work, we apply Information Theory to analyze brain activity under Motor task and Resting State using task-based fMRI data from the Human Connectome Project. Specifically, we employ a range of entropic tools—including Entropy Density, Effective Measure Complexity, and Informational Distance—to capture both linear and non-linear dynamics in brain connectivity. Unlike conventional methods that rely on predefined models or assumptions, our entropic approach does not assume specific underlying dynamics; instead, it captures the intrinsic complexity of the signals by quantifying randomness, structure, and the potential for creating novel patterns.