In recent years, the increase in healthcare data demands the adoption of fog computing for fast processing and analysis of large data volumes. Fog computing enables real-time data computations that serves closely to healthcare devices. This study proposes the analysis and computations of EEG healthcare data using Pareto optimization-based fog computing. The proposed 2DP-FHS technique selects a fog head among heterogeneous fog devices to manage the EEG data within the fog layer. Additionally, an alternative fog head is designated to ensure continuous operation within the fog layer. The study considers various scenarios and unbiased fog devices in the simulation. Further, it presents the delay analysis varying the number of EEG and fog devices. The results show the effectiveness of the proposed technique across all scenarios highlighting its real-time applicability for various healthcare centers requiring time-critical EEG applications.