Hybridization in brain-computer interface (BCI) is an active research assessed by integrating more than two modalities to control multiple user commands and enhance the BCI performance. However, evaluation of these hybrid modalities is substantially challenging which necessitates robust techniques that can efficiently analyze the underlying dynamics of the brain signal. Fuzzy entropy based approaches are the objective measurements of evaluating the dynamic electroencephalography (EEG) complexity reflecting its ability to adapt to the variabilities of the brain. In this study, we proposed an empirical mode decomposition (EMD) featured fuzzy entropy method by applying coarse-graining to the time series signal at a multi-scale level (EMFuzzyEn) to efficiently discriminate the target and non-target events, and improve classification performance during simultaneous steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) BCI system framework. The proposed hybrid BCI framework with applied canonical correlation analysis (CCA) and EMFuzzyEn algorithm to the SSVEP and RSVP classifications achieved an offline accuracy of 87.41% for 12-channel combination and an online accuracy of 84.69% for 2-channel combination. The results demonstrated that the proposed algorithm enhanced the BCI performance by 5.85% (offline) and 6.78% (online) compared to our previous ERP-based classification results. Further, the EMFuzzyEn algorithm achieved highest classification accuracy across all channel combinations compared to the multi-scale fuzzy (MFuzzyEn) and non-fuzzy (ShEn, AppEn, CondEn) entropy algorithms. Overall, our empirical results confirmed that the proposed EMFuzzyEn algorithm significantly increased the EEG complexity during target tasks thereby enhancing the discrimination between target and non-target tasks, which might be a potential indicator to measure the brain dynamics and enhance the BCI performance.