Brain-Computer Interfaces (BCIs) help individuals with severe disabilities communicate using brain activity. Most existing systems are designed for Latin alphabets and overlook the challenges of non-Latin and right-to-left (RTL) scripts (such as connected letters). To address this issue, a hybrid BCI system has been developed using Steady-State Visual Evoked Potential (SSVEP) and Rapid Serial Visual Presentation (RSVP) paradigms. In this method, 36 characters are divided into 3 groups of 12, each further split into 4 subgroups of 3. SSVEP is used to identify the target group, and Triple RSVP is employed to detect the subgroup. The final character is determined using single-frequency SSVEP. Signal processing is performed using Power Spectral Density Analysis (PSDA), wavelet transform, and Support Vector Machine (SVM). Test results on 7 healthy individuals showed a system accuracy of 91.2±3.4% and an Information Transfer Rate (ITR) of 21.5±1.64 bits/min. When SSVEP stimulation time was reduced by 1 second, accuracy remained at 90.5%, while ITR increased to 25.37 bits/min. Unlike Latin-based systems, this one is optimized for complex and right-to-left scripts and performs better than single-modality methods. This advancement marks an important step in developing inclusive BCI technology for non-Latin users.