A brain-computer interface (BCI) enables the control of external devices by interpreting brain activity without the need for physical movement. Among various neuroimaging techniques, electroencephalography (EEG) is widely used due to its non-invasiveness and high temporal resolution. BCI systems primarily rely on three major paradigms: event-related potential (ERP), motor imagery (MI), and steady-state visual evoked potential (SSVEP). Today, BCIs are employed in practical applications. One of the challenges in long-term BCI usage is user fatigue. To date, fatigue levels across different BCI paradigms have not been systematically compared. This study investigates the effects of fatigue in different BCI paradigms by analyzing frequency-based fatigue biomarkers derived from EEG signals before and after tasks. The biomarkers were compared across three electrodes—Oz, Cz, and Fz—positioned at different scalp locations. The findings indicate that task-induced fatigue increases all frequency-based biomarkers across all three electrodes, whereas rest periods lead to their reduction. Prolonged rest durations resulted in greater reductions; however, in the dataset, the allocated rest time was insufficient for users' fatigue levels to return to baseline. Furthermore, a comparison of the paradigms revealed that MI induces the highest level of fatigue, followed by ERP and SSVEP, respectively. When evaluated using the information transfer rate (ITR) criterion, SSVEP demonstrated the highest ITR among the BCI paradigms. The combination of the highest ITR and lower user fatigue in SSVEP-based BCI paradigm suggests that this paradigm is the most suitable option for practical BCI applications.