Adding noise to a weak input signal can enhance the response of a non-linear system, a phenomenon known as stochastic resonance (SR). SR has been demonstrated in a variety of diverse sensory systems including the visual system, where visual noise enhances human motion perception and detection performance. The SR effect has not been extensively studied in brain-computer interface (BCI) applications. This study compares the performance of BCIs based on SR-influenced steady-state motion visual evoked potentials. Stimulation paradigms were used between a periodically monochromatic motion-reversing simple ring and complex alternating checkerboard stimuli. To induce the SR effect, dynamic visual noise was masked on both the periodic simple and complex stimuli. Offline results showed that the recognition accuracy of different stimulation targets followed an inverted U-shaped function of noise level, which is a hallmark of SR. With the optimal visual noise level, the proposed visual noise masked checkerboard BCI paradigm achieved faster and more stable detection performance due to the noise-enhanced brain responses. This work demonstrates that the SR effect can be employed in BCI applications and can achieve considerable performance improvements.