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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.

Noise has been proven to have a beneficial role in non-linear systems, including the human brain, based on the stochastic resonance (SR) theory. Several studies have been implemented on single-modal SR. Cross-modal SR phenomenon has been confirmed in different human sensory systems. In our study, a cross-modal SR enhanced brain–computer interface (BCI) was proposed by applying auditory noise to visual stimuli. Fast Fourier transform and canonical correlation analysis methods were used to evaluate the influence of noise, results of which indicated that a moderate amount of auditory noise could enhance periodic components in visual responses. Directed transfer function was applied to investigate the functional connectivity patterns, and the flow gain value was used to measure the degree of activation of specific brain regions in the information transmission process. The results of flow gain maps showed that moderate intensity of auditory noise activated the brain area to a greater extent. Further analysis by weighted phase-lag index (wPLI) revealed that the phase synchronization between visual and auditory regions under auditory noise was significantly enhanced. Our study confirms the existence of cross-modal SR between visual and auditory regions and achieves a higher accuracy for recognition, along with shorter time window length. Such findings can be used to improve the performance of visual BCIs to a certain extent.

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