On May. 3, 2019, Dr. Xiaorong Gao published 'The effect of visual stimuli noise and fatigue on steady-state visual evoked potentials' in Journal of Neural Engineering.

In many cases, noise in visual stimuli plays an active role in brain information processing. Electroencephalogram (EEG) provides an objective mean to measure brain cognition and information processing, and studies on the effect of noise on EEG can help us better understand the mechanisms involved in information processing. Approach. In this study, visual stimuli, consisting of images with different noise levels, were created using the phase-scrambled method. EEG data evoked by these images were then obtained using the rapid serial visual presentation (RSVP) paradigm and the N-back method was used to induce and assess the fatigue state of subjects. The effect of differing noise modulation levels on EEG in different fatigue states was studied by analyzing the differences in the characteristics of steady-state visual evoked potential (SSVEP). Main results. The study results demonstrated that an image's noise level had a significant impact on the evoked SSVEP characteristics. The amplitude and time delay of induced SSVEP were effectively enhanced by moderately increasing the noise of the visual stimulus images. Fatigue also appeared to affect SSVEP, and the difference in SSVEP characteristics induced by images with different noise levels decreased when the subject was fatigued. Significance. The conclusions of this study provide insights into the relationship between visual stimuli noise and SSVEP characteristics under different fatigue states. This might provide a basis for the study of the brain's mechanisms in regulating attention resources and the stochastic resonance phenomenon. In addition, this study has the potential to provide an objective method for rapid fatigue detection based on EEG.

Paperlink: https://iopscience.iop.org/article/10.1088/1741-2552/ab1f4e