Performance of Virtual Stimulus Motion Based on the SSVEP-BCI

In this paper, a brain-computer interface (BCI) paradigm based on steady-state visually evoked potentials (SSVEP) is used to determine the motion effects of stimulus targets on the classification accuracy and signal-to-noise ratio (SNR) in a virtual environment. Several paths including fixed and up/down with different speeds were set to look for performance differences. Experimental results show that the accuracy with moving targets is sometimes better than that for conventional non-moving targets, and even the relative SNR is improved with motion on occasions. This provides a basis for intensive study of the influence of moving objects on BCIs and target detection in real environments.