  Recently, visual perception (VP) and visual imagery (VI) paradigms are
investigated in several brain-computer interface (BCI) studies. VP and VI are
defined as a changing of brain signals when perceiving and memorizing visual
information, respectively. These paradigms could be alternatives to the
previous visual-based paradigms which have limitations such as fatigue and low
information transfer rates (ITR). In this study, we analyzed VP and VI to
investigate the possibility to control BCI. First, we conducted a
time-frequency analysis with event-related spectral perturbation. In addition,
two types of decoding accuracies were obtained with convolutional neural
network to verify whether the brain signals can be distinguished from each
class in the VP and whether they can be differentiated with VP and VI
paradigms. As a result, the 6-class classification performance in VP was 32.56%
and the binary classification performance which classifies two paradigms was
90.16%.
