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Recently, visual perception (VP) and visual imagery (VI) paradigms areinvestigated in several brain-computer interface (BCI) studies. VP and VI aredefined as a changing of brain signals when perceiving and memorizing visualinformation, respectively. These paradigms could be alternatives to theprevious visual-based paradigms which have limitations such as fatigue and lowinformation transfer rates (ITR). In this study, we analyzed VP and VI toinvestigate the possibility to control BCI. First, we conducted atime-frequency analysis with event-related spectral perturbation. In addition,two types of decoding accuracies were obtained with convolutional neuralnetwork to verify whether the brain signals can be distinguished from eachclass in the VP and whether they can be differentiated with VP and VIparadigms. As a result, the 6-class classification performance in VP was 32.56%and the binary classification performance which classifies two paradigms was90.16%.

Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks