Abstract: Motor imagery (MI) is a common task in brain computer interaction (BCI), but MI is not easy to acquire and control, and there is a phenomenon of "BCI blindness", which limits the practicality of this type of BCI. This paper aims at the identification of Visual Imagery (VI) tasks that are easier to acquire and control, and aims to build VI-based BCI (VI-BCI). 15 subjects were recruited to participate in two kinds of dynamic picture VI tasks, and their EEG data were collected. Then, the EEG microstate method is used to study the differences in microstate time parameters between the two VI tasks, and the eigenvectors are constructed by microstate time parameters with significant differences. Finally, support vector machine (SVM) is used to classify the two kinds of VI tasks. The results show that the highest, the lowest and the average classification accuracy of microstate are 90%, 56% and 80.6 2.58%, respectively. This study shows that the microstate method can effectively extract VI-related EEG features and obtain comparable accuracy. The work is expected to provide ideas for the construction of a new online VI-BCI.

Key words: visual imagery, microstate, EEG, brain computer interaction