Mental rotation cognitive tasks based on motor imagery (MI) have excellent predictability for individual’s motor imagery ability. In order to explore the relationship between motor imagery and behavioral data, in this study, we asked 10 right-handed male subjects to participate in the experiments of mental rotation tasks based on corresponding body parts pictures, and we therefore obtained the behavioral effects according to their reaction time (RT) and accuracy (ACC). Later on, we performed Pearson correlation analysis between the behavioral data and the scores of the Movement Imagery Questionnaire-Revised(MIQ-R). For each subject, the results showed significant angular and body location effect in the process of mental rotation. For all subjects, the results showed that there were correlations between the behavioral data and the scores of MIQ-R. Subjects who needed the longer reaction time represented lower motor imagery abilities in the same test, and vice versa. This research laid the foundation for the further study on brain electrophysiology in the process of mental rotation based on MI.
Brain–computer interface (BCI) technology enable humans to interact with external devices by decoding their brain signals. Despite it has made some significant breakthroughs in recent years, there are still many obstacles in its applications and extensions. The current used BCI control signals are generally derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of limb movement intention. Therefore, additional sources of brain signals for controlling BCI systems need to be explored. Brain signals derived from the cognitive brain areas are more intuitive and effective. These signals can be used for expand the brain signal sources as a new approach. This paper reviewed the research status of cognitive BCI based on the single brain area and multiple hybrid brain areas, and summarized its applications in the rehabilitation medicine. It’s believed that cognitive BCI technologies would become a possible breakthrough for future BCI rehabilitation applications.