Network studies of brain connectivity have demonstrated that the highly connected area, or hub, is a vital feature of human functional and structural brain organization. Hubs identify which region plays an important role in cognitive/sensorimotor tasks. In addition, a complex visuomotor learning skill causes specific changes of neuronal activation across brain regions. Accordingly, this study utilizes the hub as one of the features to map the visuomotor learning tasks and their dynamic functional connectivity (dFC). The electroencephalogram (EEG) data recorded under three different behavior conditions were investigated: motion only (MO), vision only (VO), and tracking (Tra) conditions. Here, we used the phase locking value (PLV) with a sliding window (50 ms) to calculate the dFC at four distinct frequency bands: 8-12 Hz (alpha), 18-22 Hz (low beta), 26-30 Hz (high beta) and 38-42 Hz (gamma), and the eigenvector centrality to evaluate the hub identification. The Gaussian Mixture Model (GMM) was applied to investigate the dFC patterns. The results showed that the dFC patterns with the hub feature represent the characteristic of neuronal activities under visuomotor coordination.

In this study, we developed a human attention model for smooth human-robot interaction. The model consists of the saliency map generation module and manipulation map generation module. The manipulation map describes top-down factors, such as human face, hands and gaze in the input image. To evaluate the proposed model, we applied the model to a magic video, and measured human gaze points during watching the video. Based on the experimental results, the proposed model can better explain human attention than the saliency map.

For smooth interaction between human and robot, the robot should have an ability to manipulate human attention and behaviors. In this study, we developed a visual attention model for manipulating human attention by a robot. The model consists of two modules, such as the saliency map generation module and manipulation map generation module. The saliency map describes the bottom-up effect of visual stimuli on human attention and the manipulation map describes the top-down effect of face, hands and gaze. In order to evaluate the proposed attention model, we measured human gaze points during watching a magic video, and applied the attention model to the video. Based on the result of this experiment, the proposed attention model can better explain human visual attention than the original saliency map.