Abstract/Summary

Visual-motor coordination is an essential function of human motion control, which requires interactions of multiple brain regions. Visual tracking is a behavioural task that requires intensive visual-motor coordination, which makes it a good paradigm to study the underlying mechanism of visual-motor coordination. In this research, tracking paradigm was used to study the visual-motor coordination, and both behaviour and electroencephalography (EEG) functional connectivity were analysed. The behavioural analysis explored the anticipatory characteristic of human motion control. In the tracking paradigm, participants were asked to trace a target moving with constant speed along a circular trajectory. Two different types of tracking paradigm were applied in the research. Firstly, the full visibility tracking trials were performed, in which participants had the full visibility of the target movement. Participants showed weak anticipatory behaviour in the full visibility tracking trials. In order to observe stronger anticipatory behaviour, the intermittent tracking trials were then performed, in which two target-invisible zones were added. It was found that participants applied two distinctive control modes of visual-motor coordination in the target-visible zone and target-invisible zone, respectively. The result showed that the target-invisible zone made participants perform anticipatory control of visual tracking. In order to identify the brain activities related to visual processing and motion control separately in the visual-motor feedback loops, two reference conditions were designed and compared with the tracking trials. The functional connectivity was defined using phase-locking synchrony, and both static and dynamical features of the network were investigated. For static analysis, the time-averaged graphical properties of functional connectivity were investigated. To investigate dynamical properties, a new dynamical network analysis method was developed based on eigenvector representation of functional connectivity. Both static and dynamic analyses demonstrated significant differences between cortical functional connectivity networks of open and closed visual-motor loop. Additionally, the dynamical network analysis also revealed that the EEG network related to visualmotor coordination undergoes a meta-stable state dynamics in the prime eigenvector space. This method can also potentially be applied to other network system to reveal the meta-stable states structure.