Embodied interaction with complex neuronal data in mixed-reality

The study of natural and artificial phenomena generates massive amounts of data in many areas of research. This data is frequently left unused due to the lack of tools to effectively extract, analyze and understand it. Visual representation techniques can play a key role in helping to discover patterns and meaning within this data. Neuroscience is one of the scientific fields that generates the most extensive datasets. For this reason we built a 3D real-time visualization system to graphically represent the massive connectivity of neuronal network models in the eXperience Induction Machine (XIM). The XIM is an immersive space equipped with a number of sensors and effectors that we constructed to conduct experiments in mixed-reality. Using this infrastructure we developed an embodied interaction framework that allows the user to move freely in the space and navigate through the neuronal system. We conducted an empirical evaluation of the impact of different navigation mappings on the understanding of a neuronal dataset. Our results revealed that different navigation mappings affect the structural understanding of the system and the involvement with the data presented.