TY - GEN
T1 - Virtual Reality Platform for Systematic Investigation of Multisensory Integration and Training of Closed-Loop Prosthetic Control
AU - Phataraphruk, Kris
AU - Vangilder, Paul
AU - Buneo, Christopher A.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Multisensory integration is the process by which information from different sensory modalities is integrated by the nervous system. Understanding this process is important not only from a basic science perspective but also for translational reasons, e.g. for the development of closed-loop neural prosthetic systems. Here we describe a versatile virtual reality platform which can be used to study the neural mechanisms of multisensory integration for the upper limb and could potentially be incorporated into systems for training of robust neural prosthetic control. The platform involves the interaction of multiple computers and programs and allows for selection of different avatar arms and for modification of a selected arm's visual properties. The system was tested with two non-human primates (NHP) that were trained to reach to multiple targets on a tabletop. Reliability of arm visual feedback was altered by applying different levels of blurring to the arm. In addition, tactile feedback was altered by adding or removing physical targets from the environment. We observed differences in movement endpoint distributions that varied between animals and visual feedback conditions, as well as across targets. The results indicate that the system can be used to study multisensory integration in a well-controlled manner.
AB - Multisensory integration is the process by which information from different sensory modalities is integrated by the nervous system. Understanding this process is important not only from a basic science perspective but also for translational reasons, e.g. for the development of closed-loop neural prosthetic systems. Here we describe a versatile virtual reality platform which can be used to study the neural mechanisms of multisensory integration for the upper limb and could potentially be incorporated into systems for training of robust neural prosthetic control. The platform involves the interaction of multiple computers and programs and allows for selection of different avatar arms and for modification of a selected arm's visual properties. The system was tested with two non-human primates (NHP) that were trained to reach to multiple targets on a tabletop. Reliability of arm visual feedback was altered by applying different levels of blurring to the arm. In addition, tactile feedback was altered by adding or removing physical targets from the environment. We observed differences in movement endpoint distributions that varied between animals and visual feedback conditions, as well as across targets. The results indicate that the system can be used to study multisensory integration in a well-controlled manner.
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U2 - 10.1109/EMBC44109.2020.9175439
DO - 10.1109/EMBC44109.2020.9175439
M3 - Conference contribution
AN - SCOPUS:85091023880
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2942
EP - 2945
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -