Extensions and analysis of a virtual balancing task for studying sensory-motor control Michael Clancya, Sudarshan Sekhar, PhDa, Aaron Batista, PhDa, Patrick Loughlin, PhDa Department of Bioengineering, University of Pittsburgh
a
Michael Clancy is an undergraduate student who has been studying Bioengineering at the University of Pittsburgh since 2016. His research interests include motor control and sensory feedback; and machine learning and neural networks.
Michael Clancy
Sudarshan Sekhar did his bachelor’s in electrical engineering from Anna University in Madras, India. He then did his masters & PhD at the University of Tuebingen, Germany, and is currently a postdoc at the University of Pittsburgh.
Sudarshan Sekhar, PhD
Aaron Batista is an Associate Professor in Bioengineering at the University of Pittsburgh. Prior to joining Pitt’s faculty he earned a PhD in Computation and Neural Systems at the California Institute of Technology, and pursued postdoctoral research at Stanford University. Dr. Batista’s research include the neurophysiology of skilled behavior.
Aaron Batista, PhD
Patrick Loughlin is a Professor and Associate Chair of Bioengineering at the University of Pittsburgh, which he joined in 1993 after earning a PhD in Electrical Engineering from the University of Washington. His research interests include sensory integration in motor control; control of human movement; sensory substitution, haptics, vibrotactile feedback; brain-computer interfaces; computational models; and signal processing. Dr. Loughlin is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), the Acoustical Society of America (ASA), and the Institute of Electrical and Electronics Engineers (IEEE). Patrick Loughlin, PhD
Significance Statement
Understanding how the brain encodes sensory information about its surroundings and in turn generates neural signals that drive movement could enable us to restore sensory-motor function to paralyzed individuals, through the development of brain-computer interfaces. Virtual object manipulation provides a means by which to experimentally investigate native sensory-motor function in controlled conditions that are a balance between natural yet highly variable movements versus highly repeatable yet simple movements such as point-to-point reaching. Here we analyze and extend one such task, the Critical Stability Task (CST).
Category: Methods
Keywords: motor control, sensory feedback, virtual object manipulation, neural networks, modeling
24 Undergraduate Research at the Swanson School of Engineering
