The goals of our joint US/UK interdisciplinary effort are to investigate and model the neural mechanisms underlying multisensory processing and decision making and to design closed-loop adaptive algorithms for optimized exploitation of multisensory data for brain-computer communication. We are motivated by the observation that in performing many tasks, such as flying an airplane, an operator has to make decisions in time-pressured and stressful conditions based on a multiplicity of multisensory information presented in cluttered and distracting environments. We envision a closed-loop brain-computer interface (BCI) architecture for enhancing decision accuracy. Our effort consists of both computational and experimental components. The computational component of our effort spans Bayesian inference, stochastic control, adaptive signal processing, and machine learning. The experimental component of our effort validates our computational models and algorithms using state-of-the-art neurophysiology.
Principal Investigator: Maryam Shanechi (USC) shanechi at usc dot edu
Funding Agencies: US Army Research Office (ARO) and UK Ministry of Defense (MOD)
Program Managers: Cliff Wang (ARO), Frederick Gregory (ARO), Bhopinder K Madahar (MoD), Annalise H Whittaker (MoD)