Objective: Quantify, predict, and enhance squad-level shared situational awareness (SA) and understanding (SU) across volatile, uncertain, complex and ambiguous operating environments leading to demonstrated increases in mission effectiveness.
Description: Tactical Awareness via Collective Knowledge (TACK) consists of human state identification models that exploit gaze and physiological data to provide real-time estimates of human SA across heterogeneous teams and improve target search in complex, unstructured environments. Specifically, TACK uses gaze position data and passive neural (EEG) from multiple individuals to extract Regions of Interest (ROIs) from the environment to share across heterogeneous teams. Computer vision (CV) is used to integrate ROIs with physiology to provide real-time estimate of Soldier state and Squad SA.
Advanced Computational Approaches
Under ACA we developed a deep-learning algorithm to detect the discrete neural responses that occur when humans visually perceive an object of interest, or stimulus relevant to the current objective. Importantly, we linked neural and eye-movement activity to enable application in naturalistic viewing environments. By simultaneously combining the outputs from multiple individuals, we built a common representation of the environment that can be shared across teams or with autonomous agents
Brain Computer Interaction
Under BCI we developed the expertise and software tools for real-time exploitation of physiological signals within virtual environments.