Presentation
Real-Time Quantification of Attention and Cognitive Load via Multimodal Physiological Data
DescriptionAdvanced wearable sensors are revolutionizing our ability to quantify human attention and mental workload during interaction with complex digital systems. This live demonstration presents a real-time, closed-loop system for estimating attention and cognitive load using synchronized multimodal physiological data from the OpenBCI Galea headset. The system integrates brain, eye, muscle, heart, and skin-related sensors, and was developed using data from 45 participants who completed attention and workload tasks in virtual reality. To predict cognitive states in real-time, we trained machine-learning models to estimate attention from task performance, and estimate cognitive load using heart-rate and heart-rate-variability measures. In the live demonstration, participants will fly a virtual-reality mission while the system displays their physiological signals, flight telemetry, and changing cognitive-state estimates. This work shows how real-time multimodal physiological sensing can support future adaptive technologies. Potential applications include training systems that adjust difficulty based on the user’s state, virtual environments that respond to attention or workload, and closed-loop tools that provide personalized feedback during demanding tasks.
Event Type
Demonstration
TimeTuesday, October 20th3:30pm - 4pm PDT
Location