Multi-Modal, Multi-State, Real-Time Crew State Monitoring System

Fortier, (a) Kier and Garg, Nikhil and Pickering, Elizabeth

I helped develop a Real-time, Multi-Modal, Crew State Monitoring System that integrates EEG, GSR, and HRV. I was in charge of overall design, EEG processing, machine learning, and multi-modal integration. I developed robust artifact rejection to detect and remove blinks and other artifacts from the EEG data; EEG feature extraction to represent blocks of data with frequency characteristics, statistical measures, and blink rate; and a machine learning classification system (through Support Vector Machines) that uses the features and characterizes data from a block of time as originating from either a state of rest or a state of concentration. I then integrated EEG and GSR features for joint classification, and we demoed a end-to-end system that collected data from multiple sensors, extracted features, and trained and used the classifier to predict subject state. The system successfully classified 80 percent of subject states.