RI:Small:Collaborative Proposal: Computational Framework of Robust Intelligent System for Mental State Identification and Human Performance Prediction with Biofeedback

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This project will integrate new cognitive models of behavioral data based on queueing theory with new machine learning techniques for analyzing neurophysiological data, specifically electroencephalogram (EEG), in order to provide a deeper and more complete understanding of mental states as well as more accurate prediction of human performance. In cognitive modeling, a new brain network architecture for human performance and mental workload, called Queuing Network-Model Human Processor (QN-MHP), will be further improved. QN-MHP with a new human-like small-scale knowledge system will be used to model the increase of myelination in the brain in cognitive development and predict human performance, in terms of subjective risk perception and confidence. In machine learning, new spatio-temporal (pattern-based) classification techniques will be developed for multidimensional time series data and used to identify human mental states (e.g., fully awake, fatigue, distracted, anger) from EEG data. The integrated framework will result in a robust intelligent system that uses machine learning to identify mental states and the queueing model of that mental state to predict the human performance as well as provide a human operator with feedback. A mind-driven intelligent transportation system will be developed as a case study in this project, where a certain type of feedback will be designed to help drivers avoid accidents and to improve system safety. This system can also be applied to other human-machine systems that require full or partial attention of human operators (e.g., in aviation, military, or manufacturing settings).