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

这个项目将把基于排队论的新的行为数据认知模型与新的机器学习技术相结合，用于分析神经生理数据，特别是脑电(EEG)，以提供对精神状态的更深入和更完整的理解，以及对人类表现的更准确的预测。在认知建模中，一种新的用于人类表现和脑力工作的大脑网络体系结构将得到进一步的改进，称为排队网络模型人类处理器(QN-MHP)。具有新的类似人类的小规模知识系统的QN-MHP将被用于模拟认知发展中大脑中髓鞘增加的情况，并从主观风险感知和信心方面预测人类的表现。在机器学习中，新的时空(基于模式)分类技术将被开发用于多维时间序列数据，并用于从EEG数据中识别人类的精神状态(例如，完全清醒、疲劳、分心、愤怒)。集成框架将产生一个健壮的智能系统，该系统使用机器学习来识别心理状态和该心理状态的排队模型，以预测人类的表现，并向人类操作员提供反馈。在该项目中，将开发一种思维驱动的智能交通系统作为案例研究，其中将设计某种类型的反馈，以帮助司机避免事故，提高系统安全性。该系统还可以应用于需要操作员完全或部分注意的其他人机系统(例如，在航空、军事或制造环境中)。

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).