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

RI:Small:协作提案：利用生物反馈进行精神状态识别和人类表现预测的鲁棒智能系统计算框架

• 批准号: 1219638
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 17.42万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-30 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219638&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Proposal; Computational

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).

该项目将基于认知理论的行为数据的新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，特别是脑电图（EEG），以提供对精神状态的更深入，更完整的理解以及对人类表现的更准确预测。在认知建模方面，将进一步改进一种新的用于人类表现和心理负荷的脑网络架构，称为可重构网络模型人类处理器（QN-MHP）。QN-MHP具有新的类人小规模知识系统，将用于模拟认知发展中大脑髓鞘形成的增加，并在主观风险感知和信心方面预测人类表现。在机器学习中，将为多维时间序列数据开发新的时空（基于模式）分类技术，并用于识别人类的精神状态（例如，完全清醒、疲劳、分心、愤怒）。该集成框架将产生一个强大的智能系统，该系统使用机器学习来识别精神状态，并使用该精神状态的预测模型来预测人类的表现，并为人类操作员提供反馈。本项目将开发一个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，以帮助驾驶员避免事故，并提高系统的安全性。该系统还可以应用于需要人类操作员的全部或部分注意力的其他人机系统（例如，在航空、军事或制造环境中）。