HCC: Assessing Cognitive Function from Interactive Agent Behavior

HCC：从交互代理行为评估认知功能

• 批准号: 0934509
• 负责人: Deniz Erdogmus
• 金额: $ 38.32万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934509&HistoricalAwards=false
• 关键词: HCC; Assessing; Cognitive; Function; Interactive

This is a project to develop new methods for scientifically studying and assessing human cognitive function. It will employ sophisticated statistical multimodal data analysis techniques that will fuse contextual, behavioral, and neural information simultaneously obtained from human beings in the process of completing complex batteries of cognitive tasks. The tasks will be presented in the form of customized computer games that are designed to exhibit the crucial aspects of established cognitive assessment tests and at the same time provide a motivating and engaging environment for the subject's interactions with the game and computer agents. The tasks will involve exploiting our existing capabilities of monitoring and controlling certain enjoyable and challenging computer games that involve various combinations of cognitive tasks ranging from working memory and attention to executive functions. Multimodal information fusion will be accomplished by utilizing Bayesian inference techniques and information theoretic data analysis and dimensionality reduction methods. The work to be carried out under this grant aims to develop sophisticated pattern analysis techniques for the purpose of analyzing the fine-grain behaviors of elderly when they are engaged in complex cognitive tasks in the form of computer games. Expected significant scientific findings from the proposed research are two-fold: (1) improved statistical signal processing and pattern recognition algorithms for EEG processing, (2) an enhanced understanding of the interplay of multiple cognitive processes and their neural signatures in EEG during the execution of complex tasks. The approach is innovative in terms of three aspects: (1) an advanced adaptive interaction protocol that modifies the task parameters to maintain maximal sensitivity to cognitive state changes will be employed, (2) novel information theoretic techniques will be developed and utilized for the extraction of maximally discriminative features from EEG measurements for cognitive state estimation and neural activity visualization, (3) the developed closed-loop system will be utilized to study the human-agent interaction in complex cognitive tasks resulting in mathematical models of micro-behavior in realistic evolving environments as opposed to traditional stationary repetitive experimental paradigms. The successful completion of the work will open the way to further collaborative activities in brain interface design, closed-loop collaborative augmented cognition human-agent interfaces for improved performance, and early diagnosis of cognitive decline in elderly. An interdisciplinary research environment will engage the participating graduate students in a multidisciplinary educational setting and will help them develop skills to perform collaborative interdisciplinary research.

这是一个开发科学研究和评估人类认知功能的新方法的项目。它将采用复杂的统计多模式数据分析技术，这些技术将在完成认知任务的复杂电池的过程中同时从人类获得的上下文，行为和神经信息融合。这些任务将以定制的计算机游戏的形式呈现，旨在展示已建立的认知评估测试的关键方面，同时为受试者与游戏和计算机代理的互动提供了一个激励而引人入胜的环境。这些任务将涉及利用我们现有的监视和控制某些愉快而挑战的计算机游戏的能力，这些功能涉及各种认知任务的组合，从工作记忆和关注到执行功能。多模式信息融合将通过利用贝叶斯推理技术和信息理论数据分析和降低降低方法来完成。 这项赠款将进行的工作旨在开发复杂的模式分析技术，目的是分析老年人以计算机游戏形式从事复杂的认知任务时的细粒度行为。拟议研究的预期科学发现有两个方面的重大发现：（1）改进了脑电图处理的统计信号处理和模式识别算法，（2）在复杂任务执行过程中，对多种认知过程及其神经信号的相互作用的了解增强了。 The approach is innovative in terms of three aspects: (1) an advanced adaptive interaction protocol that modifies the task parameters to maintain maximal sensitivity to cognitive state changes will be employed, (2) novel information theoretic techniques will be developed and utilized for the extraction of maximally discriminative features from EEG measurements for cognitive state estimation and neural activity visualization, (3) the developed closed-loop system will be utilized在复杂的认知任务中研究人类代理人的相互作用，从而导致在现实发展的环境中与传统固定重复的实验范式相反，导致微行为的数学模型。 这项工作的成功完成将为进一步的脑接口设计，闭环协作增强认知界面以改善绩效以及老年人认知能力下降的早期诊断。跨学科的研究环境将使参与的研究生参与多学科的教育环境，并将帮助他们发展技能，以进行协作跨学科研究。