PFI:BIC - Unobtrusive Neurotechnology and Immersive Human-Computer Interface for Enhanced Learning

PFI:BIC - 用于增强学习的低调神经技术和沉浸式人机界面

• 批准号: 1719130
• 负责人: Gert Cauwenberghs
• 金额: $ 100万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719130&HistoricalAwards=false
• 关键词: PFI; BIC; Unobtrusive; Neurotechnology; Immersive

The increasing prevalence of learning disorders, attention deficits, and lackluster appetite for reading across all walks of life, and particularly among school-age children, poses severe problems to humanity and, in the long run, burdens social and economic development. This Partnership for Innovation Building Innovation Capacity (PFI:BIC) collaborative project tackles the impending threats to humanity of illiteracy and faltering education heads-on by creating a new smart-service human-computer interface (HCI) neurotechnology platform as a highly effective, user-friendly, and fun-to-use tool aiding learning and stimulating cognitive development at home and in the classroom. The immersive HCI neurotechnology will allow directly measuring progress at the cognitive level and providing real-time feedback to guide the user in learning to read more effectively. The project is highly Science, Technology, Engineering and Mathematics (STEM) intensive both in its activities and in the targeted benefits of the developed technology, which extends directly to learning science and mathematics by probing cognitive performance of children while they solve puzzles. The development of unobtrusive neurotechnology further addresses a critical need for practical integrated and modular brain-computer interface (BCI) solutions in HCI promoting widespread consumer and clinical use in the marketplace. The partnership provides opportunities for students to gain practical experience in innovation in the marketplace through internships with the industrial partners.The central aim is to develop and leverage new HCI technology as a learning coach and personal cognitive development assistant that facilitates learning to read and acquiring other critical skills in cognitive development. The immersive yet unobtrusive HCI technology testbed will comprise a dry-electrode electroencephalography (EEG) BCI, a tablet with touchscreen and integrated camera, and a suite of signal processing algorithms running in the cloud, for monitoring brain and gaze activity in children learning to read, and providing real-time neurofeedback on progress in cognitive performance to promote enhanced learning. The partnership will transition scientific advances of a previous NSF-sponsored UCSD project (NSF EFRI-M3C, ENG-1137279) in studying the distributed dynamics of human motor control, to development of neurofeedback training paradigms for learning enhancement, and to practical deployment on the unobtrusive immersive testbed implemented using Cognionics dry-electrode EEG wireless BCI neurotechnology and Syntrogi real-time cloud-based signal processing software pipelines. The potential for human empowerment by the technology will be demonstrated by evaluating effectiveness in enhancing learning capabilities and cognitive performance in simulated classroom settings and other targeted learning environments.The lead institution for the project is University of California San Diego (UCSD), with investigators from the Institute for Neural Computation and Department of Bioengineering. The industrial partners in the effort are Syntrogi Inc. (dba Qusp, small business, San Diego CA) and Cognionics, Inc. (small business, San Diego, CA). The project also engages broader context partners Drs. Andrea Chiba and Leanne Chukoskie from the UCSD Temporal Dynamics of Learning Center, Dr. Barbara Moss from San Diego State University Department of Psychology, and Dr. Zewelanji N. Serpell from Virginia Commonwealth University Department of Psychology, in the human case studies and the assessment of the developed HCI technology in diverse learning environments.

各行各业，特别是学龄儿童，学习障碍、注意力缺陷和阅读兴趣低迷的现象日益普遍，给人类带来了严重问题，从长远来看，给社会和经济发展带来了负担。 这一创新伙伴关系建设创新能力 (PFI:BIC) 合作项目通过创建一个新的智能服务人机界面 (HCI) 神经技术平台作为高效的用户，正面应对文盲和摇摇欲坠的教育对人类迫在眉睫的威胁。 - 友好且有趣的工具，可帮助在家和课堂上学习并刺激认知发展。 沉浸式人机交互神经技术将允许直接测量认知水平的进展并提供实时反馈以指导用户学习更有效地阅读。 该项目的活动和所开发技术的目标效益都是高度科学、技术、工程和数学 (STEM) 密集型的，通过探索儿童解决谜题时的认知表现，直接延伸到学习科学和数学。 不引人注目的神经技术的发展进一步满足了人机交互中实用集成和模块化脑机接口（BCI）解决方案的迫切需求，促进了市场上广泛的消费者和临床应用。 该合作伙伴关系为学生提供了通过与行业合作伙伴实习获得市场创新实践经验的机会。中心目标是开发和利用新的人机交互技术作为学习教练和个人认知发展助手，促进学习阅读和获取其他知识。认知发展的关键技能。 沉浸式但不显眼的 HCI 技术测试平台将包括干电极脑电图 (EEG) BCI、带有触摸屏和集成摄像头的平板电脑以及一套在云端运行的信号处理算法，用于监测学习阅读的儿童的大脑和凝视活动，并提供有关认知表现进展的实时神经反馈，以促进增强学习。 该合作伙伴关系将把之前由 NSF 资助的 UCSD 项目（NSF EFRI-M3C，ENG-1137279）在研究人类运动控制的分布式动力学方面的科学进展，转变为开发用于增强学习的神经反馈训练范例，以及在使用 Cognionics 干电极 EEG 无线 BCI 神经技术和 Syntrogi 实时基于云的信号处理软件管道实现的不显眼的沉浸式测试台。 该技术赋予人类权力的潜力将通过评估在模拟课堂环境和其他目标学习环境中增强学习能力和认知表现的有效性来证明。该项目的牵头机构是加州大学圣地亚哥分校（UCSD），研究人员来自神经计算研究所和生物工程系。 这项工作的工业合作伙伴是 Syntrogi Inc.（dba Qusp，小型企业，加利福尼亚州圣地亚哥）和 Cognionics, Inc.（小型企业，加利福尼亚州圣地亚哥）。 该项目还吸引了更广泛的合作伙伴。加州大学圣地亚哥分校学习时间动力学中心的 Andrea Chiba 和 Leanne Chukoskie、圣地亚哥州立大学心理学系的 Barbara Moss 博士以及弗吉尼亚联邦大学心理学系的 Zewelanji N. Serpell 博士进行了人类案例研究和评估在不同的学习环境中开发的人机交互技术。

项目成果

A Wearable Multi-modal Bio-sensing System Towards Real-world Applications

面向实际应用的可穿戴多模态生物传感系统

• 发表时间: 2019-04
• 期刊: IEEE transactions on biomedical engineering
• 影响因子: 4.6
• 作者: Siddharth, S.;Patel, A.;Jung, T;Sejnowski, T.
• 通讯作者: Sejnowski, T.

The Open EEGLAB Portal Interface:High-Performance Computing with EEGLAB

开放 EEGLAB 门户界面：使用 EEGLAB 进行高性能计算

• DOI: 10.1016/j.neuroimage.2020.116778
• 发表时间: 2021-01-01
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Martínez-Cancino R;Delorme A;Truong D;Artoni F;Kreutz-Delgado K;Sivagnanam S;Yoshimoto K;Majumdar A;Makeig S
• 通讯作者: Makeig S

Human Brain Dynamics Reflect the Correctness and Presentation Modality of Physics Concept Memory Retrieval

人脑动力学反映物理概念记忆检索的正确性和呈现方式

• DOI: 10.3389/fnhum.2020.00331
• 发表时间: 2020-08
• 期刊: Frontiers in Human Neuroscience
• 影响因子: 2.9
• 作者: Liang, Chih;She, Hsiao;Huang, Li;Chou, Wen;Chen, Sheng;Jung, Tzyy
• 通讯作者: Jung, Tzyy

Characterization of Ag/AgCl Dry Electrodes for Wearable Electrophysiological Sensing

用于可穿戴电生理传感的 Ag/AgCl 干电极的表征

• DOI: 10.3389/felec.2021.700363
• 发表时间: 2022-01-06
• 作者: M. Lee;Akshay Paul;Yuchen Xu;W. Hairston;G. Cauwenberghs
• 通讯作者: G. Cauwenberghs

Modeling brain dynamic state changes with adaptive mixture independent component analysis

利用自适应混合独立成分分析对大脑动态变化进行建模

• DOI: 10.1016/j.neuroimage.2018.08.001
• 发表时间: 2018-12
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Hsu SH;Pion-Tonachini L;Palmer J;Miyakoshi M;Makeig S;Jung TP
• 通讯作者: Jung TP