SCH: INT: Collaborative Research: Detection, Assessment and Rehabilitation of Stroke-Induced Visual Neglect Using Augmented Reality (AR) and Electroencephalography (EEG)

SCH：INT：合作研究：使用增强现实 (AR) 和脑电图 (EEG) 检测、评估和康复中风引起的视觉忽视

• 批准号: 1915065
• 负责人: Sarah Ostadabbas
• 金额: $ 39.42万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915065&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Detection

Unilateral spatial neglect is a perceptual disorder that is one of the most common consequences of right-side brain damage after stroke, occurring in 29% of the 15 million people who sustain stroke worldwide. Patients with neglect demonstrate inattention to objects or events on the side that is opposite to the damaged part of the brain. They often miss food on one side of the plate, missing words on one side of the page, bumping into the left door jamb, getting confused by moving objects, and being fearful of walking in crowded places. The current gold standard for detecting and rehabilitating neglect lacks generalizability to dynamic tasks and contexts encountered during activities of daily living (ADL). The investigators in this project will develop a brain-computer interface (BCI) system that will be implemented in augmented reality (AR) environment for detection, assessment and rehabilitation of unilateral neglect during ADL. More specifically, the system will in real-time monitor the brain activity recorded through electroencephalography (EEG) for the detection and assessment of visually neglected extra-personal space. Moreover, the system will also include haptic, auditory and visual stimulation while the users are engaged in real-world tasks conducted during rehabilitation for reducing neglect-related disabilities. It is also anticipated that the novel scientific discoveries and engineering enhancements of this project will have effects on the current practice on BCIs: (i) enabling design and implementation of such systems in more naturalistic environments providing more immersive experiences; and (ii) expansion of the use of BCIs in the design of intervention and rehabilitation techniques for other neurological disorders. This project will promote STEM education and provide rigorous training and variety of hands-on experiences to researchers from K-12 to graduate level.The research objective of this project is to introduce a prototype for stroke-induced neglect detection, assessment, and rehabilitation system, featuring: (i) seamless integration of EEG and AR in the design of visually evoked EEG-based BCIs to operate during activities of daily living; (ii) accurate and continuous EEG event related potential detection for neglect assessment and mapping through Bayesian inference models; (iii) information theoretic optimum design of neglect intervention focusing on activities of daily living; and (iv) multimodal real-time feedback for rehabilitation of neglect related disabilities during intervention. Unlike the common computerized neglect assessment methods, EEG will not require any physical responses from the patient. Also, the use of EEG permits automation, making it an ideal method to guide a personalized and automated neglect intervention. It is known that one common element among the existing interventions that have shown promise for reducing neglect is multimodal stimulation to the neglected side of the body or environment. Timely feedback to the user when neglect is detected during the continuous EEG monitoring will enable this stimulation. Finally, used in conjunction with AR headset and skill-based training during acute inpatient rehabilitation, the planned system will provide the opportunity to deliver high-intensity repetitive stimulation with progression during meaningful everyday activities. The outcomes of this project will be disseminated to the scientific community through technical reports, journal publications and conference presentations. All software developed through this project will be publicly available through archival repositories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

单侧空间忽略是一种知觉障碍，是中风后右侧脑损伤最常见的后果之一，在全球1500万中风患者中有29%发生。忽视症患者表现出对大脑受损部分对面的物体或事件的注意力不集中。他们经常在盘子的一边错过食物，在页面的一边错过单词，撞到左边的门框，被移动的物体弄糊涂，害怕在拥挤的地方行走。目前的黄金标准检测和康复忽视缺乏概括性的动态任务和日常生活活动（ADL）中遇到的情况。该项目的研究人员将开发一个脑机接口（BCI）系统，该系统将在增强现实（AR）环境中实现，用于ADL期间单侧忽视的检测，评估和康复。 更具体地说，该系统将实时监测通过脑电图（EEG）记录的大脑活动，以检测和评估视觉上被忽视的个人以外的空间。此外，该系统还将包括触觉，听觉和视觉刺激，而用户在康复期间从事现实世界的任务，以减少与忽视有关的残疾。预计该项目的新科学发现和工程改进将对目前的脑机接口实践产生影响：（i）使此类系统能够在更自然的环境中设计和实施，提供更身临其境的体验;（ii）扩大脑机接口在其他神经系统疾病的干预和康复技术设计中的使用。本项目的研究目的是推出一个中风所致忽视的检测、评估和康复系统原型，其特点是：（i）在设计基于视觉诱发脑电的脑机接口时，无缝整合脑电和增强现实技术，以在日常生活活动中操作;（ii）准确和连续的EEG事件相关电位检测，用于通过贝叶斯推理模型进行忽视评估和映射;（iii）忽视干预的信息论优化设计，重点是日常生活活动;以及（iv）干预期间忽视相关残疾康复的多模式实时反馈。与常见的计算机化疏忽评估方法不同，EEG不需要患者的任何身体反应。此外，EEG的使用允许自动化，使其成为指导个性化和自动化忽视干预的理想方法。众所周知，在现有的干预措施中，一个共同的元素，已显示出减少忽视的承诺是多模式刺激被忽视的身体或环境的一面。当在连续EEG监测期间检测到疏忽时，及时向用户反馈将启用该刺激。最后，在急性住院康复期间与AR耳机和基于技能的培训结合使用，计划中的系统将提供在有意义的日常活动中提供高强度重复刺激的机会。该项目的成果将通过技术报告、期刊出版物和会议介绍向科学界传播。通过该项目开发的所有软件将通过档案库公开提供。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

EEG-based Neglect Detection for Stroke Patients

• DOI: 10.1109/embc44109.2020.9176378
• 发表时间: 2020-07
• 期刊: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
• 作者: Deniz Kocanaogullari;J. Mak;Jessica Kersey;A. Khalaf;S. Ostadabbas;G. Wittenberg;E. Skidmore;M. Akçakaya

Fine-tuning and Personalization of EEG-based Neglect Detection in Stroke Patients

基于脑电图的中风患者忽视检测的微调和个性化

• DOI: 10.1109/embc46164.2021.9630794
• 发表时间: 2021
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Kocanaogullari, Deniz;Huang, Xiaofei;Mak, Jennifer;Shih, Minmei;Skidmore, Elizabeth;Wittenberg, George F.;Ostadabbas, Sarah;Akcakaya, Murat
• 通讯作者: Akcakaya, Murat