EAGER - Exploration of Brain Computer Interface for Individuals with Cerebral Palsy

EAGER - 脑瘫患者脑机接口的探索

• 批准号: 1936908
• 负责人: Anca Ralescu
• 金额: $ 20.8万
• 依托单位: University of Cincinnati Main Campus
• 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: 2019-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936908&HistoricalAwards=false
• 关键词: EAGER; Exploration; Brain; Computer; Interface

Cerebral Palsy (CP), which occurs in about 2 out of 1000 births, is a group of disorders that affect movement and posture. CP is the result of a lesion/injury in the developing brain, usually before or during birth. CP manifests itself early in life, during infancy or preschool years, with delayed or abnormal motor progress. Currently, there is no treatment for CP beyond some forms of physical therapy, mostly focused on children. In recent years, Brain Computer Interfaces (BCI) have been used successfully to enable persons who are completely immobile to use their electrical brain signals (EEG) for communication and control of objects in their environment. However, this approach has had limited success in persons with CP because most CP individuals are subject to unpredictable body movements (spasms), and the EEG signals capture such movements, contaminating the signals for a particular application. Thus the goal of this project is to explore an alternative platform for EEG-based BCI for cerebral palsy by using a two-pronged approach. First, new techniques will be used to isolate and analyze signals of interest, i.e., to identify the useful signal out of its noisy background. Second, the approach will focus on an individual person with CP (as opposed to a group). A BCI model from a healthy subject will be transferred and adapted to the CP subject. The healthy subject, who except for the fact that he does not suffer from CP, will otherwise be similar in gender, age, cultural background, educational level, and intellectual abilities.The overall goal of this exploratory research project is to develop a Brain Computer Interface (BCI) for an individual suffering from Cerebral Palsy (CP), i.e., a project focused on one individual considering the particular characteristics of that individual's physical and intellectual abilities. The final objective is to enable this individual to perform some simple actions (e.g., hold a cup, hold an apple) that healthy individuals can perform on a routine basis. The BCI approach is based on capturing brain activity in the form of electroencephalogram (EEG) signals. BCI for CP subjects is particularly difficult due to the involuntary body movements (spasms) that afflict CP subjects, which produce unwanted/unneeded brain signals. To achieve the stated objective, the Research Plan is organized under 4 tasks: 1) compare EEG signals obtained from the CP subject with those from a healthy subject with similar physical and intellectual abilities; 2) compare, for each of the two subjects, the EEG signals obtained in a neutral state to those associated to a cognitive (visual and motor imagining) task; 3) develop a novel machine learning approach based on fuzzy sets/fuzzy logic and transfer learning to obtain a model for the CP subject adapted from the model for the healthy subject; and 4) leverage the previous three tasks to propose a novel, general procedure for BCI for disabled individuals.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.

脑性瘫痪（CP），发生在约2/1000出生，是一组影响运动和姿势的疾病。CP是发育中的大脑病变/损伤的结果，通常发生在出生前或出生期间。CP在生命早期、婴儿期或学龄前阶段就表现出来，运动进展延迟或异常。目前，除了某些形式的物理治疗外，没有治疗CP的方法，主要集中在儿童身上。近年来，脑机接口（BCI）已成功地用于使完全不动的人能够使用他们的脑电信号（EEG）进行通信和控制他们环境中的对象。 然而，这种方法在患有CP的人中取得的成功有限，因为大多数CP个体受到不可预测的身体运动（痉挛）的影响，并且EEG信号捕获这种运动，污染了用于特定应用的信号。 因此，本项目的目标是通过使用双管齐下的方法来探索基于EEG的脑机接口的替代平台。 首先，将使用新技术来分离和分析感兴趣的信号，即，从嘈杂的背景中识别出有用的信号。 其次，该方法将侧重于患有CP的个体（而不是群体）。来自健康受试者的BCI模型将被转移并适应CP受试者。 健康的受试者，除了他没有患有脑瘫的事实之外，在性别、年龄、文化背景、教育水平和智力方面都是相似的。本探索性研究项目的总体目标是为患有脑瘫（CP）的个体开发脑计算机接口（BCI），即，一个项目集中在一个人，考虑到个人的身体和智力的特殊特点。最终目标是使这个人能够执行一些简单的动作（例如，拿着一个杯子，拿着一个苹果），这是健康人可以在日常生活中完成的。BCI方法基于以脑电图（EEG）信号的形式捕获大脑活动。用于CP受试者的BCI特别困难，这是由于折磨CP受试者的无意识身体运动（痉挛），其产生不想要的/不需要的脑信号。为了实现所述目标，研究计划分为4项任务：1）将从CP受试者获得的EEG信号与从具有相似身体和智力能力的健康受试者获得的EEG信号进行比较; 2）对于两名受试者中的每一名，将在中性状态下获得的EEG信号与与认知相关的EEG信号进行比较。（视觉和运动想象）任务; 3）开发基于模糊集/模糊逻辑和迁移学习的新型机器学习方法，以获得从健康受试者的模型改编的CP受试者的模型;以及4）利用前三项任务，为残疾人的BCI提出一种新颖的、通用的程序。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。