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

脑性瘫痪(CP)是一组影响运动和姿势的疾病，发生在1000名新生儿中约有2名。脑性瘫痪是发育中的大脑损伤的结果，通常是在出生前或出生期间。脑性瘫痪表现在生命的早期，在婴儿期或学龄前阶段，运动发育迟缓或异常。目前，除了某些形式的物理治疗外，还没有针对CP的治疗方法，主要针对儿童。近年来，脑机接口(BCI)被成功地用于使完全不能移动的人能够使用他们的大脑电信号(EEG)来通信和控制他们所在环境中的物体。然而，这种方法在脑瘫患者中的成功有限，因为大多数脑瘫患者受到不可预测的身体运动(痉挛)的影响，而脑电信号捕捉到这种运动，污染了特定应用的信号。因此，本项目的目标是通过双管齐下的方法探索一种基于脑电的脑机接口治疗脑瘫的替代平台。首先，将使用新的技术来分离和分析感兴趣的信号，即从噪声背景中识别出有用的信号。其次，该方法将重点放在患有CP的个人(而不是群体)上。来自健康受试者的脑机接口模型将被转移并改编到CP受试者身上。这项探索性研究项目的总体目标是为脑瘫(CP)患者开发一个脑机接口(BCI)，即考虑到一个人的身体和智力的特定特征，以一个人为中心。最终的目标是使这个人能够执行一些健康的人可以常规执行的简单动作(例如，拿着杯子，拿着苹果)。BCI方法基于以脑电(EEG)信号的形式捕获大脑活动。CP受试者的脑机接口尤其困难，因为CP受试者的不自主身体运动(痉挛)会产生不想要的/不需要的大脑信号。为了实现上述目标，研究计划分为四个任务：1)比较脑瘫患者和具有相似身体和智力能力的健康患者的脑电信号；2)比较两个受试者在中性状态下获得的脑电信号和与认知(视觉和运动想象)任务相关的脑电信号；3)开发一种基于模糊集/模糊逻辑和转移学习的新的机器学习方法，以获得适合健康患者模型的脑电患者模型；以及4)利用前三项任务，为残疾人提出一种新颖的一般程序。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.