A Hybrid Brain-Computer Interface for Long-Term Use by Persons with Severe Motor Deficit: Towards Development of Personalized Algorithms

无法交流是严重运动残疾的主要后果，如肌萎缩侧索硬化症（ALS），也称为卢伽雷病。ALS是最常见的成人发病的运动神经元疾病。在过去的三十年里，脑机接口（BCI）技术已经成为神经肌肉疾病患者的替代沟通渠道。然而，这些通信系统通常需要足够的眼睛注视控制，这对于缺乏随意肌肉控制的人来说是困难的。此外，由于性能的显著日常变化，大多数当前的BCI不足以长期使用。为了解决这些缺点，该项目将联合收割机两种技术相结合，以监测神经元的电特性和神经元附近的血流。这种组合系统将随着疾病的进展而适应，并改善ALS患者的沟通。如果成功的话，该系统可以推广到其他类型的运动控制丧失，包括最低意识和植物人状态。开发的技术将被纳入大学课程，对本科生和研究生进行该领域的教育。此外，这项研究将通过k-12课程设计广泛传播，并将为妇女和代表性不足的少数民族提供培训机会。外联活动包括讲习班：“BCI系统中的个性化算法”和“床边和家庭用户的混合BCI”，海报演示，在学生和研究人员中传播本研究的新成果，并开发K-12课程，“大脑工程，“旨在促进中学生的脑机接口学习机会。该项目的重点是设计脑机接口（BCI）基于使用两种非侵入性技术组合的人脑功能成像：脑电图（EEG-测量电活动）和功能近红外光谱（fNIRS-测量血液动力学活动）。 将开发算法，使系统考虑神经心理状态和环境因素，并可以实现在家里，长期护理的人没有运动控制。 该系统将在ALS（肌萎缩侧索硬化症）患者身上进行测试，ALS是最常见的成人运动神经元疾病。 该项目的假设是，将血流动力学活动纳入传统的基于EEG的BCI允许从大脑状态进行上级学习，提供用户意图的独特功能，并允许潜在的改变游戏规则的解决方案，为最终用户提供新水平的通信自主性。 为了验证这一假设，研究计划分为两个目标。 第一个目标是探索BCI性能变化的关联，并设计一个多模式增强预测平台，以提高非语言患者的BCI系统的鲁棒性，这些患者具有剩余的运动能力来控制他们的眼睛注视。 该目标的两个目的是探索ALS-BCI性能变化与ALS中的功能性脑变化和环境噪声的关联，并建立一组最能代表用户BCI性能的预测性电血管特征，并相应地在未来的BCI实验中校正不相关的活动。 预期的客观结果包括（i）脑模式变化的纵向评估，（ii）建立一组预测性电血管特征，（iii）优化成功BCI性能所必需的受试者特定因素，（iv）采用适当的校正策略以最大限度地减少不必要的活动，以及（v）最大限度地适应用户的需求和环境因素，同时最大限度地减少受试者间的差异。 第二个目标是为没有任何剩余运动控制的非交流人员（最好是第一个目标中已经发展到锁定阶段的参与者）开发自主混合BCI。 开发的系统将有七个自由度（DOF），可以根据用户通过运动想象（5项任务）、心算（1项任务）和休息来调节大脑信号的能力，为外部设备产生七个不同的命令。 预期的客观结果是引入混合BCI，其（i）完全基于信号，（ii）采用个性化技术来增强系统性能，（iii）相对于单一模式的脑电图和fNIRS，提高了信息传输率;（iv）可以方便地安装在使用者的床边，以便长期使用。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。

The inability to communicate is a major consequence of severe motor disabilities such as amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease. ALS is the most common adult-onset motor neuron disease. Within the past three decades, Brain-Computer Interface (BCI) technologies have emerged as an alternative communication channel for persons with neuromuscular disorders. However, these communication systems typically require adequate eye-gaze control, which is difficult for those lacking voluntary muscle control. In addition, most current BCIs are inadequate for long-term use due to significant day-to-day variation in performance. To address these shortcomings, this project will combine two technologies to monitor the electrical properties of the neurons and the blood flow near the neurons. This combined system will adapt as the disease progresses and improve communication for persons with ALS. If successful, the system may be generalized to other types of motor control loss, including minimally conscious and vegetative states. The technologies developed will be integrated into a university curriculum that educates undergraduate and graduate students about the field. Furthermore, this research will be broadly disseminated through k-12 curriculum design and will provide training opportunities for women and under-represented minorities. Outreach activities include workshops: "Personalized Algorithms in BCI Systems" and "Hybrid BCI for Bedside and Home Users," poster presentations to disseminate novel outcomes from this study among students and researchers and development of a K-12 curriculum, "Engineering the Brain," designed to promote BCI learning opportunities among middle school students.The project focuses on designing Brain Computer Interfaces (BCIs) based on functional imaging of the human brain using a combination of two non-invasive techniques: electroencephalography (EEG-to measure electrical activity) and functional near-infrared spectroscopy (fNIRS-to measure hemodynamic activity). Algorithms will be developed such that the system considers both the neuropsychological status and environmental factors and can be implemented for in-home, long-term care of people with no motor control. The system will be tested on persons with ALS (amyotrophic lateral sclerosis), the most common adult-onset motor neuron disease. The project's hypothesis is that the incorporation of hemodynamic activities into conventional EEG-based BCI permits superior learning from brain states, provides unique features of the user's intent, and allows potentially game-changing solutions to provide end-users with a new level of communication autonomy. To test this hypothesis, the Research Plan is organized under two objectives. The FIRST Objective is to explore the associations of BCI performance variations and design a multimodal augmented predictive platform to improve the robustness of the BCI systems for nonverbal patients who have residual motor ability to control their eye-gaze. The two aims of the objective are to explore the associations of ALS-BCI performance variation with both functional brain changes in ALS and environmental noise and to establish a set of predictive electro-vascular features best representative of users' BCI performance, and accordingly correct for unrelated activities in future BCI experiments. Expected objective outcomes include (i) longitudinal assessment of brain pattern changes, (ii) establishing a set of predictive electro-vascular features, (iii) optimizing subject-specific factors essential for successful BCI performance, (iv) employing appropriate correction strategies to minimize unwanted activities and (v) maximizing adaptability with users' needs and environmental factors while minimizing inter-subject variabilities. The Second Objective is to develop an autonomous hybrid BCI for non-communicative persons without any residual motor control (preferably participants in the first objective who have progressed to the locked-in stage.) The system developed will have seven Degrees of Freedom (DOF) that can produce seven different commands for an external device based on the users' ability to modulate their brain signals through motor imagery (5 tasks), mental arithmetic (1 task) and rest. The expected objective outcome is the introduction of a hybrid BCI that (i) is fully autonomous-signal-based, (ii) employs personalized techniques to enhance system performance, (iii) has enhanced information transfer rates relative to single modal EEG and fNIRS and (iv) can be conveniently set up at users' bedsides for long-term use.This project is jointly funded by the Disabilities and Rehabilitation Engineering (DARE) program and the Established Program to Stimulate Competitive Research (EPSCoR).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.