Closed-Loop Stimulus Optimization to Increase Communication Efficiency in Brain-Computer Interfaces

闭环刺激优化可提高脑机接口的通信效率

• 批准号: 10412578
• 负责人: Boyla Mainsah
• 金额: $ 26.66万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412578
• 关键词: Address; Administrative Supplement; Adoption; Algorithms; Amyotrophic Lateral Sclerosis; Artificial Intelligence; Augmentative and Alternative Communication; Award; Awareness; Belief System; Big Data; Brain; Chronic; Code; Communication; Communities; Computer software; Consumption; Data; Data Engineering; Data Files; Data Reporting; Data Science; Data Set; Development; Devices; Disease; Documentation; Electroencephalography; Elements; Environment; Eye; FAIR principles; Funding; Goals; Head; Individual; Information Technology; Injury; Learning; Learning Skill; Letters; Longitudinal Studies; Machine Learning; Measurement; Measures; Metadata; Methods; Modality; Modeling; Motor; Movement; Muscle; Noise; Output; Parents; Participant; Performance; Population; Process; Psychological Transfer; Psychophysics; Pythons; Readability; Readiness; Refractory; Research; Research Support; Schedule; Self-Help Devices; Sensory; Signal Transduction; Skeletal Muscle; Source; Speed; Stimulus; Students; System; Techniques; Technology; Testing; Time; Touch sensation; Training; Translating; Translations; Uncertainty; United States National Institutes of Health; User-Computer Interface; Validation; Work; algorithm development; base; brain computer interface; brain research; cohort; communication device; data cleaning; data curation; data management; data repository; data standards; deep learning; design; directed attention; disability; experience; file format; gaze; graduate student; improved; innovation; interest; large datasets; learning community; machine learning algorithm; motor control; multidisciplinary; neuroregulation; neurotransmission; novel; open source; programs; relating to nervous system; repository; response; simulation; skill acquisition; skills; spelling; student participation; success; time use; undergraduate student; usability

ABSTRACT This administrative supplement is in response to the Notice of Special Interest to improve the artificial intelligence and machine learning (AI/ML)-Readiness of NIH-supported sata (NOT-OD-21-094). Summary of Parent Award. Augmentative and alternative communication (AAC) systems are used by people with communication and motor disabilities, such as amyotrophic lateral sclerosis (ALS), to communicate and interact with their environment. There are conventional AAC devices that are controlled by access methods such as touch, switch, head tracking and eye gaze; however, these access methods become difficult or impossible to use when sustained muscle control or voluntary motor control is lost. There are brain-computer interface (BCI) communication systems, such as the P300 speller, that use sensory stimulation to elicit and then detect sensory neural responses in electroencephalography (EEG) data. However, communication with stimulus driven BCIs is suboptimal due to relying on inherently noisy EEG data and highly variable neural responses for BCI control. BCI communication rates can potentially be improved by leveraging information in EEG data in real-time to optimally tune the BCI system’s parameters to maximise BCI performance under conditions of uncertainty. This work investigates a novel closed-loop stimulus selection algorithm that optimises the stimulus presentation schedule of the P300 speller in real-time based on the measured EEG data and the BCI system’s belief about the user’s intent. Aim 1 develops and tests the novel algorithm in a cohort of abled- bodied individuals to evaluate the real-time feasibility and utility of closed-loop stimulus selection. Aim 2 will test the closed-loop stimulus selection algorithm in a cohort of individuals with ALS to assess the performance of the algorithm in target BCI end users. Goals of this Supplement. There is a current unmet need for large, diverse BCI datasets that include target BCI end users for BCI algorithm development, particularly with the popularity of data hungry deep learning models. Based on NIH-supported research for 10+ years, we have acquired a large amount of single- and multi-session data from P300 speller studies with abled-bodied participants and participants with ALS using different stimulus presentation paradigms. Guided by FAIR principles, in this supplement: (1) we will perform data curation, data cleaning and data engineering to develop a cross-platform readable P300 speller dataset with common data and metadata elements and make this dataset publicly available; and we will demonstrate the usability of this dataset in (2) an AI/ML application focused on developing robust data representations to mitigate the negative effect of variabilities in EEG data on AI/ML algorithms; and (3) in student research programs focused on skill development in data science and AI/ML. A large, inclusive and accessible BCI dataset will have significant impact in the BCI community and the broader AI/ML community, as it will support research to develop and compare novel data representations, stimulus paradigms and neural signal decoding algorithms towards establishing BCIs as viable AAC devices.

抽象的 本行政补充是为了响应特别关注的通知，以改善人工 智能和机器学习 (AI/ML) - NIH 支持的 sata 准备情况 (NOT-OD-21-094)。总结 家长奖。增强和替代通信 (AAC) 系统由患有以下疾病的人使用 沟通和运动障碍，例如肌萎缩侧索硬化症（ALS） 与他们的环境互动。存在通过访问方法控制的常规AAC设备 例如触摸、开关、头部追踪和眼睛注视；然而，这些访问方法变得困难或 当持续的肌肉控制或自愿运动控制丧失时，无法使用。有脑机 接口 (BCI) 通信系统，例如 P300 拼写器，使用感官刺激来引发和 然后检测脑电图（EEG）数据中的感觉神经反应。然而，与 由于依赖固有的嘈杂脑电图数据和高度可变的神经元，刺激驱动的脑机接口并不是最理想的 BCI 控制的响应。 BCI 通信速率可以通过利用以下信息来提高： 实时脑电图数据可优化调整 BCI 系统的参数，从而最大限度地提高 BCI 性能 不确定性条件。这项工作研究了一种新颖的闭环刺激选择算法，该算法可以优化 根据测量的脑电图数据和 P300 拼写器的实时刺激呈现时间表 BCI 系统对用户意图的信念。目标 1 在一组有能力的人中开发并测试新颖的算法 身体个体评估闭环刺激选择的实时可行性和效用。目标2将 在一组 ALS 患者中测试闭环刺激选择算法以评估其表现 目标 BCI 最终用户的算法。本补充文件的目标。当前对大型、 多样化的 BCI 数据集，其中包括用于 BCI 算法开发的目标 BCI 最终用户，特别是 数据匮乏的深度学习模型的流行。基于 NIH 支持的 10 多年的研究，我们已经 从与健全人的 P300 拼写研究中获得了大量的单次和多次数据 参与者和 ALS 参与者使用不同的刺激呈现范式。由公平指导 原则，在本补充中：（1）我们将进行数据管理、数据清理和数据工程来开发 具有通用数据和元数据元素的跨平台可读 P300 拼写器数据集，并使其成为 数据集公开可用；我们将在 (2) AI/ML 应用程序中演示该数据集的可用性 专注于开发稳健的数据表示，以减轻脑电图数据变异的负面影响 关于人工智能/机器学习算法； (3) 专注于数据科学技能发展的学生研究项目 人工智能/机器学习。大型、包容性和可访问的 BCI 数据集将对 BCI 社区和社会产生重大影响。 更广泛的人工智能/机器学习社区，因为它将支持开发和比较新数据表示的研究， 刺激范例和神经信号解码算法，旨在将 BCI 建立为可行的 AAC 设备。

项目成果

Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.

用于脑机通信接口的语言模型引导分类器适应。

• DOI: 10.1109/smc53654.2022.9945561
• 发表时间: 2022
• 期刊: Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics
• 作者: Chen,XinlinJ;Collins,LeslieM;Mainsah,BoylaO
• 通讯作者: Mainsah,BoylaO

Mitigating the Impact of Psychophysical Effects During Adaptive Stimulus Selection in the P300 Speller Brain-Computer Interface.

• DOI: 10.1109/embc46164.2021.9630048
• 发表时间: 2021-11
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Chen XJ;Collins LM;Mainsah BO
• 通讯作者: Mainsah BO