SCH: New Statistical Learning Methods for Brain-Computer Interfaces

SCH：脑机接口的新统计学习方法

• 批准号: 2123777
• 负责人: Jian Kang
• 金额: $ 110万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123777&HistoricalAwards=false
• 关键词: SCH; New; Statistical; Learning; Methods

Brain-computer interfaces (BCIs) are an emerging communication and computer access option for people with severe physical impairments, such as those who are in a "locked-in" state due to an acquired or congenital disability. One of the most successful non-invasive BCIs for communication is the P300-BCI design, named after the brain activity that is called the P300 event-related potential (ERP). This model works by presenting stimuli (flashing groups of keys) to an on-screen keyboard. The individual's electroencephalogram (EEG) after each stimulus is then classified according to whether it contains the ERPs produced only for the stimulus the user has selected as their target. This ERP-based BCI design can be calibrated for an individual in a single session. However, the BCI still takes time for user calibration and the selection speed is slow. The calibration process has been especially challenging for people without other communication methods and for children, who have limited attention spans. This project will create new statistical methods that 1) reduce the time required to calibrate the BCI for an individual user, 2) reduce the calibration effort for individual users by leveraging prior knowledge from other BCI users, and 3) improve the selection speed of the BCI through dynamic adjustments to the patterns of stimuli. This contribution is significant because the proposed methods will substantially improve the classification process and communication speed. The research outcome of this project will also provide new insights for a better understanding of brain functions and neurobiology of thinking, and provide valuable information for the future design of the BCI system. The team will involve undergraduate and graduate students in the project, educate them on state-of-the-art research in BCI and statistical machine learning, organize summer training workshops, and develop free software.This project will develop a series of statistical methods and study their theoretical properties for analyzing brain signals from BCI systems and making statistical inferences about brain activity. The project will focus on three unique but related problems. First, the project will establish a dynamic statistical learning framework for analyzing BCI brain signals, including the split-and-merge Gaussian process for classification, a new logistic stick-breaking process for synchronization, and the novel information-guided autoencoder for extracting the latent factors in the signals. Second, the team will address BCI data integration problems, such as combining EEG from multiple users by utilizing subgroup identifications to capture the heterogeneity in the brain activity across the population, and integrating useful prior knowledge, such as brain functional connectivity networks, into statistical inferences on brain responses. Finally, the project will develop a reinforcement learning method that dynamically adjusts the presentation of groups of stimuli by the BCI for optimal identification of the target stimulus, based on the development of a Markov decision process and the Q-learning method via deep neural networks.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.

脑机接口（bci）是一种新兴的通信和计算机接入选择，适用于那些有严重身体缺陷的人，例如那些由于后天或先天性残疾而处于“闭锁”状态的人。最成功的非侵入性脑机接口之一是P300- bci设计，以被称为P300事件相关电位（ERP）的大脑活动命名。这种模式通过向屏幕上的键盘呈现刺激（闪烁的一组键）来工作。每个刺激后的个人脑电图（EEG），然后根据它是否包含仅为用户选择作为目标的刺激产生的erp进行分类。这种基于erp的BCI设计可以在单个会话中为个人进行校准。但是，BCI仍然需要用户校准时间，并且选择速度较慢。对于没有其他沟通方式的人和注意力持续时间有限的儿童来说，校准过程尤其具有挑战性。该项目将创造新的统计方法，1)减少个人用户校准脑机接口所需的时间，2)通过利用其他脑机接口用户的先验知识减少个人用户的校准工作，3)通过动态调整刺激模式来提高脑机接口的选择速度。这一贡献是重要的，因为所提出的方法将大大提高分类过程和通信速度。该项目的研究成果也将为更好地理解脑功能和思维的神经生物学提供新的见解，并为未来BCI系统的设计提供有价值的信息。该团队将让本科生和研究生参与该项目，对他们进行BCI和统计机器学习方面的最新研究，组织夏季培训研讨会，并开发免费软件。本项目将发展一系列统计方法，并研究其理论性质，用于分析脑机接口系统的脑信号，并对脑活动进行统计推断。该项目将重点关注三个独特但相关的问题。首先，该项目将建立一个动态统计学习框架来分析BCI脑信号，包括用于分类的分裂合并高斯过程，用于同步的新型逻辑断条过程，以及用于提取信号中潜在因素的新型信息引导自编码器。其次，该团队将解决脑机接口数据集成问题，例如通过利用子组识别来捕获整个人群中大脑活动的异质性，将来自多个用户的脑电图结合起来，并将有用的先验知识（如大脑功能连接网络）整合到大脑反应的统计推断中。最后，该项目将开发一种强化学习方法，该方法基于马尔可夫决策过程的发展和通过深度神经网络的q -学习方法，通过BCI动态调整刺激组的呈现，以最佳地识别目标刺激。该奖项反映了美国国家科学基金会的法定使命，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

Adaptive Sequence-Based Stimulus Selection in an ERP-Based Brain-Computer Interface by Thompson Sampling in a Multi-Armed Bandit Problem.

• DOI: 10.1109/bibm52615.2021.9669724
• 发表时间: 2021-12
• 期刊: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
• 作者: Ma, Tianwen;Huggins, Jane E;Kang, Jian
• 通讯作者: Kang, Jian