SCH: New Statistical Learning Methods for Brain-Computer Interfaces

脑机接口(BCI)是一种新兴的通信和计算机访问选择，适用于严重身体残疾的人，例如那些因后天或先天残疾而处于“锁定”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。该模型通过向屏幕上的键盘提供刺激(闪烁的按键组)来工作。然后，个体在每个刺激后的脑电(EEG)被分类，根据它是否包含仅针对用户选择的刺激作为他们的目标而产生的ERPs。这种基于ERP的BCI设计可以在单个会话中针对个人进行校准。然而，BCI仍然需要时间进行用户校准，选择速度较慢。对于没有其他交流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。该项目将创建新的统计方法，1)减少校准单个用户的BCI所需的时间，2)通过利用其他BCI用户的先验知识来减少单个用户的校准工作，以及3)通过动态调整刺激模式来提高BCI的选择速度。这一贡献是显著的，因为所提出的方法将显著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大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，教育他们在脑-机接口和统计机器学习方面的最新研究，组织暑期培训研讨会，并开发免费软件。该项目将开发一系列统计方法，并研究其理论性质，以分析来自脑-机接口系统的大脑信号，并对大脑活动做出统计推断。该项目将重点解决三个独特但相关的问题。首先，该项目将建立一个用于分析BCI脑信号的动态统计学习框架，包括用于分类的拆分合并高斯过程，用于同步的新的Logistic折断过程，以及用于提取信号中潜在因素的新型信息制导自动编码器。其次，该团队将解决BCI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来组合来自多个用户的脑电，以及将有用的先验知识(如大脑功能连接网络)整合到关于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和基于深度神经网络的Q学习方法的发展，动态调整BCI对刺激组的呈现，以优化目标刺激的识别。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。

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.