Brain-Computer Interface in dynamic tasks with deep learning and functional connectivity analysis

摘要 PI提出了一个高影响力的多学科研究项目，以开发和验证机器学习方法。 脑电信号移位检测算法及其在脑-机接口中的应用 让他们更可靠。脑机接口是严重残障人士交流的一种手段。 通过解码大脑反应并将其检测转换为命令，使用诸如虚拟的 键盘或机器人控制系统。目前的脑机接口系统不能有效地部署fi 在临床环境中，由于他们无法正确考虑诱发的非平稳特性 脑电信号中的大脑反应。该项目旨在提高大脑的解码性能-- 当任务随着时间的推移而发生变化时。PI建议调查Well defiNed类型数据的影响 变换：协变量变换、概率变换和概念变换提高大脑在变化中的解码能力 任务。该方案的目标是：1)表征事件相关电位(ERP)中的神经成分 通过使用脑电记录和机器学习技术来实现与任务变化相对应的签名 试探性检测。2)研究与任务变化相对应的功能性脑连接神经信号 使用脑电记录和定向的基于模型的和无模型的大脑功能连接技术。3)至 结合并调整机器学习技术，以检测任务期间何时发生更改。这项提议将 Signifi可以改善西班牙裔加州州立大学弗雷斯诺分校的研究和教育基础设施- 服务机构和一个亚裔美国人和美洲原住民Pacific岛民服务机构，介绍 计算机专业少数民族和女生生物医学工程研究体会 理科和心理学专业的学生。这将允许他们体验科学fi方法的不同阶段， 并掌握与应用于具有潜在影响的生理信号的数据科学相关的基本技能 该协会致力于改善严重残疾人的生活。

Abstract The PI proposes a high-impact multi-disciplinary research project to develop and validate machine learning al- gorithms for shift-detection in electroencephalogram (EEG) signals with applications to brain-computer interface to make them more reliable. Brain-computer interface is a means of communication for severely disabled peo- ple by decoding brain responses and translating their detection into commands with applications such a virtual keyboard or robotic control systems. Current brain-computer interface systems cannot be efficiently deployed in clinical setting due to their inability to properly take into account the non-stationarity properties of the evoked brain responses in the electroencephalogram signal. This project aims at enhancing the brain decoding perfor- mance when the task changes over time. The PI proposes to investigate the effects of well defined types of data shifts: covariate shift, probability shift, and concept shift to enhance brain decoding performance in changing tasks. The goals of this proposal are: 1) to characterize in event related potential (ERP) components neural signatures corresponding to task changes by using EEG recordings and machine learning techniques for single- trial detection. 2) to research in functional brain connectivity neural signature corresponding to task changes by using EEG recordings and directed model-based and model free techniques of functional brain connectivity. 3) to combine and adapt machine learning techniques to detect when changes occur during a task. This proposal will significantly improve the infrastructure of research and education at California State University Fresno, Hispanic- Serving Institution and an Asian American and Native American Pacific Islander-Serving Institution, introducing biomedical engineering research experiences to underrepresented minority and female students in computer science and psychology students. This would allow them to experience different stages of the scientific method, and acquire fundamental skills related to data science applied to physiological signals with potential impact on society for improving the life of severely disabled people.