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算法。 用于脑-机接口的脑电（EEG）信号中的移位检测的出租ms 让他们更可靠。脑机接口是严重残疾人的一种交流手段， 通过解码大脑反应并将其检测转化为命令， 键盘或机器人控制系统。目前的脑机接口系统无法有效部署 在临床环境中，由于他们不能适当地考虑诱发的非平稳特性， 脑电图信号中的大脑反应。该项目旨在提高大脑解码性能- 当任务随着时间的推移而改变时，PI建议调查定义明确的数据类型的影响 移位：协变量移位、概率移位和概念移位，以增强大脑在变化中的解码性能。 任务本研究的目标是：1）研究事件相关电位（ERP）的神经成分 通过使用EEG记录和机器学习技术， 审判侦查2)研究与任务变化相对应的功能性大脑连接神经信号， 使用EEG记录和功能性大脑连接的定向的基于模型和无模型技术。3)到 联合收割机和调整机器学习技术，以检测任务期间何时发生变化。这项建议会 显著改善研究和教育的基础设施在加州州立大学弗雷斯诺，西班牙裔- 服务机构和亚裔美国人和美国土著太平洋岛屿服务机构，介绍 生物医学工程研究经验，以代表性不足的少数民族和女学生在计算机 科学和心理学的学生。这将使他们能够体验科学方法的不同阶段， 并获得与应用于生理信号的数据科学相关的基本技能， 改善严重残疾人生活协会。

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.