Can The Analysis Of Large Open-Access Neuroimaging Data Inform The Development Of More Effective Neurofeedback Training Systems?

大型开放获取神经影像数据的分析能否为更有效的神经反馈训练系统的开发提供信息？

• 批准号: RGPIN-2018-05470
• 负责人: Bardouille, Timothy
• 金额: $ 1.75万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713693
• 关键词: Analysis; Large; Open; Access; Neuroimaging

My research program develops new technology (hardware and software) to aid people to train their brain more effectively. We have developed innovative technology that guides people when they are thinking about a task. The technology provides feedback about how their brain is working. The feedback - provided on an electronic device - gives a second-by-second report about brain activity, called neurofeedback. Neurofeedback might help people to use their brain in a way that will help them learn more effectively. Neurofeedback is particularly important when we are thinking about a task during the learning process because we don't usually get feedback, which is key to learning. Our technology provides that feedback, and helps users to change their brain activity for the better. The problem is that our neurofeedback technology is not as effective as it could be. Our technology assumes that the brain activity that a user generates matches to the pattern that you see if you average a lot of people's brain activity together. This doesn't always work because there is a lot of variability in how different people use their brains. My research program will develop new software to provide personalized neurofeedback that accounts for the variability between users. I will test to see if personalized neurofeedback helps people change their brain activity for the better. My research will also, for the first time, use big data to better understand the signals that we will use when we personalize neurofeedback. This will combine the software that we develop for analyzing brain imaging data with big data using a set of algorithms called machine learning (borrowed from Computer Science). This novel combination has the potential to improve neurofeedback systems by accounting for individual variability, as well as demographic factors like age and gender. A neurofeedback system is only effective if it targets the right signals from a person's brain. My research is focused on making sure that we target the right signals every time. If neurofeedback is more effective, then it will be used by more researchers and companies to help people improve their brains. This has the potential to help people learn better, and to help patients recover better after a stroke or brain injury. I have collaborators that are interested in these applications for my technology. My Discovery Grant research will help them to help people.

我的研究项目开发新技术（硬件和软件）来帮助人们更有效地训练他们的大脑。我们开发了一种创新技术，可以在人们思考任务时进行引导。这项技术提供了关于他们大脑如何工作的反馈。这种反馈——通过电子设备提供——给出一份关于大脑活动的逐秒报告，称为神经反馈。神经反馈可能会帮助人们以一种帮助他们更有效地学习的方式使用他们的大脑。当我们在学习过程中思考一项任务时，神经反馈尤为重要，因为我们通常不会得到反馈，而反馈是学习的关键。我们的技术提供反馈，并帮助用户更好地改变他们的大脑活动。