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

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.