Auburn researcher works to improve brain-computer interface capabilities

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Supported by a $175,000 grant from the National Science Foundation, or NSF, a researcher from Auburn University’s College of Science and Mathematics is working to improve the capabilities of the brain-computer interface, or BCI, a breakthrough that has broad potential to help people with severe motor impairments, detect and diagnose health conditions, and provide new interfaces for games and other uses.

Jingyi “Ginny” Zheng, assistant professor in the Department of Mathematics and Statistics, received her award from the NSF Division of Computing and Communication Foundations for her project “Towards A Manifold-based Framework for the Brain- Computer Interface”.

“We are working to improve the current statistical framework on which BCI operates by using a new metric to quantify differences in human brain connectivity,” Zheng said.

BCI as a technology has been around for a number of years, Zheng says. However, it is limited in its current functionality by its mathematical framework.

“The current measure used in BCI to quantify differences in brain connectivity is not robust and is inefficient,” Zheng said. “We seek to upgrade the BCI system by developing a statistical framework using a new mathematical measure to quantify differences in brain connectivity matrices.”

BCI works by linking the human brain to a mini-computer. The user wears a special headset that picks up brain waves and transmits them to a computerized recording device. This mini-computer translates the human brain wave – essentially a thought – into a mechanical response.

“For example, a severely motor-impaired person confined to a wheelchair could use and maneuver their chair by thought using a BCI device,” Zheng said. “Over the past decades, many methods have been implemented in BCIs to decode and translate brain signals. However, BCIs still suffer from low robustness and low reliability as they are susceptible to artifacts, noise, outliers and require a long calibration process.

Zheng’s research aims to build a multiple-based framework that will improve the robustness of BCI technologies and broaden practical applications. The project is funded until 2024, and Zheng said that once BCI’s capabilities are enhanced, the technology has potential not only in health and medicine, but also in biology, neuroscience, agriculture, remote sensing, computer vision and other fields.

Zheng joined the faculty at Auburn in 2019. His research interests lie in the areas of data science, machine learning, and data-driven computing. She also teaches several statistics courses at the College of Science and Mathematics.

(Written by Mitch Emmons)