"The major advantage of this system to the user, compared to what currently exists, is that it is soft and comfortable to wear, and doesn't have any wires," said Yeo, associate professor on the George W. Woodruff School of Mechanical Engineering.
BMI systems are a rehabilitation technology that analyzes a person's brain signals and translates that neural activity into commands, turning intentions into actions. The most common non-invasive method for acquiring those signals is ElectroEncephaloGraphy, EEG, which typically requires a cumbersome electrode skull cap and a tangled web of wires.
These devices generally rely heavily on gels and pastes to help maintain skin contact, require extensive set-up times, are generally inconvenient and uncomfortable to use. The devices also often suffer from poor signal acquisition due to material degradation or motion artifacts - the ancillary "noise" which may be caused by something like teeth grinding or eye blinking. This noise shows up in brain-data and must be filtered out.
The portable EEG system Yeo designed, integrating imperceptible microneedle electrodes with soft wireless circuits, offers improved signal acquisition. Accurately measuring those brain signals is critical to determining what actions a user wants to perform, so the team integrated a powerful machine learning algorithm and virtual reality component to address that challenge.
The new system was tested with four human subjects, but hasn't been studied with disabled individuals yet.
"This is just a first demonstration, but we're thrilled with what we have seen," noted Yeo, Director of Georgia Tech's Center for Human-Centric Interfaces and Engineering under the Institute for Electronics and Nanotechnology, and a member of the Petit Institute for Bioengineering and Bioscience.
COMPAMED-tradefair.com; Source: Georgia Institute of Technology