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ATLANTA – Combining nanomembrane electrodes with flexible electronics could help disabled people wirelessly control an electric wheelchair, use a computer, or operate a small robotic vehicle without a hair-electrode cap or wires, according to researchers from the Georgia Institute of Technology, University of Kent and Wichita State University.

A portable wireless brain-machine interface (BMI) could improve on conventional EEG for measuring signals. Six human subjects have been used to test the system's ability to measure EEG signals for BMI. However, the system hasn’t been studied with disabled individuals yet.

"This work reports fundamental strategies to design an ergonomic, portable EEG system for a broad range of assistive devices, smart home systems and neuro-gaming interfaces," said Woon-Hong Yeo, an assistant professor in Georgia Tech's George W. Woodruff School of Mechanical Engineering and Wallace H. Coulter Department of Biomedical Engineering. "The primary innovation is in the development of a fully integrated package of high-resolution EEG monitoring systems and circuits within a miniaturized skin-conformal system."

The system includes highly flexible, hair-mounted electrodes; an ultra-thin nanomembrane electrode; and flexible circuity with a Bluetooth telemetry unit. EEG data from the brain is processed, then wirelessly delivered to a tablet computer via Bluetooth from up to 15 meters.

"Deep learning methods, commonly used to classify pictures of everyday things such as cats and dogs, are used to analyze the EEG signals," said Chee Siang (Jim) Ang, senior lecturer in Multimedia/Digital Systems at the University of Kent. "Like pictures of a dog that can have a lot of variations, EEG signals have the same challenge of high variability. Deep learning methods have proven to work well with pictures, and we show they work very well with EEG signals as well."

The researchers used deep learning models to determine which electrodes are best for gathering information to classify EEG signals.

"We found the model is able to identify the relevant locations in the brain for BMI, which is in agreement with human experts," Ang said. "This reduces the number of sensors we need, cutting cost and improving portability."

Next, the researchers plan to improve the electrodes and make the system more useful for motor-impaired individuals.

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