Validation by a team at the University of Houston (UH) could help to cut hospital visits for the 795,000 people in the United States who annually suffer a stroke.
“We designed and validated a wireless, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications,” professor Jose Luis Contreras-Vidal, Hugh Roy and Lillie Cranz Cullen Distinguished Professor of electrical and computer engineering, reported in the journal Sensors.
An EEG-based BCI provides a pathway between the brain and external devices by interpreting EEG brain activity to initiate robotic movement. Brain-machine interfaces based on scalp EEG also have the potential to promote cortical plasticity following stroke, which can improve motor recovery outcomes.
According to UH, the adjustable headset, designed from commercial off-the-shelf components, can accommodate 90 per cent of the population. There is a patent-pending on the BCI algorithm and the self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user’s hair to ensure contact of the electrode with the scalp.
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“We used a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability and closed-loop operation,” Contreras-Vidal said in a statement.
In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices and three skin sensors were included to measure eye movement and blinks. An inertial movement unit, measuring head motion, allows for a portable brain-body imaging system for BCI applications.
“Most commercial EEG-based BCI systems are tethered to immobile processing hardware or require complex programming or set-up, making them difficult to deploy outside of the clinic or laboratory without technical assistance or extensive training,” said Contreras-Vidal. “A portable and wireless BCI system is highly preferred so it can be used outside lab in clinical and non-clinical mobile applications.”
Contreras-Vidal continued: "Current commercial EEG amplifiers and BCI headsets are prohibitively expensive, lack interoperability, or fail to provide a high signal quality or closed-loop operation, which are vital for BCI applications.”
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