A Single Hair-Thin Electrode Surpasses the Performance of the Traditional 21-Lead EEG

Scientists have created a 3D-printable electrode, as thin as a strand of human hair, that can monitor brain activity more accurately than the standard techniques used to diagnose conditions such as epilepsy and sleep disorders.

21 Electrodes for Comprehensive Brain Monitoring

When you think of someone undergoing an electroencephalogram (EEG) to diagnose a condition like epilepsy, you likely envision a head covered in multiple electrodes. That’s because the traditional EEG setup involves 21 electrodes placed on specific areas of the scalp to monitor brain activity across different regions.

A research team at Pennsylvania State University (Penn State) has introduced a breakthrough in EEG technology: a single, hair-like electrode that outperforms the conventional multi-electrode systems.

“This new electrode offers more stable and dependable EEG signal monitoring, while also being discreet and comfortable to wear,” said Tao Zhou, professor of engineering science and mechanics at Penn State and co-corresponding author of the study.

Limited Mobility and Messy Setup

EEGs, which measure the brain’s electrical activity, play a vital role in diagnosing and tracking neurological conditions like epilepsy, seizures, sleep disorders, and brain injuries. However, the rigid electrodes used in conventional EEGs can generate noise and artifacts with even slight movements, restricting patient mobility. Most standard setups rely on “wet electrodes” that require conductive gel to maintain contact with the scalp—a process that’s messy and inconvenient, especially as the gel dries out and needs reapplication to preserve signal quality.

“These factors alter the impedance, or the interface between the scalp and the electrodes, which can distort the recorded brain signals,” explained Zhou. “And since electrode placement isn’t always perfectly consistent, even small shifts can lead to variations in the data being captured.”

Penn State’s Discreet, Gel-Free EEG Electrode Offers Long-Term Wear and Enhanced Comfort

To overcome the limitations of traditional EEG setups, Penn State researchers created a gel-free, “stick-and-play” device for continuous brain monitoring. Designed for long-term wear without disrupting daily routines or drawing attention, the electrode is 3D-printed from a polymer hydrogel and measures just 300 micrometers wide—about the thickness of a human hair. It can even be customized with biocompatible dyes to match the wearer’s hair color. Instead of gel, the electrode adheres to the scalp using a 3D-printable bioadhesive that proved nearly twice as strong as commercial EEG gels. It stayed in place during activities like showering and sweating, yet removed easily without irritating the skin.

In tests comparing its performance with the standard multi-electrode EEG, the device showed strong adhesion and consistent electrical output over 24 hours, even as users went about their normal routines. Impedance levels remained steady after 12 and 24 hours, indicating no loss in signal quality. The single-strand electrode also maintained better skin contact and eliminated movement-related artifacts common in traditional setups.

“You don’t need to worry about the electrode shifting or changes in impedance because it stays securely in place,” Zhou explained.

Researchers Eye Wireless Future for Hairlike EEG with Broad Real-World Applications

At present, the hairlike EEG device is still wired, meaning users must be connected to a recording system during use. However, the research team aims to develop a wireless version and envisions a wide range of future applications.

“This innovation could power next-generation wearable tech for monitoring mental health, stress, and cognitive performance in a discreet way,” the researchers noted. “It also has the potential to improve brain-computer interface (BCI) systems by making them more comfortable and user-friendly, which could broaden their use in areas like assistive technologies, virtual reality, and more intuitive human-computer interaction in everyday life.”

Read the original article on: New Atlas

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