Ink-based e-tattoo can decode brain waves

AUSTIN, Texas – Since the advent of temporary, skin-mounted electronic tattoos more than a decade ago – and their customization evolution cardiac activity, Track pneumoniameasure Stress leveland more – Researchers have grappled with a major logistical challenge: How to apply them to hairy skin without losing performance?

To figure out this challenge, researchers at the University of Texas at Austin and the University of California at Los Angeles borrowed from regular tattoos. The researchers developed a conductive ink that can be printed directly on the surface of a patient’s head and measures their brain waves.

These e-tattoos serve as sensors for electroencephalography (EEG), a medical test that measures the brain’s electrical activity. EEG can help diagnose and monitor brain tumors, sleep disorders, and other brain problems.

“The holy grail for EEG is a sensor that patients can wear for long periods of time, outside of the clinical setting and without constant maintenance,” said Nanshu Lu, a pioneer in e-tattoos and a professor in the Cockrell School of Engineering Department of Air – and space engineering and technical mechanics and one of the leaders of the project. “What we have developed opens the door to more mobile EEG capture.”

See photos and videos of research at work.

The best EEG machines today include a cap and rigid or soft electrodes that are attached to the head with many long wires. Manually mapping the patient’s head can take several hours and the soft electrodes can dry out quickly.

The new method, published in Cell biomaterialsuses a camera to digitally map the individual head shape. The researchers developed an algorithm that designs the EEG sensors specifically for the person and tells a robotic printer where to place the conductive ink.

The printer does not touch the patient. It propels the ink fast enough to penetrate the hair – although researchers have only had success with short-haired patients. In addition to the printed sensors and connectors, short cables are used to connect the printed e-tattoo to a small, commercial EEG recorder.

José del R. Millán, a professor in the Cockrell School’s Chandra Family Department of Electrical and Computer Engineering and the Dell Medical School’s Department of Neurology and a leading expert in brain-computer interfaces, is one of the project’s co-authors. These e-tattoos could transform brain-computer interfaces that use the same devices as EEG, making them easier to use in everyday activities and less intrusive to the user.

Brain-computer interfaces allow people to control devices using just their thoughts. This technology can help people with cognitive impairments live better lives. In recent years, Millán has developed one using brain-computer interface technology brain-powered wheelchair and a mind-controlled decision-making game.

“This design is extremely flat and mechanically imperceptible to the user,” Millán said. “The fact that this device requires less setup and maintenance, and the user could eventually wear a hat or helmet over it, means we could achieve longer recording times and learn more about their brain activity.”

Going forward, the researchers have two main goals: The first is to improve its applicability in patients with longer hair. Lu mentioned robotic fingers and combs that could quickly separate hair to enable printing.

The second is to increase the frictional resistance of the ink. Today it rubs off in the shower or while sleeping. However, the researchers’ goal is to improve the ink’s durability so that a patient can sleep through the night without it rubbing off. This would be particularly useful for monitoring sleep disorders and other unpredictable conditions such as epilepsy in everyday life.

The research team also includes Luize Scalco de Vasconcelos, Pukar Maharjan, Hongbian Li, Eric Li, Eric Williams, Sandhya Tiku, Pablo Vidal, Charles Block, Andrew T. Repetski, Philip Tan and Pulin Wang of UT Aerospace Engineering; Satyam Kumar, Minsu Zhang and Fumiaki Iwane from UT Electrical and Computer Engineering; Martín G. Martín of Dell Med Neurology; and Yichen Yan, Bowen Yao, Sidi Duan, R. Sergio Solorzano-Vargas, Wen Hong, Yingjie Du, Zixiao Liu and Ximin He from the David Geffen School of Medicine at the University of California, Los Angeles.