Scientists at the University of Texas at Austin have built a thin, soft patch that can improve REM sleep. The patch, called NEUSLeeP, sits on the skin and sends tiny ultrasound pulses while tiny sensors record brain activity at the same time.
"For the first time we can reach deep brain areas that control REM sleep without any needles or surgery," said Kai Wing “Kevin” Tang, a recent Ph.D. graduate who led the work. "The patch also tells us how the brain reacts right away," added Huiliang “Evan” Wang, the project’s principal investigator.
Patch Makes REM Sleep Arrive Sooner
In a real‑world test with 28 volunteers, the patch helped people fall into REM sleep about 43 minutes earlier than usual. Once they entered REM, they stayed there roughly 16 minutes longer.
Both healthy sleepers and those with mild sleep problems saw the benefit. Participants said the patch felt comfortable and safe, and only a few mild side effects were reported.
Extra Help for Stress and Mood
Healthy volunteers also showed higher heart‑rate variability, a sign that the body can handle stress better. Brain scans revealed small changes in areas linked to emotions, suggesting the patch might aid mood regulation.
"REM sleep is more than dreaming; it resets emotions and helps us cope with stress," explained Gregory Fonzo, a co‑principal investigator from the Dell Medical School. "Improving REM could make people more resilient and happier."
Looking Ahead: Insomnia, Depression, PTSD
The researchers plan larger studies to see if NEUSLeeP can help people with chronic insomnia, depression, or post‑traumatic stress disorder. They also see uses for home sleep monitoring, brain research, and personalized treatment plans.
"Our goal is a safe, non‑invasive way for anyone with mental‑health challenges to get better sleep," said Dr. Vincent Mysliwiec, a sleep‑disorder expert and co‑PI.
Moving Toward the Market
The team is working with UT’s commercialization office, Discovery to Impact, to bring the patch to consumers. A patent application is already in progress.
Team members include engineers, psychologists, and researchers from UT Austin and Virginia Tech who helped develop the technology.