A group of researchers from the University of Texas at Arlington and the University of Texas at Dallas have created a hairbrush that is capable of monitoring brain function.
It might sound like something from straight out of a science-fiction novel, but apparently a group of researchers from the University of Texas at Arlington and the University of Texas at Dallas have created a hairbrush that is capable of monitoring brain function.
Typically, brain activity is monitored using functional near-infrared spectroscopy (fNIRS), which is a noninvasive optical technique that records neurological activity by measuring oxygen levels in the brain. However, this method is often impaired because of the subject’s hair getting in the way.
With the new research being done at the University of Texas, the device that has been developed, which is called a brush optrode, contains fiber tips that are designed to thread through hair, thus enhancing scalp contact and providing increased sensitivity.
"Using light to measure a person's thinking pattern has numerous advantages over EEGs, including ease of use, reliability, cost, portability and MRI compatibility," said Duncan McFarlane, a member of the research team at the University of Texas at Dallas.
"The conventional fibers used in fNIRS systems terminate in a large, flat bundle, and it is easy for a patient's hair to get in the way and block the signal. So we developed a new tip for the fNIRS fibers-a brush optrode that slides the fibers between the hair follicles. Signal levels increase 3- to 5-fold, and patients report that the brush optrode is considerably more comfortable than the conventional fiber ends. And the brush optrode is easier to set up, which saves time and money," said McFarlane.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.