Electroencephalography (EEG), a technique for measuring brain activity, has long been a cornerstone of diagnosing neurological conditions like epilepsy, brain tumors, and head trauma. However, the process often proves time-consuming and uncomfortable for patients, with head shape, skull size, and more playing a role in how accurate the readings can be. Luckily, researchers have created a temporary EEG tattoo that can help.
Researchers at the University of Texas at Austin created the new procedure, which simplifies and enhances the EEG process. These tattoos, created using specialized conductive ink, offer a more efficient and patient-friendly approach to brainwave monitoring.
The EEG tattoo’s ink is made from two polymers: poly(3,4-ethylenedioxythiophene) (PEDOT) and polystyrene sulfonate (PSS). These materials are known for their conductivity and durability. They also avoid irritating the skin. To ensure precise electrode placement, a computer program generates a personalized design based on a 3D scan of the patient’s scalp.
From here, a robotic arm then prints the tattoo directly onto the skin. The result is a lightweight, skin-friendly e-tattoo that can pick up high-quality brain signals. This advanced system uses two types of ink formulations: one for electrodes that detect brain activity and another for connections that lead to the back of the neck.
While physical wires currently carry signals from the tattoo to a recording device, the research team envisions a wireless future, with embedded data transmitters eliminating the need for cables altogether when these EEG tattoos are utilized.
Testing so far has shown promising results, too, especially on individuals with bald or buzz-cut hairstyles. For those with thick, curly hair, the team is exploring modifications, such as redesigned nozzles or robotic fingers to part hair during the printing process.
The potential of these temporary EEG tattoos extends beyond practicality. By reducing preparation time and patient discomfort, they could make brain monitoring more accessible and appealing for widespread clinical and research applications. This could make it much easier to analyze the brain and brain conditions, including brain-related diseases like Alzheimer’s.
