A new portable EEG monitor developed by American researchers could pave the way for constant brain-activity monitoring.
While most existing systems incorporate so called 'wet electrodes' that can only be used in laboratory settings, the new system developed by a team from the University of California, San Diego, relies on 'dry' EEG sensors that are easier to use.
The team says that the system is as accurate as the best existing laboratory equipment and could be used for monitoring subjects in real-life situations.
"This is going to take neuroimaging to the next level by deploying on a much larger scale," said Mike Yu Chi, who led the team developing the headset. "You will be able to work in subjects' homes. You can put this on someone driving."
Such a system could be used to feed data into smartphone applications to monitor brain activity throughout the day to optimise the brain’s performance.
"We will be able to prompt the brain to fix its own problems," said Professor Gert Cauwenberghs. "We are trying to get away from invasive technologies, such as deep brain stimulation and prescription medications, and instead start up a repair process by using the brain's synaptic plasticity."
The headset is fitted with two types of device. The sensors in touch with the subject’s hair are made of a mix of silver and carbon deposited on a flexible substrate. Sensors intended to attach to bare skin are made from a hydrogel encased inside a conductive membrane.
Testing the headset, the researchers were able to create an evolving network map of brain activity displaying signals and their propagation.
While the headset worked well on walking subjects, its accuracy dropped when the subjects were engaged in more strenuous activity, such as running.
A major obstacle that had to be overcome was filtering out the brain signal from noise created by the body’s motions.
The researchers designed an algorithm that separates the EEG data in real-time into different components which are statistically unrelated to one another. It then compared these elements with clean data obtained, for instance, when a subject is at rest. Abnormal data were labelled as noise and discarded. "The algorithm attempts to remove as much of the noise as possible while preserving as much of the brain signal as possible," said Mullen.
"A Holy Grail in our field is to track meaningful changes in distributed brain networks at the 'speed of thought'. We're closer to that goal, but we're not quite there yet,” he added.