Scientists take EEG brain monitoring out of the lab
14 January 2016
Scientists have developed the first portable, 64-channel wearable brain activity monitoring system that’s comparable to state-of-the-art equipment found in research laboratories.
The system is a better fit for real-world applications because it is equipped with dry EEG sensors that are easier to apply than wet sensors, while still providing high-density brain activity data. The system comprises a 64-channel dry-electrode wearable EEG headset and a sophisticated software suite for data interpretation and analysis. It has a wide range of applications, from research, to neuro-feedback, to clinical diagnostics.
The researchers’ goal is to get EEG out of the laboratory setting, where it is currently confined by wet EEG methods. In the future, scientists envision a world where neuro-imaging systems work with mobile sensors and smart phones to track brain states throughout the day and augment the brain’s capabilities.
The researchers from the Jacobs School of Engineering and Institute for Neural Computation at UC San Diego envisage a future when neuro-imaging can be used to bring about new therapies for neurological disorders. Though further work is needed to ensure that the sensors are not only wearable but also comfortable, and algorithms for data analysis will need to be able to cut through noise to extract meaningful data.
“This is going to take neuro-imaging to the next level by deploying on a much larger scale,” says Mike Yu Chi, a Jacobs School alumnus and CTO of Cognionics* who led the team that developed the headset used in the study.
The EEG headset has an octopus-like shape, in which each arm is elastic, so that it fits on many different kinds of head shapes. The sensors at the end of each arm are designed to make optimal contact with the scalp while adding as little noise in the signal as possible. Sensors designed to work on a subject’s hair are made of a mix of silver and carbon deposited on a flexible substrate, allowing them to remain flexible and durable while still conducting high-quality signals.
For this to work, a silver/silver-chloride coating is key. Sensors designed to work on bare skin are made from a hydrogel encased inside a conductive membrane. These sensors are mounted inside a pod equipped with an amplifier, which helps boost signal quality while shielding the sensors from interferences from electrical equipment and other electronics.
Next steps include improving the headset’s performance while subjects are moving. The device can reliably capture signals while subjects walk but less so during more strenuous activities such as running. Electronics also need improvement to function for longer time periods—days and even weeks instead of hours.
The researchers have designed an algorithm that separates the EEG data in real-time into different components that are statistically unrelated to one another. It then compares these elements with clean data obtained - for instance, when a subject is at rest. Abnormal data are labelled as noise and discarded.
They also used information about the brain’s known anatomy and the data they collected to find out where the signals come from inside the brain. They were able to track, in real time, how signals from different areas of the brain interact with one another, building an ever-changing network map of brain activity. They then used machine learning to connect specific network patterns in brain activity to cognition and behaviour.
“A Holy Grail in our field is to track meaningful changes in distributed brain networks at the ‘speed of thought’,” says Tim Mullen, a UC San Diego alumnus, now CEO of Qusp** and lead author on the study. “We’re closer to that goal, but we’re not quite there yet.”
The researchers have detailed their findings in an article published recently in IEEE Transactions on Biomedical Engineering.
*Chi’s company, Cognionics, sells the headset to research groups.
**Mullen’s start-up, Qusp, has developed NeuroScale, a cloud-based software platform that provides continuous real-time interpretation of brain and body signals through an Internet application program interface. The goal is to enable brain-computer interface and advanced signal processing methods to be easily integrated with various everyday applications and wearable devices.