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Portable Monitoring System Tracks Real-Time Brain Activity

By LabMedica International staff writers
Posted on 07 Feb 2016
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Image: The Cognionics wearable 72-channel EEG headset (Photo courtesy of Cognionics).
Image: The Cognionics wearable 72-channel EEG headset (Photo courtesy of Cognionics).
An innovative wearable brain activity monitoring system with dry electroencephalogram (EEG) sensors provides a better solution for real-world applications.

Developed by researchers at the University of California, San Diego (UCSD, USA), the HD-72 headset features a wearable 72-channel (64 EEG + 8 ExG) form factor, compact electronics with active shielding, and a wireless triggering system. Active dry-contact electrodes leverage a pressure-induced flexing mechanism to contact the scalp through hair. A sophisticated software suite wirelessly streams data for online analysis, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification.

The octopus-like headset has 18 tentacles, in which each arm is elastic, so that it can fit different 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. The sensors are are made of a mix of silver and carbon deposited on a flexible substrate with a silver/silver-chloride coating. This allows them to remain flexible and durable while still conducting high-quality signals. The data captured is first separated from high amplitude artifacts generated when subjects move, speak, or even blink.

This is achieved by an algorithm that separates the EEG data into different components statistically unrelated to one another. It then compares these elements with clean data obtained, for instance, when a subject is at rest; abnormal data is labeled as noise and discarded. The data collected is also tracked to see how signals from different areas of the brain interact with one another, creating an ever-changing network map of brain activity. Machine learning then connects specific network patterns in brain activity to cognition and behavior. The study describing the system was published in the November 2015 issue of IEEE Transactions on Biomedical Engineering.

“This is going to take neuroimaging to the next level by deploying on a much larger scale. You will be able to work in subjects’ homes; you can put this on someone driving,” said study coauthor Mike Yu Chi, MSc and CTO of Cognionics (San Diego, CA, USA), which is developing the system commercially.

Related Links:

University of California, San Diego
Cognionics


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