We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Non-Invasive Brain Imaging Technique Distinguishes Hand Gestures

The 306-sensor MEG helmet that detects nerve activity in the brain by measuring the magnetic field.
The 306-sensor MEG helmet that detects nerve activity in the brain by measuring the magnetic field. Credit: MEG Center at UC San Diego Qualcomm Institute
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 2 minutes

Researchers from University of California San Diego have found a way to distinguish among hand gestures that people are making by examining only data from noninvasive brain imaging, without information from the hands themselves. The results are an early step in developing a non-invasive brain-computer interface that may one day allow patients with paralysis, amputated limbs or other physical challenges to use their mind to control a device that assists with everyday tasks.

The research, published online ahead of print in the journal Cerebral Cortex on May 13, 2023, represents the best results thus far in distinguishing single-hand gestures using a completely noninvasive technique, in this case, magnetoencephalography (MEG).

Want more breaking news?

Subscribe to Technology Networks’ daily newsletter, delivering breaking science news straight to your inbox every day.

Subscribe for FREE

“Our goal was to bypass invasive components,” said the paper’s senior author Mingxiong Huang, PhD, co-director of the MEG Center at the Qualcomm Institute at UC San Diego. Huang is also affiliated with the Department of Electrical and Computer Engineering at the UC San Diego Jacobs School of Engineering and the Department of Radiology at UC San Diego School of Medicine, as well as the Veterans Affairs (VA) San Diego Healthcare System. “MEG provides a safe and accurate option for developing a brain-computer interface that could ultimately help patients.”

The researchers underscored the advantages of MEG, which uses a helmet with embedded 306-sensor array to detect the magnetic fields produced by neuronal electric currents moving between neurons in the brain. Alternate brain-computer interface techniques include electrocorticography (ECoG), which requires surgical implantation of electrodes on the brain surface, and scalp electroencephalography (EEG), which locates brain activity less precisely.

“With MEG, I can see the brain thinking without taking off the skull and putting electrodes on the brain itself,” said study co-author Roland Lee, MD, director of the MEG Center at the UC San Diego Qualcomm Institute, emeritus professor of radiology at UC San Diego School of Medicine, and physician with VA San Diego Healthcare System. “I just have to put the MEG helmet on their head. There are no electrodes that could break while implanted inside the head; no expensive, delicate brain surgery; no possible brain infections.”

Lee likens the safety of MEG to taking a patient’s temperature. “MEG measures the magnetic energy your brain is putting out, like a thermometer measures the heat your body puts out. That makes it completely noninvasive and safe.”

Rock Paper Scissors

The current study evaluated the ability to use MEG to distinguish between hand gestures made by 12 volunteer subjects. The volunteers were equipped with the MEG helmet and randomly instructed to make one of the gestures used in the game Rock Paper Scissors (as in previous studies of this kind). MEG functional information was superimposed on MRI images, which provided structural information on the brain.

To interpret the data generated, Yifeng (“Troy”) Bu, an electrical and computer engineering PhD student in the UC San Diego Jacobs School of Engineering and first author of the paper, wrote a high-performing deep learning model called MEG-RPSnet.

“The special feature of this network is that it combines spatial and temporal features simultaneously,” said Bu. “That’s the main reason it works better than previous models.”

When the results of the study were in, the researchers found that their techniques could be used to distinguish among hand gestures with more than 85% accuracy. These results were comparable to those of previous studies with a much smaller sample size using the invasive ECoG brain-computer interface.

The team also found that MEG measurements from only half of the brain regions sampled could generate results with only a small (2 – 3%) loss of accuracy, indicating that future MEG helmets might require fewer sensors.

Looking ahead, Bu noted, “This work builds a foundation for future MEG-based brain-computer interface development.”

Reference: Bu Y, Harrington DL, Lee RR, et al. Magnetoencephalogram-based brain–computer interface for hand-gesture decoding using deep learning. Cereb. Cortex. 2023:bhad173. doi: 10.1093/cercor/bhad173

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.