Non-Invasive Brain Imaging: A Tool to Help Paralysis Patients

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Researchers at the University of California, San Diego, discovered a means to identify people's hand movements by reviewing solely data from noninvasive brain imaging, rather than information from the hands themselves. The findings represent an early step in developing a non-invasive brain-computer interface that could one day allow patients with paralysis, amputated limbs, or other physical problems to use their minds to control a device that aids in daily tasks.

The study, published online ahead of print in the journal Cerebral Cortex, shows the best results to date in differentiating single-hand gestures using a wholly noninvasive technique, magnetoencephalography (MEG).

Magnetoencephalography: A Key to Helping Paralysis Patients

"Our goal was to bypass invasive components," said the paper's senior author Mingxiong Huang, Ph.D., 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 the 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."

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The researchers underscored the advantages of MEG, which uses a helmet with an 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."

Distinguishing Rock Paper Scissors with Magnetoencephalography

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 Ph.D. student at the UC San Diego Jacobs School of Engineering and the 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 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:

1. Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning. - (https:europepmc.org/article/med/37183188)

Source: Eurekalert