Brain-machine interfaces could one day help patients who have lost their ability to speak

Brain-machine interfaces could one day help patients who have lost their ability to speak

New Caltech research shows how devices implanted into people's brains, called brain-machine interfaces (BMIs), could one day help patients who have lost the ability to speak. In a new study presented at the 2022 Society for Neuroscience conference in San Diego, researchers showed that they could use a BMI to accurately predict which words a quadriplegic participant only thought and did not speak or mime.

“You may have seen videos of people with quadriplegia using BMIs to control robotic arms and hands to, for example, grab a bottle and drink from it or eat a piece of chocolate,” says Sarah Wandelt, a graduate student in Caltech's Richard Andersen lab, the James G. Boswell Professor of Neuroscience and director of the Tianqiao and Chrissy Chen Brain-Machine Interface Center at Caltech.

"These new results are promising in the areas of language and communication. We used BMI to reconstruct language," says Wandelt, who presented the results at the November 13 conference.

Previous studies have had some success in predicting participants' speech by analyzing brain signals recorded from motor areas when a participant whispered or mimed words. But predicting what someone is thinking, the internal dialogue, is much more difficult because there is no movement, explains Wandelt. “In the past, algorithms that tried to predict internal speech could only predict three or four words and with low accuracy or not in real time,” says Wandelt.

The new research is the most accurate yet at predicting internal words. In this case, brain signals were recorded from individual neurons in a brain area called the supramarginal gyrus, located in the posterior parietal cortex. In a previous study, the researchers found that this area of ​​the brain represents spoken words.

Now the team has extended its findings to internal language. In the study, researchers first trained the BMI device to recognize the brain patterns produced when certain words were internally spoken or thought by the quadriplegic participant. This training phase lasted approximately 15 minutes. They then showed a word on a screen and asked the participant to say the word internally. The results showed that the BMI algorithms were able to predict eight words with up to 91 percent accuracy.

The work is still preliminary but could help patients with brain injuries, paralysis or diseases such as amyotrophic lateral sclerosis (ALS) that affect speech.

Neurological disorders can result in complete paralysis of voluntary muscles, leaving patients unable to speak or move but still able to think and reason. For this population, an internal language BMI would be incredibly helpful.”

Sarah Wandelt, a Caltech graduate student

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“We have previously shown that we can decode imaginary hand shapes for grasping from the human supramarginal gyrus,” says Andersen. “The ability to also decode speech from this area suggests that an implant can restore two important human abilities: grasping and speaking.”

The researchers also point out that BMIs cannot be used to read people's minds; The device would have to be trained in each person's brain separately, and they only work when a person focuses on the word.

The study, which is in the process of journal submission but has not yet been peer-reviewed, is titled “Online internal speech decoding of single neurons in a human participant.” It was funded by the National Institutes of Health, the Tianqiao and Chrissy Chen Brain-Machine Interface Center, and the Boswell Foundation. Other Caltech authors in addition to Wandelt and Andersen include David Bjanes, Kelsie Pejsa, Brian Lee (PhD '06), and Charles Liu. Lee and Liu are Caltech Visiting Associates who are on the faculty of the Keck School of Medicine at USC.

Source:

California Institute of Technology

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