Re: BCIs & Neurotechnology News and Discussions
Posted: Sat Nov 05, 2022 5:03 am
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In a multi-year research program coordinated by the two directors of .NeuroRestore—Grégoire Courtine, a neuroscience professor at EPFL, and Jocelyne Bloch, a neurosurgeon at Lausanne University Hospital (CHUV)—patients who had been paralyzed by a spinal cord injury and who underwent targeted epidural electrical stimulation of the area that controls leg movement were able to regain some motor function.
In the new study by .NeuroRestore scientists, appearing today in Nature, not only was the efficacy of this therapy demonstrated in nine patients, but the improved motor function was shown to last in patients after the neurorehabilitation process was completed and when the electrical stimulation was turned off. This suggested that the nerve fibers used for walking had reorganized. The scientists believe it was crucial to understand exactly how this neuronal reorganization occurs in order to develop more effective treatments and improve the lives of as many patients as possible.
Read more here: https://www.eurekalert.org/news-releases/971279(EurekAlert) New Caltech research is showing how devices implanted into people’s brains, called brain-machine interfaces (BMIs), could one day help patients who have lost their ability to speak. In a new study presented at the 2022 Society for Neuroscience conference in San Diego, the researchers demonstrated that they could use a BMI to accurately predict which words a tetraplegic participant was simply thinking and not speaking or miming.
“You may already have seen videos of people with tetraplegia using BMIs to control robotic arms and hands, for example to grab a bottle and to drink from it or to eat a piece of chocolate,” says Sarah Wandelt, a Caltech graduate student in the lab of Richard Andersen, 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 a BMI to reconstruct speech,” says Wandelt, who presented the results at the conference on November 13.
Previous studies have had some success at predicting participants’ speech by analyzing brain signals recorded from motor areas when a participant whispered or mimed words. But predicting what somebody is thinking, internal dialogue, is much more difficult, as it does not involve any movement, explains Wandelt. “In the past, algorithms that tried to predict internal speech have only been able to predict three or four words and with low accuracy or not in real time,” Wandelt says.
The new research is the most accurate yet at predicting internal words. In this case, brain signals were recorded from single neurons in a brain area called the supramarginal gyrus, located in the posterior parietal cortex. The researchers had found in a previous study that this brain area represents spoken words.
Read more of the EurekAlert article here: https://www.eurekalert.org/news-releases/970833(EurekAlert) A mind-controlled wheelchair can help a paralyzed person gain new mobility by translating users’ thoughts into mechanical commands. On November 18 in the journal iScience, researchers demonstrate that tetraplegic users can operate mind-controlled wheelchairs in a natural, cluttered environment after training for an extended period.
“We show that mutual learning of both the user and the brain-machine interface algorithm are both important for users to successfully operate such wheelchairs,” says José del R. Millán, the study’s corresponding author at The University of Texas at Austin. “Our research highlights a potential pathway for improved clinical translation of non-invasive brain-machine interface technology.”
Millán and his colleagues recruited three tetraplegic people for the longitudinal study. Each of the participants underwent training sessions three times per week for 2 to 5 months. The participants wore a skullcap that detected their brain activities through electroencephalography (EEG), which would be converted to mechanical commands for the wheelchairs via a brain-machine interface device. The participants were asked to control the direction of the wheelchair by thinking about moving their body parts. Specifically, they needed to think about moving both hands to turn left and both feet to turn right.
In the first training session, three participants had similar levels of accuracy—when the device’s responses aligned with users’ thoughts—of around 43% to 55%. Over the course of training, the brain-machine interface device team saw significant improvement in accuracy in participant 1, who reached an accuracy of over 95% by the end of his training. The team also observed an increase in accuracy in participant 3 to 98% halfway through his training before the team updated his device with a new algorithm.
The improvement seen in participants 1 and 3 is correlated with improvement in feature discriminancy, which is the algorithm’s ability to discriminate the brain activity pattern encoded for “go left” thoughts from that for “go right.” The team found that the better feature discrimnancy is not only a result of machine learning of the device but also learning in the brain of the participants. The EEG of participants 1 and 3 showed clear shifts in brainwave patterns as they improved accuracy in mind-controlling the device.
Neuralink owner Elon Musk said paperwork for human trials has been submitted to the U.S. Food and Drug Administration (FDA), which he claimed could lead to human trials of the company’s brain-implant technology “in about six months.”
Neuralink, which Musk helped to set up in 2016, is developing a system that directly links the human brain to a computer interface. It believes the technology, part of which is implanted directly into the brain, will one day allow the human mind to control gadgets and programs merely through thought, potentially opening up a whole new world for people with brain disorders and conditions such as paralysis.
In April 2021, Neuralink demonstrated early trials of the technology by showing a monkey playing a game of Pong just by thinking about it.