Paralyzed man controls a robotic arm using his thoughts for seven months without requiring recalibration using a brain-computer interface (BCI) enhanced with AI machine learning.
machine learning that is capable of empowering a paralyzed man to control a robotic arm using his thoughts for seven months without requiring recalibration. This study shows significant advancement for brain-computer interfaces given prior BCIs require adjustments after only a day or two according to a UCSF release.
“Studies in animals have indicated that neural representations can experience drift—changes in the correlation between activity and behavior over time,” wrote corresponding author and neurologist Karunesh Ganguly, MD, PhD, along with co-authors Nikhilesh Natraj, Sarah Seko, Reza Abiri, Runfeng Miao, Hongyi Yan, Yasmin Graham, Adelyn Tu-Chan, and Edward Chang.
To test this hypothesis, the UCSF researchers recorded electrocorticography activity in the left sensorimotor cortex brain region of a study participant who had lost control of the muscles required to speak and severe muscle weakness of both legs and arms . The sensors on the subject’s brain captured the neural activity during the mental exercise of imagining the movement of various parts of his body. The researchers found that the overall shape of the brain’s representations of these movements did not change, but the locations varied a little bit day-to-day.
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