Université de Montréal

After days of practicing a brain-computer interface with adaptive decoders, rhesus macaques needed significantly fewer motor cortex neurons to reach 80% task classification accuracy than in early sessions (p = 9.8 x 10^-4). Fixed decoders produced no such compaction. A neural network model suggests the adaptive decoders directly contribute to these more compact representations; the authors propose this may be because the decoders alter the error information used for credit assignment. The finding implies that assistive algorithms do not merely boost immediate BCI performance but actively reshape how the brain learns over time -- a consideration for real-world decoder design. The study used 128-channel microwire arrays in motor and premotor cortices of two monkeys and has not been tested in humans.

Researchers have found that whether people can learn to control a device with their thoughts may depend on whether the control scheme follows the brain's own activity patterns. Participants lying in an MRI scanner controlled a video game avatar in real time by modulating activity in brain regions responsible for spatial navigation. When the researchers then changed the mapping between brain activity and avatar movement, participants could readjust and regain control if the new mapping fell along directions in which their brain activity already varied widely, known as the intrinsic manifold; when the mapping departed from that structure, they never learned it. The researchers say the principle could be used to design BCIs that are easier to learn.