Chinese Academy of Sciences Team Reveals Efficient Neural Encoding for Brain-Computer Interfaces

On November 13, researchers at the Chinese Academy of Sciences' Institute of Automation reported that the brain can decode three-dimensional hand movement trajectories using only a small number of neurons. The findings, based on primate experiments, offer a neuroscience foundation for developing more efficient next-generation brain-computer interfaces (BCIs).

In the experiments, the team recorded neural activity while primates performed hand movement tasks. Data showed that both the primary motor cortex and the premotor cortex required only a few position-tuned cells to decode 3D trajectories, underscoring the brain's remarkably high encoding efficiency.

Position-tuned cells respond preferentially to specific hand positions. The study found that a sparse population of these cells carries enough information for accurate three-dimensional decoding, suggesting that BCIs could achieve high performance with fewer recording channels.

Yu Shan, a researcher at the Institute of Automation, said that the deeper the brain is understood, the better the performance of brain-computer interfaces can be. He noted that if future BCIs can directly extract movement trajectories from the brain, they could not only execute action plans more efficiently but also enable smoother control of external devices.

The research was recently published in the international academic journal Journal of Neuroscience, according to Xinhua. The team said the work deepens understanding of how the brain encodes movement and could guide the design of more efficient neural decoding algorithms, potentially reducing the number of electrodes needed in future implants and making BCIs less invasive and more practical.