16/07/2023
AI is playing a significant role in brain-spine interfaces by enhancing their functionality and usability.
1. Signal processing: It process and interpret neural signals recorded from the brain or spinal cord, enabling the translation of these signals into meaningful commands or actions.
2. Machine learning: AI analyse patterns in neural data to recognize specific intentions or movements. And learn to predict and interpret neural signals more accurately.
3. Adaptive control: Algorithms can continuously adapt and optimize the control signals based on real-time feedback from the user and the environment. This adaptive control enables brain-spine interfaces to adjust to individual users' needs.
4. Data fusion: It integrate information from multiple sensors and modalities, such as neural recordings, electromyography, and proprioceptive feedback, to create a more comprehensive understanding of the user's intentions and movements. This fusion of data enhances the accuracy and robustness of brain-spine interfaces.
https://spectrum.ieee.org/brain-spine-interface …
The device decodes the brain’s signals and brings movement back to the legs