This technology is a brain-computer interface system for controlling motor commands that can generalize across different movements and contexts for an efficient brain-computer interface communication method.
Current brain-computer interfaces require supervised calibration for each individual while still having issues of low recognition accuracy. To communicate with an external prosthetic device, an individual must perform a series of supervised tasks to calibrate the motor commands. There are currently no brain-machine interfaces that can perform in a generalized manner without context or supervision.
This technology describes a method for a movement- and context-agnostic brain-computer interface that does not require supervised calibration. Using invariant dynamics and transition of neural activity across different movements, this interface can generate motor control commands in a generalizable manner. The motor commands can be learned without prior context and supervision to provide an alternative method for brain-computer interface communication.
This technology has been validated in animal models.
Patent Pending (WO/2024/196737)
IR CU23225
Licensing Contact: Kristin Neuman