Engineers at the University of Texas at Austin have incorporated machine learning capabilities with their brain-computer interface, normally used to help with patient motor disabilities.
The subjects wear a cap with electrodes that gather data by measuring electrical signals from the brain, and the decoder interprets that information and translates it into game action.
Improving brain function
The experiments are designed to improve brain function for patients and use the devices controlled by brain-computer interfaces to make their lives easier.
The decoder worked well enough that subjects trained simultaneously for the bar game and the more complicated car racing game, which required thinking several steps ahead to make turns.
The research on the calibration-free interface is published in PNAS Nexus.
Improved clinical use
In a clinical setting, this technology will eliminate the need for a specialized team to do this calibration process, which is long and tedious, the researchers note.
This project used 18 subjects with no motor impairments. They plan to test this on people with motor impairments to apply it to larger groups in clinical settings.
The researchers have showed off another potential use of the technology: controlling two rehabilitation robots for hand and arm.
Citation: Kumar, S., Alawieh, H., Racz, F. S., Fakhreddine, R., & Millรกn, J. D. (2024). Transfer learning promotes acquisition of individual BCI skills. PNAS Nexus, 3(2). https://doi.org/10.1093/pnasnexus/pgae076 (open access)
