In the recent study, Stephen LaConte, an assistant professor at the Virginia Tech Carilion Research Institute and his colleagues used whole-brain, classifier-based real-time functional magnetic resonance imaging to understand the neural underpinnings of brain-computer interface control.
The research team asked two dozen subjects to control a visual interface by silently counting numbers at fast and slow rates. For half the tasks, the subjects were told to use their thoughts to control the movement of the needle on the device they were observing; for the other tasks, they simply watched the needle.
The scientists discovered a feedback effect that LaConte said he had long suspected existed but had found elusive: the subjects who were in control of the needle achieved a better whole-brain signal-to-noise ratio than those who simply watched the needle move.
The scientists also found that the act of controlling the computer-brain interface led to an increased classification accuracy, which corresponded with improvements in the whole-brain signal-to-noise ratio.
This enhanced signal-to-noise ratio, LaConte added, carries implications for brain rehabilitation.
The study has been published in the Proceedings of the National Academy of Sciences.
Source: ANI
