There is a report in eNeuro by an engineer at the University of Houston, suggests that a brain-computer interface, a form of artificial intelligence, can sense when its user is expecting a reward by examining the interactions between single-neuron activities and the information flowing to these neurons called the local field potential.
Joe Francis, Professor of biomedical engineering reports that the findings of his team allow for the development of an autonomously updating brain-computer interface (BCI) that improves on its own, learning about its subject without having to be programmed.
The results of the study potentially have applications for robotic prosthetics, which would sense what a user wants to do (for instance, pick up a glass) and do it. The work represents a significant step forward for prosthetics that perform more naturally.
Noting further, Francis said that this would help prosthetics work the way the user wants them to. The BCI quickly interprets what anyone is going to do and what the person expects as far as whether the outcome will be good or bad. Francis explained that information drives scientists' abilities to predict reward outcome to 97 percent, up from the mid-70s.
For him to have a grasp of the effect of reward on the brain's primary motor cortex activity, Francis used implanted electrodes to investigate brainwaves and spikes in brain activity while tasks were performed to see how interactions are modulated by conditioned reward expectations.
He said that they assumed the intention is in the brain, and they decode that information by an algorithm and have it control either a computer cursor, for instance, or a robotic arm. The exciting part is that even when the task called for no movement, just passively observing an activity, the BCI was able to determine intention because the pattern of neural activity resembled that during movement.
Francis noted further that this is essential because they are going to have to extract this information and brain activity out of people who cannot move, so this is their way of showing they can still get the information even if there is no movement. This process utilized mirror neurons, which fire when action is taken, and action is observed.
He concluded in his explanation that this examination of reward motivation in the primary motor cortex could be useful in developing an autonomously updating brain-machine interface.
