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Brain-Computer Interface
Brain-Computer Interface
Engineers recently reported that a brain-computer interface, using a form of artificial intelligence, can sense when its user is expecting a reward.
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Engineers at the University of Houston recently reported in the journal eNeuro that a brain-computer interface, using 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.

These findings allowed for the development of an autonomously updating brain-computer interface (or BCI) that improves on its own, learning about its subject without having to be programed.

The has applications for robotic prosthetics, which would sense what a user wants to do (pick up a glass, for example) and do it. The work represents a significant step toward prosthetics that perform more naturally. Why? Because the BCI quickly interprets what you’re going to do and what you expect as far as whether the outcome will be good or bad. That information drives scientists’ abilities to predict reward outcome to 97 percent, up from the mid-70s.

To understand the effects of reward on the brain’s primary motor cortex activity, the researchers 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.

They decode that information by an algorithm, and have it control either a computer cursor, or a robotic arm. Interestingly even when the task calls for no movement, other than passively observing an activity, the BCI is able to determine intention because the pattern of neural activity resembles that during movement.

This is important because real world applications are going to have to extract this information and brain activity out of people who cannot actually move, so this is one way of getting that information even if there is no movement. This process utilizes mirror neurons, which fire when action is taken, and action is observed.

This examination of reward motivation in the primary motor cortex could be useful in developing an autonomously updating brain-machine interface.

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