The devices are programmed with complex algorithms that interpret thoughts. But the algorithms, or code, used in current brain-machine interfaces don't adapt to change, Sanchez said.
"The status quo of brain-machine interfaces that are out there have static and fixed decoding algorithms, which assume a person thinks one way for all time," he said. "We learn throughout our lives and come into different scenarios, so you need to develop a paradigm that allows interaction and growth."
To create this type of brain-machine interface, Sanchez and his colleagues developed a system based on setting goals and giving rewards.
Fitted with tiny electrodes in their brains to capture signals for the computer to unravel, three rats were taught to move a robotic arm toward a target with just their thoughts. Each time they succeeded, the rats were rewarded with a drop of water.
The computer's goal, on the other hand, was to earn as many points as possible, Sanchez said. The closer a rat moved the arm to the target, the more points the computer received, giving it incentive to determine which brain signals lead to the most rewards, making the process more efficient for the rat. The researchers conducted several tests with the rats, requiring them to hit targets that were farther and farther away. Despite this increasing difficulty, the rats completed the tasks more efficiently over time and did so at a significantly higher rate than if they had just aimed correctly by chance, Sanchez said.
"We think this dialogue with a goal is how we can make these systems evolve over time," Sanchez said. "We want these devices to grow with the user. (Also) we want users to be able to experience new scenarios and be able to control the device."
Dawn Taylor, Ph.D., an assistant professor of biomedical engineering at Case Western Reserve University, said the results of the study add a new dimension to brain-mac
|Contact: April Frawley Birdwell|
University of Florida