University of Florida researchers have taken the concept of brain machine interfaces a step further, devising a way for computerized devices not only to translate brain signals into movement but also to evolve with the brain as it learns. This is a huge step forward to transhuman and technological singularity goals. The computer and the user co-evolve to learn to work together more effectively.
Until now, brain-machine interfaces have been designed as one-way conversations between the brain and a computer, with the brain doing all the talking and the computer following commands. The system UF engineers created actually allows the computer to have a say in that conversation, too, according to findings published this month online in the Institute of Electrical and Electronics Engineers journal IEEE Transactions on Biomedical Engineering.
“In the grand scheme of brain-machine interfaces, this is a complete paradigm change,” said Justin C. Sanchez, Ph.D., a UF assistant professor of pediatric neurology and the study’s lead author. “This idea opens up all kinds of possibilities for how we interact with devices. It’s not just about giving instructions but about those devices assisting us in a common goal. You know the goal, the computer knows the goal and you work together to solve the task.”
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.”
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ANOTHER DIFFERENT BRAIN IMPLANT
The other brain implant advance is described in an IEEE Spectrum June 2008 article. Michigan engineers are developing a closed-loop deep-brain stimulation device for Parkinson’s disease that would listen to the brain while stimulating it.
The group is also trying to make the system more energy efficient. They claim to have already reduced the power consumption and size compared with other stimulators, characteristics that would translate into huge benefits for a clinical model.