Technology with attitude

Model helicopter flown accurately using noninvasive BCI cap (video)



Researchers at the University of Minnesota have been able to control a model helicopter using noninvasive BCI cap to capture brain electrical activity. Although it’s not the first time we see helicopters being flown via brainwaves but as you can see on the video this latest research is truly pushing forward the potential of BCI technology.

In the lab of biomedical engineering professor Bin He, several young people have learned to use their thoughts to steer a flying robot around a gym, making it turn, rise, dip, and even sail through a ring.

“My entire career is to push for noninvasive 3-D brain-computer interfaces, or BCI,” says He, a faculty member in the College of Science and Engineering. “[Researchers elsewhere] have used a chip implanted into the brain’s motor cortex to drive movement of a cursor [across a screen] or a robotic arm. But here we have proof that a noninvasive BCI from a scalp EEG can do as well as an invasive chip.”

“We were the first to use both functional MRI and EEG imaging to map where in the brain neurons are activated when you imagine movements,” he says. “So now we know where the signals will come from.”

“This knowledge about what kinds of signals are generated by what kind of motion imagination helps us optimize the design of the system to control flying objects in real time,” He explains.

Monitoring electrical activity from the brain, the 64 scalp electrodes of the EEG cap report the signals (or lack of signals) they detect to a computer, which translates the pattern into an electronic command. Volunteers first learned to use thoughts to control the 1-D movement of a cursor on a screen, then 2-D cursor movements and 3-D control of a virtual helicopter. And now it’s the real deal, controlling an actual flying helicopter.

In the future the researchers want to apply the flying robot technology to help disabled patients interact with the world. A report on the technology has been published in the Journal of Neural Engineering.

Source: University of Minnesota