It was only a matter of time before it became possible to control a drone with mere thoughts. In a gymnasium in Minneapolis, Minnesota, an AR.Drone quad-rotor helicopter made by French firm Parrot has been zooming right and left, up and down, and even through hoops as its pilot merely thinks of concepts related to such directions.
Bin He and colleagues at the University of Minnesota, who developed the rig, are not trying to use mind control to launch precision drone strikes. Instead, their aim is to demonstrate the power of the brain to move the machines that aid disabled people – whether those machines are exoskeletons,wheelchairs, or bionic prosthetic limbs.
Drones have been piloted with low-resolution, 14-electrode gaming electroencephalography (EEG) headsets before (see video above), but the Minnesota team are claiming a first in that they use an EEG headset with 64 electrodes peppered across the pilot’s scalp. The result is finer drone control than was previously possible. The team has demonstrated this control by navigating a series of obstacles.
Their trick was to come up with a very distinctive thought for each desired motion. For instance, to move the AR.Drone to the right, the pilot had to imagine making a fist with their right hand, while to move upwards they had to think of making fists with both hands. This provided a strong output from the motor cortex that could be sensed by the EEG, rather than vague thoughts of, for example, left and right motion.
The Minnesota team’s feat is yet more proof that EEG sensing is ready for prime time, claims Tre Azam, CEO of MyndPlay, a London-based company that is already using EEG technology to run thought-controlled multimedia experiences at movie premieres and consumer product launches.
“We keep hearing of interesting ventures like this drone project and how they can be applied to the future – but the reality is that the future is here,” Azam says.
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- Original article: http://www.newscientist.com/article/dn23652-steered-by-thoughts-drone-flies-through-hoops.html
- Journal reference: Journal of Neural Engineering DOI: 10.1088/1741-2560/10/4/046003