Researchers at The University of Texas at Austin have developed a mind-controlled wheelchair that translates the thoughts of a paralyzed person into movement. It was tested on three people with tetraplegia, the inability to move their arms and legs due to spinal injuries, all of whom operated the wheelchair in a natural environment to varying degrees of success.
The interface recorded their brain activity, and then a machine-learning algorithm translated that data into commands that drove the wheelchair. The system consists of a cap covered with electrodes that recorded brain electrical activity, while an amplifying device sent those electrical signals to a computer that interpreted each participant’s intentions and translated them into movement.
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We demonstrated that the people who will actually be the end users of these types of devices are able to navigate in a natural environment with the assistance of a brain-machine interface. It works a lot like riding a horse. The rider can tell the horse to turn left or to go into a gate. But the horse will ultimately have to figure out the optimal way to carry out those commands,” said José del R. Millán, professor in the Cockrell School of Engineering’s Chandra Family Department of Electrical and Computer Engineering.
