The study by researchers at The University of Texas at Austin and published today in the journal iScience is an important step forward for brain-machine interfaces — computer systems that turn mind activity into action. The concept of a thought-powered wheelchair has been studied for years, but most projects have used non-disabled subjects or stimuli that leads the device to more or less control the person rather than the other way around.
In this case, three individuals with tetraplegia, the inability to move their arms and legs due to spinal injuries, operated the wheelchair in a cluttered, natural environment to varying degrees of success. The interface recorded their brain activity, and a machine-learning algorithm translated it into commands that drove the wheelchair.
The researchers said this is a sign of future commercial viability for mind-powered wheelchairs that can assist people with limited motor function.

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