Scientists create self-learning brain-machine interface for prosthetics

Brain machine interfaces allow those with artificial limbs to control them with nothing but their thoughts. They are, however, difficult to control and take patience and persistence to master.

But this could be about to change as researchers have come up an approach that may make the control of artificial limbs easier.

They have created a way for an artificial limb to store correct movements. When a patient is missing a limb and is using a brain-controlled prosthetic – which, officially comes under the discipline of neuroprosthetics – the brain sends out an Error-related potential (Errp); effectively an error signal.

Scientists from École polytechnique fédérale de Lausanne, Switzerland, have now used this signal to create new brain interfaces that they say can learn full movements.

“If we fail to grasp a glass of water placed in front of us, the neuroprosthesis will understand that the action was unsuccessful and the next movements will change accordingly until the desired result is achieved,” the institution says in a press release.

“The machine knows that the goal is reached when the actions performed no longer generate an ErrP.”

In essence it works by using trial and error to teach the system whether a movement has been successful or not.

“According to our expectations, this new approach will become a key element of the next generation brain-machine interfaces that mimic the natural motor control,” said lead researcher José Millán.

“The prosthesis can function even if it does not have clear information about the target.”

The study used 12 subjects, who were all asked to train their prosthesis to be able to detect the error signal.

They were then strapped into an electrode headset where the machine completed 350 separate movements. However, to teach the system when it was wrong, it was programmed to fail 20% of the time.

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Those being studied were then required to complete three experiments using their prosthetic arms. The final one of these involved them being asked to identify a target that was two meters away.

The researchers found that the artificial arm stores the correct movements and build up a range of movements.

The research was published in the Nature Scientific Reports journal.

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