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Brain-computer interface controls exoskeleton

18 August 2015

Scientists have developed a brain-computer control interface for a lower limb exoskeleton by decoding specific signals from within the user's brain.

A volunteer calibrates the brain-computer interface (photo: Korea University/TU Berlin)

Using an electroencephalogram (EEG) cap, the system, developed by Scientists working at Korea University, and TU Berlin, allows users to move forwards, turn left and right, sit and stand simply by staring at one of five flickering light emitting diodes (LEDs).

Each of the five LEDs flickers at a different frequency, and when the user focuses their attention on a specific LED, this frequency is reflected within the EEG readout. The corresponding signal is identified and used to control the exoskeleton.

A key problem has been separating these precise brain signals from those associated with other brain activity, and the highly artificial signals generated by the exoskeleton.

"Exoskeletons create lots of electrical 'noise'," says researcher, Klaus Muller. "The EEG signal gets buried under all this noise; but our system is able to separate not only the EEG signal, but the frequency of the flickering LED within this signal."

"People with amyotrophic lateral sclerosis [motor neuron disease], or high spinal cord injuries face difficulties communicating or using their limbs," says Muller. "Decoding what they intend from their brain signals could offer means to communicate and walk again."

The control system could serve as a technically simple and feasible add-on to other devices, with EEG caps and hardware now emerging on the consumer market.

It only took volunteers a few minutes to be trained to operate the system. The researchers are now working to reduce the 'visual fatigue' associated with longer-term users of such systems.

"Our study shows that this brain control interface can easily and intuitively control an exoskeleton system, despite the highly challenging artefacts from the exoskeleton itself," Muller adds.

The results of this work are published today (Tuesday, August 18) in The Journal of Neural Engineering.

YouTube video clip courtesy of Klaus Muller.


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