Turn Thoughts into Motion With New ‘Brain Cap’ Technology

by Tanya Thomas on  July 29, 2011 at 9:16 PM News on IT in Healthcare   - G J E 4
Turning your thoughts into motion may soon be possible with a new 'Brain Cap' technology that scientists have developed.
 Turn Thoughts into Motion With New ‘Brain Cap’ Technology
Turn Thoughts into Motion With New ‘Brain Cap’ Technology

Researchers from the University of Maryland have created a non-invasive, sensor-lined cap with neural interface software that soon could be used to control computers, robotic prosthetic limbs, motorized wheelchairs and even digital avatars.

"We are on track to develop, test and make available to the public- within the next few years - a safe, reliable, noninvasive brain computer interface that can bring life-changing technology to millions of people whose ability to move has been diminished due to paralysis, stroke or other injury or illness," said Associate Professor of Kinesiology Jos? 'Pepe' L. Contreras-Vidal of the university's School of Public Health.

The team demonstrated that people wearing the EEG brain cap could achieve performance levels comparable to those by subjects using invasive implanted electrode brain computer interface systems.

"We are doing something that few previously thought was possible," said Contreras-Vidal.

"We use EEG [electroencephalography] to non-invasively read brain waves and translate them into movement commands for computers and other devices."

The team successfully used EEG brain signals to reconstruct the complex 3D movements of the ankle, knee and hip joints during human treadmill walking.

The team's brain cap technology is being paired with a DARPA-funded next-generation robotic arm designed by John Hopkins Applied Physics Laboratory researchers.

Contreras-Vidal says the use of the device in stroke victims offers exciting possibilities.

"By decoding the motion of a normal gait," Contreras-Vidal says, "we can then try and teach stroke victims to think in certain ways and match their own EEG signals with the normal signals."

The study has been published in the Journal of Neural Engineering.

Source: ANI

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