Washington, Dec 17 (IANS): Researchers at the University of Pittsburgh have enabled a paralysed woman to control a robotic arm with just her thoughts, offering a ray of hope to restoring natural arm and hand movements in people suffering upper limb paralysis.

Pittsburgh-based Jan Scheuermann, 55, who is paralysed from the neck down since 2003 due to a neuro-degenerative condition, has gone from giving "high fives" to the "thumbs-up" after increasing the manoeuvrability of the robotic arm from seven dimensions (7D) to 10 dimensions (10D).

The extra dimensions come from four hand movements -- finger abduction, a scoop, thumb extension and a pinch -- and have enabled Scheuermann to pick up, grasp and move a range of objects much more precisely than with the previous 7D control.

"10D control allowed her to interact with objects in different ways just as people use their hands to pick up objects depending on their shapes and what they intend to do with them," said the study's co-author Jennifer Collinger.

After her eligibility for a research study was confirmed in 2012, Scheuermann underwent surgery to be fitted with two quarter-inch electrode grids. Each was fitted with 96 tiny contact points in the regions of Scheuermann's brain that were responsible for right arm and hand movements.

After the electrode grids in Jan's brain were connected to a computer, creating a brain-machine interface (BMI), the 96 individual contact points picked up pulses of electricity that were fired between the neurons in Scheuermann's brain.

Computer algorithms were used to decode these firing signals and identify the patterns associated with a particular arm movement such as raising the arm or turning the wrist.

By simply thinking of controlling her arm movements, Scheuermann was able to make the robotic arm reach out to objects, as well as move it in a number of directions and flex and rotate the wrist.

It also enabled Scheuermann to "high five" the researchers and feed herself dark chocolate.

The researchers used a virtual reality computer programme to calibrate Jan's control over the robotic arm. They discovered that it is crucial to include virtual objects in this training period to allow reliable and real-time interaction with objects.

"We hope to repeat this level of control with additional participants and to make the system more robust, so that people who might benefit from it will one day be able to use brain-machine interfaces in daily life," Collinger noted.

The findings appeared in the Journal of Neural Engineering.