The team from the University of Houston has created an algorithm that allowed a man to grasp a bottle and other objects with a prosthetic hand that was powered only by his thoughts
The technique, demonstrated with a 56-year-old man whose right hand had been amputated, uses non-invasive brain monitoring, capturing brain activity to determine what parts of the brain are involved in grasping an object.
With that information, researchers created a computer program, or brain-machine interface (BMI), that harnessed the subject’s intentions and allowed him to successfully grasp objects, including a water bottle and a credit card. The subject grasped the selected objects 80 per cent of the time using a high-tech bionic hand fitted to the amputee’s stump.
Previous studies involving either surgically implanted electrodes or myoelectric control, which relies upon electrical signals from muscles in the arm, have shown similar success rates, according to the researchers.
Jose Luis Contreras-Vidal, a neuroscientist and engineer at UH, said the non-invasive method offers several advantages: It avoids the risks of surgically implanting electrodes by measuring brain activity via scalp electroencephalogram, or EEG. And myoelectric systems aren’t an option for all people, because they require that neural activity from muscles relevant to hand grasping remain intact.
The work, funded by the US National Science Foundation, demonstrates for the first time EEG-based BMI control of a multi-fingered prosthetic hand for grasping by an amputee. It also could lead to the development of better prosthetics, Contreras-Vidal said.
The results of the study were published March 30 in Frontiers in Neuroscience.
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