A research team from the University of Houston (UH) has created an algorithm that allowed a man to grasp objects with a prosthetic hand, powered only by his thoughts. The noninvasive technique, demonstrated with a 56-year-old man whose right hand had been amputated, captures brain activity to determine what parts of the brain are involved in grasping an object. With that information, researchers created a brain-machine interface (BMI) that harnessed the subject’s intentions and allowed him to successfully grasp the objects, including a water bottle and a credit card. The subject was able to grasp the selected objects 80 percent of the time while using a high-tech bionic hand fitted to his residual limb. The results of the study were published March 30 in Frontiers in Neuroscience, in the neuroprosthetics section.
Researchers first recorded brain activity and hand movement in five able-bodied volunteers as they picked up five objects, each chosen to illustrate a different type of grasp: a soda can, a compact disc, a credit card, a small coin, and a screwdriver. The recorded data were used to create decoders of neural activity into motor signals, which reconstructed the grasping movements. They then fitted the subject with a computer-controlled neuroprosthetic hand and told him to observe and imagine himself controlling the hand as it moved and grasped the objects. The subject’s EEG data, along with information about prosthetic hand movements gleaned from the able-bodied volunteers, were used to build the algorithm.
Previous studies involving either surgically implanted electrodes or myoelectric control have shown similar success rates, according to the researchers.
Professor José Luis Contreras-Vidal, PhD, a neuroscientist and engineer at UH, was one of three lead authors on the paper. He said the noninvasive method is advantageous as it avoids the risks associated with surgically implanting electrodes, 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 researchers said the work, which was funded by the National Science Foundation, demonstrated for the first time EEG-based BMI control of a multi-fingered prosthetic hand for grasping by an individual with an amputation. It could lead to the development of better prosthetic devices, Contreras-Vidal said. Beyond demonstrating that prosthetic control is possible using noninvasive EEG, researchers said the study offers a new understanding of the neuroscience of grasping and will be applicable to rehabilitation for other types of injuries, including stroke and spinal cord injury.
Contreras-Vidal said additional practice, along with refining the algorithm, could increase the success rate to 100 percent.
Editor’s note: This story was adapted from materials provided by UH.