Restoring both walking and sensation to patients with paraplegia is an ambitious goal—but a team of researchers from the Keck School of Medicine of USC , the University of California, Irvine (UCI) and the California Institute of Technology (Caltech) is now one step closer.
With $8 million in funding from the highly competitive National Science Foundation CyberPhysical Systems program, the team is building a fully implantable brain-computer interface (BCI) that allows patients to use their thoughts to control wearable robotic legs, known as a robotic exoskeleton. The system is designed to help patients walk while also restoring the sensation of walking. In the first full test, the BCI was about 92% accurate at both reading step signals from the brain and delivering an artificial walking sensation. The results were just published in the journal Brain Stimulation .
Other research groups are testing BCIs that help restore movement to paralyzed patients, such as brain implants that control robotic arms, but these are not yet commercially available. Most send signals in one direction—from brain to device—but the new system adds a feedback loop that allows the brain to feel what the body is doing. Unlike existing BCIs, the new system also aims to decode brain signals inside the implant, eliminating the need for external computers.
"What's really new here is that sensors on the skeleton also trigger stimulation of the brain, so the person can feel every step. The plan is for the technology to be fully implantable, so patients aren't tethered to a large external device," said Charles Liu, MD, PhD , one of the study's principal investigators, professor of clinical neurological surgery, urology and surgery at the Keck School of Medicine and director of the USC Neurorestoration Center .
In the system, electrodes are placed on brain's surface over the motor cortex—the region responsible for movement, specifically the part that controls the legs. A tiny computer decodes signals from that area to detect when the patient intends to step. Those signals then control a robotic exoskeleton the patient wears, triggering a step.
At the same time, the system sends signals to electrodes on the sensory cortex—the part of the brain that feels touch. This artificial stimulation, timed to the robot's movement, mimics the feeling of walking.
Existing brain-computer interfaces that restore walking send signals in just one direction, from brain to device. The team's early proof-of-concept study, done in a patient with epilepsy who had electrodes implanted as part of her medical care, shows it is possible to build a bidirectional, or two-way, system. This could someday give people with spinal cord injuries the chance to walk with more natural control.
"Paraplegic subjects using exoskeletons must currently rely on visual feedback, but this research provides a new avenue for more naturalistic and effective use of walking exoskeletons," said Richard A. Andersen, professor of neuroscience and director of the T&C Chen Brain-Machine Interface Center at Caltech, who is one of the project's principal investigators.
Turning thought into steps
Because the new technology involves brain surgery, testing it requires careful attention to patient safety and ethics. Liu and his team waited years for a patient with epilepsy who needed electrodes implanted in exactly the right locations. This allowed them to demonstrate the system's capabilities without any additional safety risk.
During the demonstration, the patient sat on her hospital bed with the device by her side (future versions will be small enough to implant inside the body), while one of the researchers wore the robot exoskeleton. When the patient mimed taking a step, the device signaled the exoskeleton, sending the researcher on a walk around the intensive care unit. The system correctly detected brain signals indicating the intent to walk about 92% of the time.
Next, the researcher walked out of the patient's line of sight, while she received artificial stimulation that mimicked walking sensations. She counted the researcher's steps with about 93% accuracy.
"These results are promising, especially given that this patient received no training. We expect the system to perform even better with practice," Liu said.
Restoring walking after paralysis
The demonstration helped the researchers earn an Investigational Device Exemption from the U.S. Food and Drug Administration, which allows them to test the device in a clinical trial for patients with paraplegia. They aim to implant electrodes for 30 days as a time, using that window to test and refine the system's capabilities.
The researchers will also continue improving the technology, including making its sensory feedback more sophisticated and miniaturizing the system so it can be fully implanted.
"This work represents an important feasibility step toward future fully implantable systems," said Zoran Nenadić, DSc, a professor of biomedical engineering at UCI and one of the project's principal investigators. "Our ultimate goal is to test the function of such a system on people with complete leg paralysis, demonstrating its potential to mimic the function of an intact sensorimotor loop."
In addition to Liu, Andersen and Nenadić, An Do, MD, and Payam Heydari, PhD, both of UCI, are also principal investigators. The same research team is also developing technology that combines brain-computer interfaces with stem cell therapy to help people recover abilities lost after brain damage.
About this research
In addition to Liu, Andersen, Nenadić, Do and Heydari, the study's other authors are Brian Lee, and Darrin Lee from the USC Neurorestoration Center, Keck School of Medicine of USC, University of Southern California and the Rancho Los Amigos National Rehabilitation Center, Downey, California; Angelica Nguyen, Hui Gong, Michelle Armacost and Susan J. Shaw from the Rancho Los Amigos National Rehabilitation Center, Downey, California; Jeffrey Lim, Po T. Wang, Shravan Thaploo, Won Joon Sohn and Derrick Lin from the University of California, Irvine; and Luke Bashford, David A. Bjanes and Spencer Kellis from the California Institute of Technology.
This work was supported by the National Science Foundation [grant number: 1646275].
