Using virtual reality technology, researchers found the faulty alignment of prosthetics resulted in a measurable loss in performance for users.
Adding a BCI that evokes tactile sensations makes it easier for users to manipulate and use robotic arm prosthetics.
A newly developed robotic thumb imprints how the hand is represented in the brain. Using the robotic thumb, researchers reported improvements in conducting dexterity tasks, such as building with blocks. Additionally, those who trained to use the additional thumb began to feel as though the digit was a part of their body.
Reconnecting muscle pairs during surgery following amputation provides patients more sensory feedback from the limb, researchers report.
Sensory signals transmitted from a prosthetic leg to the nervous system helps amputees perceive their prosthetic limb as part of their body. The new neurofeedback helps them perceive the new limb as significantly lighter.
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A closed-loop system combining AI, robotics, and BCI technology allows a quadriplegic man to control movements to cut food and feed himself.
After a year of using a bionic arm, patients report subjective sensations did not shift to match the location of the touch sensor on their prosthetic device.
A newly developed simultaneous brain-machine interface allowed a quadriplegic man to control two prosthetic arms with the power of his mind.
Researchers have developed a new model that represents the planning of movement from seeing an object to grasping it.
The brain processes motor commands, not just through fine muscle contractions, but also via higher-level motor areas that provide a blueprint for performing more complex motor functions, such as grasping, no matter if the toes or fingers are used.
Combining machine learning with neuroprosthetic technology allowed a patient with paralysis to learn to control a computer cursor by utilizing brain activity without extensive daily retraining.
A new wireless intracortical brain-computer interface neuroprosthesis is capable of gathering and transmitting accurate neural signals, using a tenth of the power required by current wire-enabled systems.
