University of Rochester researchers report low levels of electrical stimulation delivered to areas of the brain responsible for movement can instruct an appropriate response, replacing signals for sensory processing.
Neurons in the anterior intraparietal cortex learn to modulate their activity to compensate for errors in brain-machine interface tasks. Findings suggest the extent to which a person can learn a new skill is constrained by preexisting neural networks.
Researchers have identified a neurobiological basis for the processing and reactivation of spatial information in rodents.
A collaborative effort aims to provide a new method of communication for people with cerebral palsy.
A bilateral implant and brain-machine interface technology allowed a patient with a high spinal cord injury to control prosthetic arms with the power of thought.
Researchers have developed a method of fabricating nanoscale electronic scaffolds that can be injected. The scaffolds can be used to monitor neural activity or promote neurogenesis.
Researchers investigate advances in BCI and consider the implications of linking our brains up to technology.
A Neuron study reports brain machine interface technology may shed light on how mentally running through a routine improves performance.
Researchers report a patient has been able to use a mind-controlled prosthesis to move individual digits without extensive training.
Machine learning algorithm allows a brain-computer interface to readjust itself continually in the background to ensure the system is always calibrated and ready to use.
Researchers have developed a BMI that allows primates to control a robotic wheelchair with their thoughts alone.
Findings enhance understanding how the brain controls movement.
