Researchers have developed a new brain-machine interface headset that harnesses the power of machine learning to allow those with disabilities to maneuver their wheelchair with thought alone.
Neural activity patterns for limb movements remain stable over time. Researchers were able to record, decode, and reconstruct activity patterns from common movement skills. The findings could have immediate implications for the development of neuroprosthetics that can bypass brain injuries by inferring intended motor actions from a person's brain.
Applying a nonlinear signal processing method to experimental data, a new study reveals a connection between motor behavior and brain activity. The findings could help with the development of new brain-computer interfaces and artificial intelligence technologies.
Researchers have created a hybrid neural network where biological and artificial neurons in different parts of the world were able to communicate via the internet through a hub of memristive synapses.
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 were able to restore the sense of touch to a 28-year-old who suffered a spinal cord injury with the help of new brain-computer interface technology.
Researchers reduced the power requirements of neural interfaces while improving their accuracy by tuning into a subset of brain waves.
A new machine-learning algorithm has uncovered new neural mechanisms and enhanced the decoding of behaviors directly from brain signaling data.
Alterations in internal states can influence how behavior improves with learning.