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.
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.
Thin, flexible, next-generation of brain implants include more than a thousand electrodes and can survive for more than six years.
The hand knob area of the premotor cortex operates across a wide range of motor functions and body areas.
Researchers have developed a new method to record brain activity at scale. The new technique could help in the development of new neuroprosthetic devices to help amputees and those with movement restricting neurological conditions.
Using artificial intelligence and brain-computer interface technology, researchers reconstructed English words from neural signals recorded from the brains of non-human primates.
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.
Using ECoG and machine learning, researchers decoded spoken words and phrases in real-time from brain signals that control speech. The technology could eventually be used to help those who have lost vocal control to regain their voice.
A new brain-machine interface technology can enhance emotional interaction with music.
Using EEG and brain computer interface technology, researchers have created a robotic arm that can be controlled without brain implants.
Findings allow for the development of an autonomously updating brain-machine interface, which is able to improve on its own by learning about its subject without additional programming. The system could help develop new robotic prosthetics, which can perform more naturally.
Learning a new skill may involve the generation of new neural activity patterns.
