Combining EEG brain function data, brain-computer interface technology, and artificial intelligence, researchers have created a system that can generate an image of what a person is thinking.
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.
Researchers test machine learning algorithms to determine the metal workload and affective states of the human brain.
An implantable, wireless brain-computer interface device can help improve the functional independence of those living with paralysis, a new study reports.
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.
Three days of training with brain-computer interface technology reduced phantom limb pain. Patients reported a 30% reduction in pain after one session, and the effect lasted up to five days after training was complete.
Researchers provide the first evidence that the human brain replays waking experiences while we sleep.
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.