A team of scientists and researchers has developed a new wearable brain-machine interface (BMI) system that aims to improve the quality of life of people with paralysis, motor dysfunction or even those who are fully conscious but can't communicate or move. An international multi-institutional team of scientists and researchers led by Wan-Hong Yeo at the Georgia Institute of Technology created a device that combines wireless soft scalp electronics and virtual reality in a brain-machine interface system. The device enables users to imagine an action and control a robotic arm or wheelchair wirelessly. The team described the new motor imagery-based brain-machine interface system in a paper published in the journal Advanced Science on July 17. "The major advantage of this system to the user, compared to what currently exists, is that it is comfortable to wear, and doesn't have any wires," Yeo, who is an associate professor at George W. Woodruff School of Mechanical Engineering, said as per Science Daily. The team designed a portable EEG system that includes imperceptible microneedle electrodes with soft wireless circuits to enhance the acquisition of signals.
Somewhat unceremoniously, Facebook this week provided an update on its brain-computer interface project, preliminary plans for which it unveiled at its F8 developer conference in 2017. In a paper published in the journal Nature Communications, a team of scientists at the University of California, San Francisco backed by Facebook Reality Labs -- Facebook's Pittsburgh-based division devoted to augmented reality and virtual reality R&D -- described a prototypical system capable of reading and decoding study subjects' brain activity while they speak. It's impressive no matter how you slice it: The researchers managed to make out full, spoken words and phrases in real time. Study participants (who were prepping for epilepsy surgery) had a patch of electrodes placed on the surface of their brains, which employed a technique called electrocorticography (ECoG) -- the direct recording of electrical potentials associated with activity from the cerebral cortex -- to derive rich insights. A set of machine learning algorithms equipped with phonological speech models learned to decode specific speech sounds from the data and to distinguish between questions and responses.
A woman paralysed for 13 years can now walk again thanks to'brain training' programmes that restored feeling in her legs. The 32-year old woman is one of eight people with spinal cord injuries who have improved their condition from full paralysis to partial paralysis thanks to the software. The'surprising' clinical results from the Walk Again Project in Sao Paulo, Brazil, show patients have some sensations and muscle control in their legs. Scientists, led by neuroscientist Dr Miguel Nicolelis, of Duke University in North Carolina, used a virtual reality system which worked with the patients' own brain activity. The training period lasted 12 months.
A wearable that gives users telekinesis-like abilities aims to let disabled individuals lead more normal lives. The wireless brain-machine interface gives them the power to control an electric wheelchair, interact with a computer or operate a small robotic vehicle - just by using brain signals. This fully portable, wireless, flexible scalp electronic system has been redesigned to give people more freedom without having to wear the traditional bulky hair electrode cap. A wearable that gives users telekinesis-like abilities aims to let disabled individuals lead more normal lives. The redesigned wearable was developed in collaboration with researchers from Georgia Institute of Technology, University of Kent and Wichita State University.
A new method to accurately record brain activity at scale has been developed by researchers at the Crick, Stanford University and UCL. The technique could lead to new medical devices to help amputees, people with paralysis or people with neurological conditions such as motor neurone disease. The research in mice, published in Science Advances, developed an accurate and scalable method to record brain activity across large areas, including on the surface and in deeper regions simultaneously. Using the latest in electronics and engineering techniques, the new device combines silicon chip technology with super-slim microwires, up to 15-times thinner than a human hair. The wires are so thin they can be placed deep in the brain without causing significant damage.