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The brain-computer interface (BCI) space continues to rise in notoriety, and a number of players are throwing their hats in the ring.

Such technologies could enable users to control a computer with their brain, or even go beyond that. Countless immobile people someday could control a mouse cursor, keyboard, mobile device/tablet, wheelchair or prosthetic device by only thinking.

Big names have already established their presence in the space. Elon Musk’s Neuralink continues to make headway, while Bill Gates-and Jeff Bezos-backed Synchron has an innovative catheter-delivered implant. Blackrock Neurotech, which has a next-generation BCI, has been implanting its Utah Array in patients since 2004.

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A speech prosthetic developed by a collaborative team of Duke neuroscientists, neurosurgeons, and engineers can translate a person’s brain signals into what they’re trying to say.

She gets a new cybernetic arm.

Watch this teen‘s wholesome reaction to getting her very first bionic arm.

During the company’s second quarter earnings call in July, Musk had said that combining the two could provide a cyber body that is incredibly capable.

An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.

Humans can gently slide a finger on the surface of an object and identify it by capturing both static pressure and high-frequency vibrations. Previous approaches to create artificial tactile sensors for sensing physical stimuli, such as pressure, have been limited in their ability to identify real-world objects upon touch, or they rely on multiple sensors. Creating a real-time artificial sensory system with high spatiotemporal resolution and sensitivity has been challenging.

Chuan Fei Guo and colleagues present a flexible slip sensor that mimics the features of a human fingerprint to enable the system to recognize small features on surface textures when touching or sliding the sensor across the surface. The authors integrated the sensor onto a prosthetic human hand and added machine learning to the system.

Link :- Watch a video of Karin using her new bionic hand: https://www.freethink.com/hard-tech/bionic-hand-phantom-limb

A new way of merging the body with a bionic hand provided a woman with more control over her prosthetic and less phantom limb pain.

A recent study published in the Proceedings of the National Academy of Sciences examines the use of Softbotics to mimic the movements of the ancient marine organism, pleurocystitid, which is estimated to have existed approximately 450 million years ago and is believed to be one of the first marine invertebrates to control their movements with a muscular stem. This study was led by researchers from Carnegie Mellon University and holds the potential to help scientists use a new field known as Paleobionics to better understand the evolutionary history of extinct organisms with paleontological evidence.

Image of a Pleurocystitid fossil (inset) and the pleurocystitid robot replica developed for the study. (Credit: Carnegie Mellon University College of Engineering)

“Softbotics is another approach to inform science using soft materials to construct flexible robot limbs and appendages,” said Dr. Carmel Majidi, who is a Professor of Mechanical Engineering at Carnegie Mellon University and lead author of the study. “Many fundamental principles of biology and nature can only fully be explained if we look back at the evolutionary timeline of how animals evolved. We are building robot analogues to study how locomotion has changed.”

Summary: A new speech prosthetic offers hope for those with speech-impairing neurological disorders.

By converting brain signals into speech using high-density sensors and machine learning, the technology represents a significant advancement over current slower communication aids.

Though still in early stages, the device has achieved a 40% accuracy in decoding spoken data during limited trials and is moving towards a cordless design.