Rice University and Baylor College of Medicine Join BrainGate Consortium for Paralysis Research
Rice University and Baylor College of Medicine have joined the BrainGate consortium to develop brain-computer interface technologies that help people with paralysis regain independence, according to a press release.
BrainGate is a collaboration of universities and medical centers working on implantable systems that link the brain with external devices. The new team in Texas will focus on decoding brain signals to control robotic assistive devices, enabling individuals with tetraplegia to perform tasks such as eating and drinking on their own.
At Rice, assistant professor Nishal Shah will lead the creation of computational infrastructure that translates neural activity into movement commands for robotic systems. At Baylor, Dr. Sameer Sheth will oversee clinical operations, including participant recruitment and surgical placement of electrode arrays.
The project aims to refine algorithms that convert complex brain activity into control signals for robotic arms or speech prostheses. It marks the beginning of a broader effort by Rice and Baylor to explore how brain-computer interfaces can also support research into cognitive and emotional processes related to mental health disorders.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Robotics Brief, Life AI Weekly or Daily AI Brief.
Also, consider following us on social media:
More from Robotics
Oct 16 Rareview and Tuttle Launch Humanoid Robotics ETF HOID Oct 16 Element to Manage Waymo's Toronto Fleet for 2028 Launch Oct 16 RoboParty launches open source RP1 humanoid robot Oct 16 Jodell Robotics Unveils Dexterous Hand and Tactile Sensors Oct 16 Saab Completes Sea Trials of Large Uncrewed Underwater VehicleMore from Life Sciences
Oct 16 Study Tests Smartphone AI for Children's Lung Sounds Oct 16 Manhattan Physician Invests in RunPractice Oct 16 Attilio appoints Buck Institute CEO Eric Verdin as cochairman Oct 16 Aignostics launches HETA Clinical for pathology workflows Oct 16 Specialty1 Partners Invests in AI for Dental Practice OperationsRobotics Brief
Weekly coverage of AI-driven robotics advances in industrial automation, autonomous vehicles, and robotic systems.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
The Biological Computing Co. Names Atish Banerjea Strategic Adviser
Linearis Joins Two Canadian AI Health Data Projects
Hammerhead AI and Columbia Study AI Factory Power Control
CIPRA.ai and Murata Explore AI Wearables for Chronic Care
SAS and NC State Launch AI Research Partnership
Daily AI Brief: the AI news that matters, in your inbox.