We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




Brain Wave-Reading Robot Could Help Stroke Patients

By HospiMedica International staff writers
Posted on 10 Sep 2012
Print article
Image: The researchers testing the MAHI-EXO II robotic rehabilitation device (Photo courtesy of Bruce French/TIRR Memorial Hermann).
Image: The researchers testing the MAHI-EXO II robotic rehabilitation device (Photo courtesy of Bruce French/TIRR Memorial Hermann).
An innovative device could help rehabilitate stroke survivors by turning their thoughts into actions, retraining motor pathways.

Researchers at Rice University (Rice, Houston, TX, USA), the University of Houston (UH, TX, USA), and UTHealth Motor Recovery Lab TIRR Memorial Hermann (Houston, TX, USA) are developing a noninvasive brain-machine interface (BMI) coupled to an exoskeleton robotic orthotic device that is expected to innovate upper-limb rehabilitation. Researchers at Rice are developing the exoskeleton, UH are developing the electroencephalograph-based (EEG) neural interface, and the combined device will be validated by physicians at TIRR Memorial Hermann.

The technology will first be used to translate brain waves from stroke survivors who have some ability to initiate movements, to prompt the robot into action. That will allow the researchers to refine the EEG-robot interface before moving to a clinical population of stroke patients with no residual upper-limb function. When set into motion, the intelligent exoskeleton will use thoughts to trigger repetitive motions and retrain the brain’s motor networks.

An earlier version of the MAHI-EXO II robot developed by researchers at Rice is already in validation trials to rehabilitate spinal-cord-injury patients at the at TIRR Memorial Hermann, and incorporates sophisticated feedback that allows the patient to work as hard as possible while gently assisting, and sometimes resisting, movement to build strength and accuracy.

“The capability to harness a user’s intent through the EEG neural interface to control robots makes it possible to fully engage the patient during rehabilitation,” said José Luis Contreras-Vidal, director of UH’s laboratory for Noninvasive BMI Systems and a professor of electrical and computer engineering. “Putting the patient directly in the ‘loop’ is expected to accelerate motor learning and improve motor performance. The EEG technology will also provide valuable real-time assessments of plasticity in brain networks due to the robot intervention – critical information for reverse engineering of the brain.”

“This is truly an outstanding opportunity to demonstrate how various technological advances can potentially boost traditional rehabilitation therapies,” said Gerardo Francisco, MD, chief medical officer of TIRR Memorial Hermann. “This project will be among the first to investigate the benefits of combined therapeutic interventions to help stroke survivors.”

Related Links:

Rice University
University of Houston
UTHealth Motor Recovery Lab TIRR Memorial Hermann


Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Portable Patient Lift
Maxi Move
New
Mammo 3D Performance Kits
Mammo 3D Performance Kits

Print article

Channels

Surgical Techniques

view channel
Image: Silicon-IC test structures prepared for long-term accelerated in vitro and in vivo aging (Photo courtesy of Nature Communications, DOI:10.1038/s41467-024-55298-4)

Novel Coating Extends Lifespan of Neural Implants

Neural implants play a vital role in studying the brain and developing treatments for conditions such as Parkinson's disease and clinical depression. These implants electrically stimulate, block, or record... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.