Collaborative research: small: immersive multimodal plan-to-grasp neural decoders: integrating deep graph-based and adaptive shared control theories

Active Project Grant Awarded Last action

Award ID
2516236
Recipient
Awarding agency
Obligated
$69,745

Award facts

Assistance listing
47.070 Computer and Information Science and Engineering
Completion date 3% complete
Funding agency
Division of Computing and Communication Foundations National Science Foundation
Place of performance
North Dartmouth, Dartmouth, MA 02747, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
This project seeks to advance the science of brain-computer interfaces (BCIS), in the context of helping people with upper limb impairments use brain activity to control prosthetic arms to reach and grasp objects. Current control techniques have real limitations: they generally either require surgically implanted probes to capture brain activity or support a limited range of commands. To reduce these limitations, the project team will advance non-invasive methods for understanding and decoding plan-to-grasp signals in the brain that coordinate vision, movement planning, and hand control when reaching for objects. By combining these decoded plan-to-grasp signals with methods for shared human and AI control of robots, the project will develop new ways to control neuroprosthetic arms with a wider range of motion and abilities than existing methods. The project will also support education and workforce development through student training, K-12 outreach activities, sharing open-source data and software, and collaborations with community organizations focused on independent living and rehabilitation in rhode island. The project is structured around three main research activities. The first is to develop an immersive multimodal BCI framework that simultaneously records non-invasive brain activity, virtual video, and kinematic data during natural plan-to-grasp tasks in virtual reality. The second is to create large graph-based deep learning models to decode complex neural dynamics, classify grip types, and predict movement kinematics. The third is to implement a personalized real-time closed-loop virtual assistive arm-hand system and validate its performance on users with upper-limb impairment. Together, the work will lead to fundamental advances in neuroscience, advancing understanding of macroscale neural representations and dynamic cortical interaction mechanisms underlying active observation of the natural plan-to-grasp task using large graph-based nonlinear models and multidimensional spatial, temporal, and spectral features. There will also be significant advances around BCI-based prosthetic control, via combining these neurological signals insights with adaptive, personalized, and closed-loop shared control theories to support complex plan-to-grasp tasks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.

Quick answers

Who received award 2516236?

2516236 was awarded to University Of Massachusetts Dartmouth by Division of Information and Intelligent Systems (National Science Foundation) on October 1, 2026.

How much is award 2516236 worth?

$69,745 has been obligated on 2516236.

Which agency awarded 2516236?

Division of Information and Intelligent Systems (National Science Foundation) awarded 2516236, with funding from Division of Computing and Communication Foundations (National Science Foundation).

On GovTribe

See this award's full record

  • Every transaction and subaward
  • The recipient's other awards and teaming partners
  • Similar grant awards
  • Ask GovTribe AI about this award
Try for free Login