Ultra-low-power ML brain implants to help manage Alzheimer’s and related disorders
Official title: SCH: Transforming Brain Disorder Management through Ultra-Low-Power ML Implants
Funded at Northeastern University · NIH-11579161 · Data updated 2026-11-12
A project to create a tiny, energy-saving brain-monitoring device with smart software that can help detect and manage Alzheimer’s-related conditions in real time.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Northeastern University NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | September 7, 2026 |
| Project end | July 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $313,321 |
| NIH project number | 1R01NS154768-01 |
| Project ID | NIH-11579161 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults diagnosed with Alzheimer’s disease or related dementias who may benefit from brain-monitoring approaches and wearable/neural interfaces.
✕Less likely to help
Individuals without Alzheimer’s or related brain disorders are unlikely to benefit from this device-focused approach.
What this research studies
If you or a loved one has Alzheimer’s or related dementia, this project aims to put a small sub-scalp device on your head that uses smart, ultra-low-power machine learning to interpret brain signals as they happen. The team will design new algorithms and a compact hardware system that can work with EEG data without draining battery life, making continuous monitoring more feasible. By addressing differences between individuals’ brain signals, the work seeks to provide reliable, real-time insights that could guide treatments or interventions. The project combines advances in machine learning, circuit design, and systems engineering to create a practical, patient-friendly solution. A proof-of-concept prototype will demonstrate feasibility in real-world conditions for potential clinical use.
Why it matters
Potential benefit: If successful, it could enable real-time monitoring and smarter interventions for people with Alzheimer’s-related conditions, potentially improving symptom management and outcomes.
How similar studies have gone: Previous work shows promise for on-device ML in neural sensing, but the specific ultra-low-power ssEEG and transient ML algorithms are novel.
Where this research is happening
-
Northeastern University
Boston, United States Active
Researchers
- Principal investigator: Shrivastava, Aatmesh — Northeastern University
- Study coordinator: Shrivastava, Aatmesh
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.