Aatmesh Shrivastava, an Indian American professor at Northeastern University, has been awarded a $1.2M grant from the National Institute of Neurological Disorders and Stroke for “Transforming Brain Disorder Management through Ultra-Low-Power ML Implants.”
Low-power chips being developed by electrical and computer engineering professor Shrivastava’s lab in College of Engineering laid the groundwork for implants that can continuously track and analyze brain activity.
The implant that uses machine learning to analyze brain signals in real time, could offer a deeper, longer look at faulty brain activity. With epilepsy as one test case, the goal is to uncover patterns that brief EEG tests miss.
The brain never clocks out. Whether you’re chatting with a friend, solving an algebra problem, reading a blog post or taking a nap, it’s buzzing with electrical activity. Nerve cells communicate this way, firing up to 1,000 signals every second.
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Shrivastava’s chip being developed with support from the National Institutes of Health aims to keep tabs on this activity as it unfolds. The chip tracks electrical signals under the scalp and uses AI to analyze the data and pick out meaningful patterns in real time.
Eventually, the device could detect signatures of neurological disorders such as epilepsy, which involves uncontrolled bursts of activity by groups of brain cells firing together.
It could also help monitor Alzheimer’s and other conditions involving disrupted brain signaling, and provide insights into ALS, where the brain generates signals that don’t reach their destinations, Shrivastava said.
Scientists detect the brain’s signals with electroencephalogram (EEG) technology developed by German psychiatrist Hans Berger in 1924. It typically works by placing electrodes on the scalp to record waves of activity by large groups of cells.
While EEG provides a useful snapshot, signals overlap and messages get blurred, Shrivastava explained. It’s a bit like multiple people talking over one another on the same phone line.
Measuring under the scalp clears up the confusion by getting closer to the source. “By going a little bit below, you can actually improve the quality of the signal,” Shrivastava said.
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Shrivastava’s device will also record brain activity for much longer periods — days or even months at a time — compared to the typical 20 to 90 minutes that a standard EEG test lasts.
This approach can expose long-term patterns that short recordings miss. Beyond capturing individual episodes, continuous monitoring could also provide a more nuanced picture of brain functioning and demonstrate long-term effects of medications.
But tracking for that long is practical only if the device uses very little power — after all, you can’t exactly plug a brain implant into the wall every night. Shrivastava’s previous work on low-power and self-powering chips laid the foundation for the new tech, which uses similar principles.
With a BS in Electronics and Communications Engineering from Birla Institute of Technology, India, he earned a PhD in Electrical Engineering from University of Virginia, Charlottesville.