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\u2019s and other conditions involving disrupted brain signaling, and provide insights into ALS, where the brain generates signals that don\u2019t reach their destinations, Shrivastava said.\u00a0<\/p>\n
Scientists detect the brain\u2019s signals with electroencephalogram (EEG) technology developed by German psychiatrist Hans Berger<\/a> in 1924. It typically works by placing electrodes on the scalp to record waves of activity by large groups of cells.\u00a0<\/p>\n While EEG provides a useful snapshot, signals overlap and messages get blurred, Shrivastava explained. It\u2019s a bit like multiple people talking over one another on the same phone line.<\/p>\n Measuring under the scalp clears up the confusion by getting closer to the source.\u00a0<\/p>\n \u201cBy going a little bit below, you can actually improve the quality of the signal,\u201d Shrivastava said.\u00a0<\/p>\n Shrivastava\u2019s device will also record brain activity for much longer periods \u2014 days or even months at a time \u2014 compared to the typical 20 to 90 minutes<\/a> that a standard EEG test lasts.<\/p>\n This approach can expose long-term patterns that short recordings miss, Ziv Peremen, chief executive officer of X-trodes Ltd<\/a>, a company that develops wearable monitoring patches, explained. It can reveal \u201ctransitions into and out of abnormal activity, relationships to sleep and behavior, and early signatures that may precede clinical events,\u201d he said.<\/p>\n Beyond capturing individual episodes, continuous monitoring could also provide a more nuanced picture of brain functioning and demonstrate long-term effects of medications, Ivan Gligorijevi\u0107, chief executive officer and co-founder of mbraintrain.com<\/a>, a company that develops wearable EEG technology, added.<\/p>\n But tracking for that long is practical only if the device uses very little power \u2014 after all, you can\u2019t exactly plug a brain implant into the wall every night. Shrivastava\u2019s previous work<\/a> on low-power and self-powering chips laid the foundation for the new tech, which uses similar principles.<\/p>\n And then there\u2019s the problem of data. Piles of it that someone \u2014 or something \u2014 must analyze.<\/p>\n Raw EEG output can look like a jumbled mess, Shrivastava said. Alas, the brain doesn\u2019t highlight the useful bits or leave notes to help scientists make sense of it.<\/p>\n That is where machine learning, a form of AI, comes in.\u00a0<\/p>\n Built directly into the chip, it can break down each recording into attributes \u2014 individual characteristics of the signal, such as the height, speed or shape of an EEG wave. Next, it can link these features to any events \u2014 such as seizures \u2014 that happened during the recording.<\/p>\n The AI will then tag certain combinations of attributes as markers \u2014 telltale signatures of seizure activity that vary among people and neurological conditions. A series of unusually sharp, rapid waves, for example, could count as a marker.\u00a0<\/p>\n \u201cSeizures in different parts of the brain will manifest differently,\u201d Geoff Bobb, executive director of Epilepsy Toronto<\/a>, a nonprofit, told Northeastern Global News. Ones involving convulsions, unusual sensations or memory problems carry unique markers and require different treatments, he added.<\/p>\n The network within the chip will store the growing list of markers, refining the profile as more data comes in.\u00a0<\/p>\n This built-in analysis sets the new chip apart from existing continuous sub-scalp systems<\/a> that act largely as sensors. It can allow physicians to look for potential seizure triggers, Shrivastava said. For example, certain foods, lack of sleep, stress or missed medication might coincide with spikes in activity.<\/p>\n \u201cYou can analyze the data, identify disease patterns, identify \u2026 aspects of life that you can control,\u201d he explained.<\/p>\n \u201cIt is not an intervention,\u201d he clarified, comparing the device to a glucose tracker that regularly updates diabetes patients about their blood sugar.<\/p>\n \u201cSeizures \u2026 often follow individual rhythms over days to months,\u201d said Manfred Hartmann, senior research engineer from the Center for Health & Bioresources<\/a>, a research facility based in Vienna, Austria. Knowing a person\u2019s cycle may make it possible to estimate periods of high seizure risk and warn them ahead of time.\u201d<\/p>\n Detailed recordings can also \u201cshow whether someone is truly seizure-free,\u201d he added.<\/p>\n Once the chip works as intended, the prototype will be tested in rats or macaques at the University of Florida.<\/p>\n![\"Aatmesh](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/09\/031126_MM_Aatmesh_Shrivastava_008.jpg\")
After spending years figuring out how to make chips do more with less power, Northeastern electrical and computer engineering professor Aatmesh Shrivastava is applying that expertise to brain monitoring. Photo by Matthew Modoono\/Northeastern University<\/p>\n