# A flexible, low power, multi-channel, real-time BCI for seizure monitoring and Modulation

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $638,036

## Abstract

PROJECT SUMMARY
Implantable neuromodulation devices for seizure control have emerged as an important treatment for medically
refractory epilepsy, particularly for focal seizure disorders increasingly identified as distributed in networks not
amenable for surgical resection. Current devices, however, are not suited for monitoring and modulating a
brain network due to limitations in channel count, computational capabilities, programmability, and power
budget. Our long-term goal is to develop a programmable, low power embedded device which can be used for
the control of seizures through monitoring and modulating brain network activity. The overall objective of this
application, which is the next step towards attaining our long-term goal, is to develop a programmable
computational device to perform signal processing in real-time and monitor and modulate multiple brain
locations within a tight power budget. In conjunction with this hardware solution, we will implement an open
language and evaluate the hardware and software solutions through bench and animal studies. Our central
hypothesis, based on our preliminary work, is that instead of the current approach of performing feature
detection in an analog front-end or using a single application specific IC (ASIC) – which place hard constraints
on programmability – a programmable and low power digital design can be achieved with asynchronous (clock-
less) design. The rationale for the proposed research is that once the hardware and software solutions are
developed, we will be able to more readily advance the use of devices for seizure control and for research on
brain networks. We will achieve the objective of this program through three aims. In Aim 1 we will develop a
processor called neuro Stream for real-time processing of streaming multichannel data. Neuro Stream will
feature a reduced instruction set computer-V (RISC-V) microcontroller unit, processing elements for specific
signal processing tasks, and a vector engine. Neuro Stream will be designed using asynchronous techniques,
operate within a limited power budget, and support execution of a wide range of computational methods. In
Aim 2 we will implement open languages neuro Octave and neuropy. Neuro Octave will be a subset of Octave,
an open-source language. Neuropy will be a subset of Python. Neuro Octave and neuropy will allow users to
develop their own programs for neuro Stream using familiar, commonly used, languages. In Aim 3 we will
implement monitoring and control algorithms in neuro Stream and control seizures in a model of epilepsy. This
contribution will be significant because it will bring the development of epilepsy devices fully into the realm of
the network theory of epilepsy allowing developments in the modulation of networks to emerge and be applied
for the device-based control of seizures. The proposed research is innovative, in our opinion, because in
contrast to previous advances, we will develop a programmable, powerful, all-d...

## Key facts

- **NIH application ID:** 10855667
- **Project number:** 1R01NS136442-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Abhishek Bhattacharjee
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $638,036
- **Award type:** 1
- **Project period:** 2024-04-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10855667

## Citation

> US National Institutes of Health, RePORTER application 10855667, A flexible, low power, multi-channel, real-time BCI for seizure monitoring and Modulation (1R01NS136442-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10855667. Licensed CC0.

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