Neural interfaces will revolutionize disease care for patients of neurological conditions. Today, implantable neurostimulation (NS) devices have seen widespread adoption in the treatment of movement disorders, pain, and epilepsy, and have shown promise in treating psychiatric disorders, memory loss, depression, and more. Clinical neurostimulators have few channels and provide simple electrical pulses that are programmed by a doctor in a process that can take months or even years. There is a need for clinically viable, low-power and miniaturized systems that enable simultaneous stimulation and recording on many channels. Providing simultaneous sensing and closed-loop control will allow devices to automatically optimize parameters for a given outcome and only treat when symptoms are present, reducing side effects and reducing power. Closing the loop can provide dynamically delivered therapies that adjust electrical stimulation in response to a patient's real-time neural state. Furthermore, they will, for the first time, allow clinical researchers to understand the brain's response to stimulation and monitor changes (plasticity) over the long term. The future of medical care centers on wearable and implantable devices that will continuously monitor body functions and autonomously give treatment in an intelligent, closed-loop and well-controlled manner without the need for a doctor's intervention. This future requires broad, interdisciplinary engineering that combines electronics, artificial intelligence and biology. The multidisciplinary nature of this project extends to the long-term educational goals of promoting hands-on bioelectronic science and technology to the next generation of engineers.

This project develops miniaturized and highly integrated devices for closed-loop neuromodulation that combine high channel count neural recording with stimulation in a truly closed-loop manner for the first time. If successful, the proposed work will make significant advancements in two areas. First, low-power, low-noise integrated circuit design techniques will be developed to combine simultaneous stimulation and recording. Stimulation can significantly interfere with neural recording, resulting in large, persistent artifacts that mask or distort the neural signal and obscure reliable biomarker detection. Recording and stimulation circuits will be co-designed to eliminate this interaction. Second, online machine learning algorithms will be developed and integrated in hardware for dynamic closed-loop control. The emphasis will be to develop computationally efficient techniques that minimize the power consumption of the circuit. The two techniques will be combined into a single integrated circuit for multi-channel closed-loop neuromodulation that achieves a small footprint and ultra-low power dissipation that is safe for chronic use in humans. The device will be tested in animal models of disease.

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.

National Science Foundation (NSF)
Division of Electrical, Communications and Cyber Systems (ECCS)
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John Zhang
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University of California Berkeley
United States
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