From sensor to inference: end-to-end chip design for wearable and implantable biomedical applications
J Van Assche, MF Carlino, MD Alea… - … Circuits and Systems …, 2023 - ieeexplore.ieee.org
Real time biomedical monitoring applications, such as epileptic seizure or cardiac arrest
detection, require low-latency sensing and processing at ultra-low-power budgets. In this …
detection, require low-latency sensing and processing at ultra-low-power budgets. In this …
Fascicle-Selective Ultrasound-Powered Bidirectional Wireless Peripheral Nerve Interface IC
This paper presents an innovative, minimally invasive, battery-free, wireless, peripheral
nervous system (PNS) neural interface, which seamlessly integrates a millimeter-scale …
nervous system (PNS) neural interface, which seamlessly integrates a millimeter-scale …
A Partially Feedback NSSAR Embedded Third-Order Delta–Sigma Modulator With Gain-Boosted Two-Stage FIAs
L Meng, J Chen, M Zhao, Z Tan - IEEE Journal of Solid-State …, 2024 - ieeexplore.ieee.org
This article presents a switched-capacitor (SC) delta–sigma modulator (DSM) for low-power
and high-precision applications. With a 5-bit noise-shaping (NS) successive-approximation …
and high-precision applications. With a 5-bit noise-shaping (NS) successive-approximation …
Closed-loop control of functional electrical stimulation using a selectively recording and bidirectional nerve cuff interface
YCE Hwang, L Long, J Sales Filho… - … on Neural Systems …, 2024 - ieeexplore.ieee.org
Discriminating recorded afferent neural information can provide sensory feedback for closed-
loop control of functional electrical stimulation, which restores movement to paralyzed limbs …
loop control of functional electrical stimulation, which restores movement to paralyzed limbs …
Artificially Intelligent Closed-Loop Neurostimulators: Trade-Offs between Local and Remote Computing
The current state of the art in electronic neuromodulation is closed-loop devices that employ
machine learning inference to perform automated diagnostics and treatment of neurological …
machine learning inference to perform automated diagnostics and treatment of neurological …
33.4 A Multi-Loop Neuromodulation Chipset Network with Frequency-Interleaving Front-End and Explainable AI for Memory Studies in Freely Behaving Monkeys
Y Hou, Y Zhu, X Wu, Y Li, T Lucas… - … Solid-State Circuits …, 2024 - ieeexplore.ieee.org
Alzheimer's disease (AD), a common cause of dementia, affects over 30 million people
worldwide and accounts for more than 1% of the global GDP [1]. Given that age is a …
worldwide and accounts for more than 1% of the global GDP [1]. Given that age is a …
Artificially-Intelligent Fascicle-Selective Bidirectional Peripheral Nerve Interfaces
This paper presents a comprehensive overview of our latest work on two design strategies
for minimally invasive, battery-free wireless neural interfaces for adaptive neuromodulation …
for minimally invasive, battery-free wireless neural interfaces for adaptive neuromodulation …
MEDSA: A Memristive-passive Delta-Sigma ADC Circuit for Detecting Neural Signals
H You, J Xu, A Amirsoleimani… - … Circuits and Systems …, 2023 - ieeexplore.ieee.org
In this study, we present an analog-to-digital converter (ADC) optimized for implantable
neural interfaces. The proposed ADC integrates a series of memristors in both the input and …
neural interfaces. The proposed ADC integrates a series of memristors in both the input and …
Advanced Noise-Shaping SAR ADCs Utilizing Single-Capacitor Arbitrary-Resolution DACs for Miniaturized Neural Interfaces
J Xu, S Wu, H You, J De Sales Filho… - … Circuits and Systems …, 2023 - ieeexplore.ieee.org
This paper presents the development and validation of a high-performance Noise-Shaping
(NS) Successive-Approximation-Register (SAR) Analog-to-Digital Converter (ADC) …
(NS) Successive-Approximation-Register (SAR) Analog-to-Digital Converter (ADC) …
Design Methodology for Energy-constrained AI Edge Inference in Implantable Medical Devices
J Sales Filho, J Xu, M Kanchwala… - … Circuits and Systems …, 2023 - ieeexplore.ieee.org
This paper provides design guidelines for enabling edge-computing machine learning
blocks in implantable medical devices. Energy consumption is a critical design factor for …
blocks in implantable medical devices. Energy consumption is a critical design factor for …