Fully Differential Dynamic Neural Amplifier: Preventing Saturation from Artifacts and Breaking the Gain-Bandwidth Trade-Off
Neural recording is fundamental to advancements in neuroscience and the development of brain-computer interfaces. Central to this process is the neural amplifier, a critical component that directly influences the quality and fidelity of neural signal acquisition. The amplifier's performance det...
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Published in | Biomedical Circuits and Systems Conference pp. 1 - 5 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
24.10.2024
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Subjects | |
Online Access | Get full text |
ISSN | 2766-4465 |
DOI | 10.1109/BioCAS61083.2024.10798211 |
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Abstract | Neural recording is fundamental to advancements in neuroscience and the development of brain-computer interfaces. Central to this process is the neural amplifier, a critical component that directly influences the quality and fidelity of neural signal acquisition. The amplifier's performance determines the noise level contaminating neural signals and ensures adequate amplification for accurate analog-to-digital conversion (ADC). However, conventional neural amplifiers face significant challenges. They typically require a constant biasing current, leading to inefficient power consumption, and are limited by a constrained gain-bandwidth product due to power budget restrictions. This paper addresses these challenges by delving into the essential requirements for neural amplifiers and discussing the limitations of traditional designs. We introduce a dynamic amplifier that not only surpasses the conventional gain-bandwidth trade-off but also effectively mitigates saturation issues caused by stimulation or motion artifacts. Furthermore, the proposed amplifier exhibits significantly lower thermal and 1 / \mathrm{f} noise compared to traditional static amplifiers. Our simulation results demonstrate that the proposed neural amplifier consumes less than 200 nW of power, with a signal-to-noise-and-distortion ratio (SNDR) of 82.6 dB at a \mathrm{5 k H z} bandwidth. The amplifier also achieves an input-referred noise of 4.54 \mu V_{\text {rms }}. Additionally, the noise-efficient factor is 1.1, highlighting the amplifier's noise performance and its suitability for high-fidelity neural signal acquisition. |
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AbstractList | Neural recording is fundamental to advancements in neuroscience and the development of brain-computer interfaces. Central to this process is the neural amplifier, a critical component that directly influences the quality and fidelity of neural signal acquisition. The amplifier's performance determines the noise level contaminating neural signals and ensures adequate amplification for accurate analog-to-digital conversion (ADC). However, conventional neural amplifiers face significant challenges. They typically require a constant biasing current, leading to inefficient power consumption, and are limited by a constrained gain-bandwidth product due to power budget restrictions. This paper addresses these challenges by delving into the essential requirements for neural amplifiers and discussing the limitations of traditional designs. We introduce a dynamic amplifier that not only surpasses the conventional gain-bandwidth trade-off but also effectively mitigates saturation issues caused by stimulation or motion artifacts. Furthermore, the proposed amplifier exhibits significantly lower thermal and 1 / \mathrm{f} noise compared to traditional static amplifiers. Our simulation results demonstrate that the proposed neural amplifier consumes less than 200 nW of power, with a signal-to-noise-and-distortion ratio (SNDR) of 82.6 dB at a \mathrm{5 k H z} bandwidth. The amplifier also achieves an input-referred noise of 4.54 \mu V_{\text {rms }}. Additionally, the noise-efficient factor is 1.1, highlighting the amplifier's noise performance and its suitability for high-fidelity neural signal acquisition. |
Author | Amirsoleimani, Amirali Ma, Junyu Xu, Jianxiong Genov, Roman Cai, Hanfeng You, Hao |
Author_xml | – sequence: 1 givenname: Jianxiong surname: Xu fullname: Xu, Jianxiong email: jianxiong.xu@mail.utoronto.ca organization: University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada – sequence: 2 givenname: Hao surname: You fullname: You, Hao organization: University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada – sequence: 3 givenname: Hanfeng surname: Cai fullname: Cai, Hanfeng organization: University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada – sequence: 4 givenname: Junyu surname: Ma fullname: Ma, Junyu organization: University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada – sequence: 5 givenname: Amirali surname: Amirsoleimani fullname: Amirsoleimani, Amirali organization: University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada – sequence: 6 givenname: Roman surname: Genov fullname: Genov, Roman email: roman@eecg.utoronto.ca organization: University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada |
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Snippet | Neural recording is fundamental to advancements in neuroscience and the development of brain-computer interfaces. Central to this process is the neural... |
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SubjectTerms | dynamic amplifier Faces gain-bandwidth product input-referred noise MOS devices Motion artifacts Neural amplifiers Neuroscience Noise level Power demand Recording Signal to noise ratio Simulation stimulation artifacts Thermal noise |
Title | Fully Differential Dynamic Neural Amplifier: Preventing Saturation from Artifacts and Breaking the Gain-Bandwidth Trade-Off |
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