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 inBiomedical Circuits and Systems Conference pp. 1 - 5
Main Authors Xu, Jianxiong, You, Hao, Cai, Hanfeng, Ma, Junyu, Amirsoleimani, Amirali, Genov, Roman
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.10.2024
Subjects
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ISSN2766-4465
DOI10.1109/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.
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
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  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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