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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Related Experiment Video

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Single-unit In vivo Recordings from the Optic Chiasm of Rat
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A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Jiajia Wu, Abraham Akinin, Jonathan Somayajulu

    IEEE Transactions on Biomedical Circuits and Systems
    |February 26, 2024
    PubMed
    Summary
    This summary is machine-generated.

    A new 16-channel neural recording system-on-chip (SoC) enables precise, wide-band biopotential signal detection for wearable healthcare devices. It achieves ultra-low noise and high impedance across a broad frequency range, improving electrophysiology applications.

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    Area of Science:

    • Biomedical Engineering
    • Integrated Circuit Design
    • Electrophysiology

    Background:

    • Advances in brain-machine interfaces and wearable sensors require precise electrophysiology for diverse biopotential signals (mHz to kHz).
    • Current wearable solutions face limitations in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption.

    Purpose of the Study:

    • To present a novel 16-channel wide-band, ultra-low-noise neural recording system-on-chip (SoC) for chronic use in mobile healthcare.
    • To enable high-fidelity biopotential recordings across a wide frequency spectrum, from electrogastrogram (EGG) to electroneurogram (ENG).

    Main Methods:

    • Fabrication of a 16-channel SoC in 65nm CMOS technology.
    • Implementation of each channel using a delta-sigma analog-to-digital converter (ADC).
    • Inclusion of a sample-level duty-cycling (SLDC) mode to achieve a 0.001 Hz to 1 kHz bandwidth.

    Main Results:

    • Achieved 1.0 μVrms input-referred noise over a 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous mode.
    • In SLDC mode, maintained ultra-low noise (1.1 μVrms over 0.001Hz-1Hz) and achieved 435 MΩ input impedance.
    • Validated functionality with electroencephalogram (EEG) and electrogastrogram (EGG) recordings in human subjects.

    Conclusions:

    • The proposed SoC offers a significant advancement for wearable biopotential monitoring, overcoming limitations of existing technologies.
    • The wide bandwidth, ultra-low noise, and high input impedance make it suitable for chronic mobile healthcare applications, including brain and digestive monitoring.