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    Area of Science:

    • Biomedical Engineering
    • Wearable Technology
    • Signal Processing

    Background:

    • Biopotential signals vary widely in bandwidth, necessitating adaptable recording systems.
    • Existing biopotential recorders often lack dynamic tuning capabilities for bandwidth and data rates, impacting efficiency.
    • Efficient power consumption is critical for long-term, implantable, or wearable monitoring devices.

    Purpose of the Study:

    • To develop a miniaturized, low-power, batteryless, wireless biopotential recorder with dynamic bandwidth and data rate update capabilities.
    • To enable efficient operation by adapting to the varying bandwidth requirements of different biopotential signals.
    • To validate the performance of the proposed system through chip verification and in-vivo measurements.

    Main Methods:

    • Implementation of an automated tuning approach for analog front-end bandwidth (0.25-10 kHz) and transmitter data rate (10.5-420 kbps).
    • Design of a System-on-Chip (SoC) with nominal power consumption of 32 µW at 21 kbps.
    • Adaptive adjustment of sensing bandwidths and data rates for multi-band sensing.
    • Chip verification and in-vivo rodent electrocardiogram (ECG) measurements.

    Main Results:

    • Demonstrated dynamic bandwidth tuning from 0.25 kHz to 10 kHz.
    • Achieved data rate adaptability from 10.5 kbps to 420 kbps.
    • Validated low power consumption (32 µW at 21 kbps) and effective in-vivo ECG recording.
    • Showcased multi-band sensing capability for signals of interest that change over time.

    Conclusions:

    • The developed biopotential recorder offers efficient, adaptive, and low-power operation for diverse biopotential signal acquisition.
    • Dynamic tuning of bandwidth and data rate enhances energy efficiency and enables selective neural signal capture (e.g., LFPs and APs).
    • The system's adaptability is crucial for advanced neural recording applications, facilitating high-fidelity data capture with minimal power usage.