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    Summary

    This study demonstrates a wireless communication system for prosthetic control using 4 implants and ultrasound. It achieved high data rates and improved accuracy with machine learning, advancing neural recording technology.

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    Area of Science:

    • Biomedical Engineering
    • Neural Engineering
    • Wireless Communication Systems

    Background:

    • Untethered, wireless peripheral nerve recording is crucial for advanced prosthetic control.
    • High data rate multi-implant communication is a significant challenge in this field.

    Purpose of the Study:

    • To develop and evaluate a multiple-access ultrasonic uplink data communication channel for multiple neural implants.
    • To achieve high data rates and improve communication reliability for prosthetic applications.

    Main Methods:

    • A system comprising 4 free-floating implants and a single external transducer was designed.
    • Code-Division Multiple Access (CDMA) was employed for multi-access communication.
    • A machine-learning assisted decoder was utilized to enhance Bit Error Rate (BER).

    Main Results:

    • An overall channel data rate of up to 784 kbps was achieved.
    • The machine-learning decoder improved BER by over 100 times.
    • This work represents the largest number of implants at the highest data rate and spectral efficiency reported.

    Conclusions:

    • The developed ultrasonic communication channel effectively supports high data rate, multi-implant communication for neural recording.
    • Machine learning significantly enhances the reliability of wireless neural data transmission.
    • This technology advances the potential for sophisticated, untethered prosthetic control systems.