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A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in

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    Summary

    This study introduces a low-power Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) for brain-machine interfaces. The NeuRAD enables real-time neural feature extraction and behavior prediction, paving the way for fully implantable paralysis therapies.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Computer Engineering

    Background:

    • Intracortical brain-machine interfaces (BMIs) offer functional restoration for paralysis but are limited by high power consumption.
    • Existing BMIs, despite reduced power, still require external computation for neural data processing.
    • Bridging the gap to fully implantable devices necessitates ultra-low-power, integrated solutions.

    Purpose of the Study:

    • To develop and validate a novel application-specific integrated circuit (ASIC) for real-time neural recording and decoding.
    • To significantly reduce power consumption in BMI systems for portable and implantable applications.
    • To demonstrate the feasibility of using the developed ASIC for closed-loop control of motor functions.

    Main Methods:

    • Developed the Neural Recording And Decoding (NeuRAD) ASIC using 180 nm CMOS technology.
    • Integrated a hardware accelerator for Spiking Band Power (SBP) extraction and an M0 processor with a Matrix Acceleration Unit (MAU) for decoding.
    • Validated the ASIC's performance in a nonhuman primate model, recording SBP and predicting finger movements using a steady-state Kalman filter (SSKF).

    Main Results:

    • The NeuRAD ASIC achieved real-time extraction of neural spiking features and prediction of two-dimensional behaviors.
    • Controlling one-dimensional finger movements with NeuRAD predictions resulted in 100% success rate and 0.82 s acquisition time, consuming only 581 μW.
    • Predicting two-dimensional finger movements consumed 588 μW, enabling a 96% success rate and 2.4 s acquisition time.

    Conclusions:

    • The developed NeuRAD ASIC significantly reduces power consumption for neural decoding in brain-machine interfaces.
    • The ASIC's ability to perform real-time feature extraction and decoding is crucial for developing practical, fully implantable BMI systems.
    • This technology advances the potential for effective, implantable therapies for individuals with paralysis.