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An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces.

Omid Malekzadeh-Arasteh, Haoran Pu, Jeffrey Lim

    IEEE Transactions on Biomedical Circuits and Systems
    |January 7, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an energy-efficient electrocorticography (ECoG) architecture for brain-machine interfaces. A novel dual-mode signal processing method significantly reduces power consumption for neural feature extraction.

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    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Electrical Engineering

    Background:

    • Fully-implantable brain-machine interface (BMI) systems require energy-efficient components.
    • Electrocorticography (ECoG) signal acquisition is crucial for advanced BMI applications.
    • Existing systems face power consumption challenges, limiting long-term implantability.

    Purpose of the Study:

    • To present an energy-efficient ECoG array architecture for fully-implantable BMI systems.
    • To introduce a novel dual-mode analog signal processing method for efficient neural feature extraction.
    • To validate the performance of the proposed architecture through prototype fabrication and in-vivo testing.

    Main Methods:

    • Developed a dual-mode analog signal processing approach for early neural feature extraction in the high-gamma band (80-160 Hz).
    • Implemented a system with full-band mode for weight computation and base-band mode for feature extraction.
    • Fabricated a 32-channel ultra-low power signal acquisition front-end using a 180 nm CMOS process with a 0.8 V supply.

    Main Results:

    • Achieved 1.72x power reduction in analog blocks and up to 50x potential savings in digitization and processing.
    • The fabricated chip consumes 1.05 μW (0.205 μW for feature extraction) and occupies 0.245 mm² per channel.
    • Demonstrated superior performance with >76.5 dB CMRR, 4.09 NEF, and 10.04 PEF, validated by in-vivo human tests.

    Conclusions:

    • The proposed dual-mode ECoG architecture offers significant power savings for implantable BMI systems.
    • The novel signal processing method enables efficient extraction of high-gamma band neural features.
    • In-vivo results confirm the system's effectiveness and potential for clinical applications compared to commercial systems.