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Hardware and Power-Efficient Compression Technique Based on Discrete Tchebichef Transform for Neural Recording

Sirous Farsiani, Amir M Sodagar

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
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    A novel compression technique using a modified discrete Tchebichef transform significantly reduces hardware complexity for neural implants. This method achieves high compression rates with minimal error, enabling efficient neural signal processing.

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Hardware Implementation

    Background:

    • On-implant hardware for neural recording requires low power and minimal silicon area.
    • Existing compression techniques may not meet these stringent hardware constraints.
    • High-density neural recordings generate large datasets, necessitating efficient compression.

    Purpose of the Study:

    • To introduce a new, hardware-efficient data compression technique for neural implants.
    • To modify and truncate the discrete Tchebichef transform for on-implant implementation.
    • To develop an algorithm for generating approximate transform matrices that preserve signal energy.

    Main Methods:

    • Modification and truncation of the discrete Tchebichef transform.
    • Development of an algorithm to generate approximate, orthogonal transform matrices.

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  • Hardware prototyping using standard digital hardware for neural signal compression.

    • Main Results:

    • A new truncated transformation matrix reduces hardware complexity by up to 74%.
    • Achieved a compression rate of 26.15 with a root-mean-square error of 1.1% on pre-recorded neural signals.
    • Successful hardware implementation demonstrating feasibility for neural signal processing.

    Conclusions:

    • The proposed technique offers a power- and area-efficient solution for neural signal compression in implants.
    • This advancement supports the development of sophisticated neuro-prostheses and brain-machine interfaces.
    • The method effectively balances compression efficiency with hardware implementation constraints.