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Adaptive Threshold Neural Spike Detector Using Stationary Wavelet Transform in CMOS.

Yuning Yang, C Sam Boling, Awais M Kamboh

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
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    This study presents a low-power digital spike detector using a lifting stationary wavelet transform. The adaptive system achieves high accuracy for neural recordings, reducing data bandwidth for implantable devices.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Neuroscience

    Background:

    • Spike detection is crucial for analyzing neural recordings.
    • Frontend detection reduces bandwidth for wireless multichannel data transfer.

    Purpose of the Study:

    • To develop a low-power digital integrated spike detector.
    • To enable adaptive, user-independent thresholding for neural data.

    Main Methods:

    • Utilized the lifting stationary wavelet transform for spike detection.
    • Implemented adaptive thresholding by monitoring wavelet coefficient standard deviation.
    • Designed and tested a prototype 16-channel detector on an FPGA.

    Main Results:

    • Achieved nearly 90% accuracy in spike detection, even at low signal-to-noise ratios (SNR=2).
    • The design is power-efficient, dissipating 1.7 μW per channel.
    • Occupies a small area (0.014 mm²) in 130 nm CMOS technology.

    Conclusions:

    • The developed spike detector is suitable for implantable multichannel neural recording systems.
    • Offers a low-power, high-accuracy solution for real-time neural data processing.