Assessment Of The Spike Detection Performance Of The Wired-OR Readout Architecture

Master Thesis (2023)
Author(s)

Y.S. Landbrug (TU Delft - Mechanical Engineering)

Contributor(s)

Dante Muratore – Mentor (TU Delft - Bio-Electronics)

C Strydis – Graduation committee member (TU Delft - Computer Engineering)

Tiago L. Costa – Graduation committee member (TU Delft - Bio-Electronics)

Faculty
Mechanical Engineering
Copyright
© 2023 Yawende Landbrug
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Yawende Landbrug
Graduation Date
31-05-2023
Awarding Institution
Delft University of Technology
Programme
Biomedical Engineering
Faculty
Mechanical Engineering
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Abstract

Abstract—This paper investigates the performance of commonly used spike detection algorithms (Absolute Amplitude Thresholding and Non-linear Energy Operator) on compressed neural signals using a novel wired-OR lossy compression algorithm. Performing compression with the wired-OR architecture mainly removes the noisy baseline and preserves spikes in the neural signal. As a result, the spike detection sensitivity and accuracy improve or stay similar after compression. In addition, this paper proposes a new spike detection algorithm, the non-zero spike detector, that can be efficiently integrated into hardware with the wired-OR compression scheme. By using a firing rate-based approach to optimize the threshold in the non-zero spike detector, the proposed technique outperforms both Absolute Amplitude Thresholding and the Non-linear Energy Operator across different signal-to-noise ratios and firing rates. The wired-OR readout architecture, in combination with the non-zero spike detector, is a promising approach to achieve massive compression while preserving the neural signal and maintaining spike detection performance.

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