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Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform

Emily L Yang, Liyuan Guo

2022enspike sortingbiomedicalfeature extractiondata processingneural interfaces

Abstract

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Implantable neural interfaces require low-power real-time signal processing to remain within strict thermal and bandwidth constraints, motivating lightweight feature extraction methods for on-chip spike sorting. This work presents the Walsh-Hadamard Transform (WHT) as a hardware-efficient feature extraction method for neural spike classification. WHT can be implemented using only adders, subtractors, and registers without coefficient memory. WHT performance is compared against the Compressed Hadamard Transform (CHT) and Principal Component Analysis (PCA), improving mean F1-scores from 55–60% to 70–75% on difficult high-noise datasets and from 90–95% to 95–99% on all other simulated datasets. In addition to improved classification performance, WHT demonstrates greater robustness to noise, downsampling, reduced training size, and distance metric selection, maintaining standard deviations typically below 5%, while CHT and PCA reach up to 10% under high-noise conditions.

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Cite This Work

@article{443c8941-8760-4545-b35d-9dad84f22b5c,
  title={Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform},
  author={Emily L Yang and Liyuan Guo},
  year={2022},
  language={en}
}
TY  - JOUR
TI  - Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform
AU  - Emily L Yang
AU  - Liyuan Guo
PY  - 2022
LA  - en
ER  -

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