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Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform — arXiv:2608.19048

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19048
License (as archived): CC0-1.0
Archived: 2026-10-18T13:52:32.848Z
SHA-256 of the archived text: daad0ea0798a946ef08337d5998568458d759699d09486f1b546f9537d4458f9
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How to cite

Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform — arXiv:2608.19048 — arXiv (en), https://arxiv.org/abs/2608.19048 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_de9dca0cdf80 (Metraipolis archive dc_de9dca0cdf80, sha256 daad0ea0798a946ef08337d5998568458d759699d09486f1b546f9537d4458f9)

BibTeX

@misc{dcde9dca0cdf80,
  title = {Robust and Efficient Feature Extraction for Spike Sorting via the Walsh-Hadamard Transform — arXiv:2608.19048},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19048},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_de9dca0cdf80; sha256 daad0ea0798a946ef08337d5998568458d759699d09486f1b546f9537d4458f9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19048. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.HC).
Categorie arXiv: Signal Processing (eess.SP) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2608.19048 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:50:23.975Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).