• Empirical multi-scale threshol...

*Result*: Empirical multi-scale thresholding for evoked neural activity denoising.

Title:
Empirical multi-scale thresholding for evoked neural activity denoising.
Authors:
Abbaspour H; Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL 35233, USA. Electronic address: habbaspour@uabmc.edu., Walker HC; Department of Neurology, University of Alabama at Birmingham, Birmingham, AL 35233, USA., Irwin ZT; Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
Source:
Journal of neuroscience methods [J Neurosci Methods] 2026 Feb; Vol. 426, pp. 110643. Date of Electronic Publication: 2025 Nov 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 7905558 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-678X (Electronic) Linking ISSN: 01650270 NLM ISO Abbreviation: J Neurosci Methods Subsets: MEDLINE
Imprint Name(s):
Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
MeSH Terms:
Evoked Potentials*/physiology , Signal Processing, Computer-Assisted* , Electrocorticography*/methods , Brain*/physiology, Signal-To-Noise Ratio ; Humans ; Reproducibility of Results
References:
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Brain Commun. 2025 Jan 21;7(1):fcaf006. (PMID: 39980742)
Grant Information:
R01 NS124563 United States NS NINDS NIH HHS; UH3 NS100553 United States NS NINDS NIH HHS
Contributed Indexing:
Keywords: Biomedical signal processing; Bootstrap resampling; Empirical noise estimation; Evoked potentials; Neural signal denoising; Wavelet thresholding
Entry Date(s):
Date Created: 20251201 Date Completed: 20251211 Latest Revision: 20251214
Update Code:
20260130
PubMed Central ID:
PMC12700562
DOI:
10.1016/j.jneumeth.2025.110643
PMID:
41325803
Database:
MEDLINE

*Further Information*

*Background: Evoked potentials (EPs) are responses elicited by stimulation of the nervous system that serve as key biomarkers for assessing neural function, connectivity, and pathophysiology. Reliable EP extraction is challenged by low signal amplitudes, unrelated neural activity, and background noise across overlapping frequency ranges.
New Method: This study presents a novel framework to estimate the noise distribution around EPs without relying on prior assumptions. The method uses a multi-scale bootstrap approach to statistically characterize noise and uncertainty, allowing separation of meaningful EP components from unrelated background activity. The core principle of the bootstrap is that the variance of resampled distributions empirically estimates variability, enabling noise characterization around the mean. By applying this strategy across multiple frequency bands, the method effectively captures dynamic neural variations and improves EP detection reliability.
Results: The method is evaluated using electrocorticographic (ECoG) recordings, including synthetic and real EPs. Quantitative analysis showed lower mean square error (MSE) between denoised and true EPs, indicating improved signal-to-noise ratio (SNR). Qualitative evaluation of real EPs demonstrated enhanced visualization and more accurate morphology recovery, with reduced false detections and preserved EP integrity.
Comparison With Existing Methods: Compared with conventional filtering techniques, the proposed method better adapts to non-stationary noise and dispersed EP energy while maintaining computational efficiency, ease of implementation, and adjustable confidence levels.
Conclusions: This approach offers improved EP detection and visualization in clinical and research contexts, particularly where recordings are time-limited or patient tolerance for extended sessions is low, supporting broader applications in neuroscience and neuro-engineering.
(Copyright © 2025 Elsevier B.V. All rights reserved.)*

*Declaration of Competing Interest The authors declare no conflicts of interest.*