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logLIRA: a method for reliable suppression of electrical stimulation artifacts enabling short-latency neural response recovery.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Benchmark analysis ↔ +benchmark/processSnippet.m, the whole file · a weak match · score 0.70 · cross correlation, zero lag, spike train, metric, basal, snippet
  2. [2] § Results ↔ +benchmark/processSnippet.m, the whole file · a weak match · score 0.69 · cross correlation, zero lag, spike trains, FN, FP, RMSE
  3. [3] § Methods › Synthetic dataset generation › Snippets synthesis ↔ +builder/buildSnippets.m, lines 102–211 · score 0.62 · iteratively removed, desired MAR, shifted, IAI, median, Snippets
  4. [4] § Methods › Synthetic dataset generation › Dictionaries of artifact templates ↔ +builder/extractTemplates.m, lines 115–144 · score 0.61 · accepted cluster, artifact template, lowpass, smoothing, stimulus artifacts, median
  5. [5] § Results ↔ +utils/+SD/SD_SWTTEO.m, lines 1–142 · score 0.54 · stationary wavelet, Teager, SWTTEO, operator, width, transform
  6. [6] § Methods › Synthetic dataset generation › Segments of basal activity ↔ +config/extractBasalChunksParams.m, lines 1–42 · score 0.52 · basal signal, movement, scraping, chunks, raw, MFR
  7. [7] § Methods › Benchmark analysis ↔ +config/extractBasalChunksParams.m, lines 1–42 · score 0.50 · basal signal, spike detection, SWTTEO, filtered, Benchmark

Paper

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The authors' code

MATLAB · 65 lines · 2.4 KB · no license · 2 matches

  1. function [metrics, blankedIntervals] = processSnippet(metrics, params)
  2. file = metrics.Properties.RowNames{:};
  3. file = fullfile(params.datasetPath, params.datasetSnippetsFolder, file);
  4. load(file, 'snippet');
  6. metrics = table2struct(metrics);
  8. metrics.timeScore = tic();
  9. [output, blankedIntervals] = params.algorithm(snippet);
  10. metrics.timeScore = toc(metrics.timeScore);
  12. [b, a] = butter( ...
  13. snippet.BPF.order / 2, ...
  14. [snippet.BPF.lowerCutoff, snippet.BPF.upperCutoff] / (snippet.sampleRate / 2), ...
  15. 'bandpass' ...
  16. );
  17. output = filtfilt(b, a, output);
  19. [spikeIdxs, ~, ~, ~] = snippet.SD.algorithm(output, snippet.SD.params);
  20. spikes = false(size(output));
  21. spikes(spikeIdxs) = true;
  23. %% Number of stimuli
  24. metrics.nStimuli = numel(snippet.stimuli.onset);
  26. %% Number of spikes
  27. basalSpikes = full(snippet.SD.spikeTrain);
  28. metrics.nSpikes = sum(basalSpikes);
  30. %% Root-Mean-Square Error (RMSE)
  31. blankingSamples = repmat(1:ceil(params.blanking * snippet.sampleRate), numel(snippet.stimuli.onset), 1);
  32. blankingSamples = blankingSamples + repmat(snippet.stimuli.onset', 1, size(blankingSamples, 2)) - 1;
  33. blankingSamples = reshape(blankingSamples', 1, []);
  34. error = output - snippet.basal;
  35. error(blankingSamples) = 0;
  36. metrics.RMSE = sqrt(sum(error .^ 2) / length(snippet.basal));
  38. %% Time score
  39. metrics.timeScore = metrics.timeScore / numel(snippet.stimuli.onset);
  41. %% False negatives (FN) and false positives (FP)
  42. unmatchedSpikes = int8(basalSpikes) - int8(spikes);
  44. jitter = round(params.jitter * snippet.sampleRate);
  45. unmatchedIdxs = find(unmatchedSpikes ~= 0);
  46. if ~isempty(unmatchedIdxs)
  47. idxsPre = find(diff(unmatchedIdxs) <= jitter);
  48. idxsPost = idxsPre + 1;
  49. idxsPre = idxsPre(unmatchedSpikes(unmatchedIdxs(idxsPre)) .* unmatchedSpikes(unmatchedIdxs(idxsPost)) == -1);
  50. idxsPost = idxsPre + 1;
  52. unmatchedSpikes(unmatchedIdxs(idxsPre)) = 0;
  53. unmatchedSpikes(unmatchedIdxs(idxsPost)) = 0;
  54. end
  56. metrics.FN = sum(unmatchedSpikes == 1);
  57. metrics.FP = sum(unmatchedSpikes == -1);
  59. %% Cross-correlation at zero-lag (C0)
  60. metrics.C0 = sum(unmatchedSpikes(basalSpikes == 1) == 0);
  61. metrics.C0 = metrics.C0 / sqrt(sum(basalSpikes) * sum(spikes));
  63. %%
  64. metrics = struct2table(metrics);
  65. end

processSnippet.m at commit 3e65906, no license · at the source

Overview

Authors: Francesco Negri1, David J Guggenmos2, Federico Barban1,3
ORCID iDs: Francesco Negri, David J Guggenmos, Federico Barban
  1. Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), Università degli Studi di Genova, Genova, Italy
  2. Department of Physical Medicine and Rehabilitation, University of Kansas Medical Center, Kansas City, KS, United States of America
  3. IRCCS Azienda Ospedaliera Metropolitana, Genova, Italy
Institutions: University of Genoa (Italy); University of Kansas Medical Center (United States); IRCCS Azienda Ospedaliera Metropolitana (Italy)
Journal: Journal of neural engineering, volume 23, issue 3, article 036008
Dates: received 1 August 2025; accepted 14 April 2026; published online 12 May 2026; in print 1 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1088/1741-2552/ae5f4b · PMID 41979289 · PMCID PMC13166083 · OpenAlex W7154255851
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), extracellular electrophysiology (units, LFP) (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, Statistics, Single-unit activity, calcium imaging
Keywords: ICMS, electrical stimulation, multielectrode array, artifact suppression, neural evoked activity
MeSH: Algorithms*, Artifacts*, Electric Stimulation*, Electroencephalography*, Evoked Potentials*, Neurons*, Reaction Time*, Animals, Humans (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministry of Universities and Research (PE0000006); National Institute of Neurological Disorders and Stroke (R01NS131466)
Citations: cited by 1 paper (Europe PMC); 61 references in the paper

Abstract

Objective. We propose logLIRA, a novel method for the rejection of intracortical microstimulation artifacts in electrophysiological recordings specialized in the recovery of short-latency evoked activity. Additionally, we introduce a comprehensive comparison framework to evaluate the performance of logLIRA against previously reported algorithms. Approach. Our method estimates the artifact profiles by means of a piece-wise linear interpolation between logarithmically distributed points. It handles signal saturation thanks to a dynamically adjusted blanking interval. Finally, it deals with residual secondary artifacts by clustering and common activity rejection. The artifact rejection proficiency of logLIRA is evaluated against other state-of-the-art algorithms by means of a semisynthetic dataset acting as ground truth and enabling the computation of key performance metrics. Main results. The benchmark analysis highlights that our new method outperforms its competitors, enabling a robust recovery of short-latency evoked activity and, at the same time, minimizing the likelihood of introducing false positives as a consequence of misclassified residual artifacts. The results hold for very heterogeneous artifact profiles (with or without signal saturation) and across different combinations of mean artifacts rate and mean firing rate in the semisynthetic dataset. Additionally, we show the functioning of logLIRA with real-world data, where its capability to handle secondary artifacts and avoid inflated evoked responses is particularly evident. Significance. This work provides the scientific community with a valuable and powerful tool to improve the recovery of evoked responses, potentially contributing to a better understanding of functional connectivity and brain plasticity mechanisms in in vivo neural circuits as well as the advancement of therapeutic electrical neuromodulation techniques.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

barbaLab/logLIRA

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: a73400d2e01989536dba143e7bec109a6845d589, 22 September 2024
Languages: MATLAB (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Code availability statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/barbaLab/logLIRA

barbaLab/logLIRA-benchmark

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3e65906d32b8c59680bda209520332e8d19f9946, 16 April 2024
Languages: MATLAB (39)
Size: 42 files, 39 scripts
Software Heritage: not archived
Found in: “Code availability statement”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (5 files), Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
40 files
At the source: github.com/barbaLab/logLIRA-benchmark

Code availability statement

The source code for logLIRA is publicly available at https://github.com/barbaLab/logLIRA. The Matlab scripts to generate a semisynthetic dataset from raw data and to run the benchmark analysis are available at https://github.com/barbaLab/logLIRA-benchmark.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 43 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability statement

The data that support the findings of this study will be openly available following an embargo at the following URL/DOI: https://doi.org/10.5281/zenodo.16 642 111 (https://doi.org/10.5281/zenodo.16642111) [61].

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → IOP Publishing
  • Funding: added Ministero dell'Università e della Ricerca: PE0000006; National Institute of Neurological Disorders and Stroke: R01NS131466

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 9 MeSH terms, 55 references.

Cite

This paper

Negri, F., Guggenmos, D. J., & Barban, F. (2026). logLIRA: a method for reliable suppression of electrical stimulation artifacts enabling short-latency neural response recovery. Journal of neural engineering, 23(3), 036008. https://doi.org/10.1088/1741-2552/ae5f4b

BibTeX

@article{negri2026loglira,
author = {Negri, Francesco and Guggenmos, David J and Barban, Federico},
title = {{logLIRA: a method for reliable suppression of electrical stimulation artifacts enabling short-latency neural response recovery}},
journal = {Journal of neural engineering},
year = {2026},
month = may,
volume = {23},
number = {3},
pages = {036008},
publisher = {IOP Publishing},
issn = {1741-2560},
doi = {10.1088/1741-2552/ae5f4b},
url = {https://doi.org/10.1088/1741-2552/ae5f4b},
pmid = {41979289},
pmcid = {PMC13166083}
}

RIS

TY - JOUR
AU - Negri, Francesco
AU - Guggenmos, David J
AU - Barban, Federico
TI - logLIRA: a method for reliable suppression of electrical stimulation artifacts enabling short-latency neural response recovery
T2 - Journal of neural engineering
J2 - J Neural Eng
PY - 2026
DA - 2026/05/12
VL - 23
IS - 3
SP - 036008
SN - 1741-2560
PB - IOP Publishing
DO - 10.1088/1741-2552/ae5f4b
UR - https://doi.org/10.1088/1741-2552/ae5f4b
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13166083",
"ISSN": "1741-2560",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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