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Electrophysiological Indices of Hierarchical Speech Processing Differentially Reflect the Comprehension of Speech in Noise.

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] § Materials and Methods › Data acquisition and preprocessing ↔ Code/sn_preprocess.m, lines 38–127 · score 0.88 · eye artifacts, PREP pipeline, EEGLAB, detrended, iterative, mastoid
  2. [2] § Results › Lexical surprisal’s influence on early auditory encoding decreases at high noise levels ↔ Code/backwardsModel_dataPrep.m, the whole file · a weak match · score 0.73 · stage regression, backward modeling, word reconstruction accuracy, speech envelope, resolvability, Spearman
  3. [3] § Materials and Methods › Assessing the role of lexical context on acoustic encoding in different levels of background noise ↔ Code/backwardsModel_dataPrep.m, the whole file · a weak match · score 0.67 · standard deviation, word reconstruction accuracy, speech envelope, Broderick, regressors, onset
  4. [4] § Materials and Methods › Speech stimulus characterization › Acoustic onsets and spectrogram ↔ Code/sn_fw_mod_indivFeat.m, lines 40–91 · score 0.66 · half wave rectifying, Acoustic onsets, derivative, spectrogram, envelope
  5. [5] § Materials and Methods › Modeling the relationship between speech features and EEG responses ↔ Code/sn_bw_mod_env.m, lines 1–35 · score 0.62 · cross validation, nested leave, lagged, 100 ms, stimuli, EEG
  6. [6] § Materials and Methods › Speech stimulus characterization › Envelope ↔ Code/sn_preprocess.m, lines 1–36 · score 0.61 · passband attenuation, stopband attenuation, filtered, speech
  7. [7] § Materials and Methods › Modeling the relationship between speech features and EEG responses ↔ Code/sn_bw_mod_env.m, lines 1–35 · score 0.54 · Cross validation, nested, lagged, 300 ms, backward, 100 ms

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 127 lines · 4.7 KB · no license · 2 matches

  1. %% Shyanthony Synigal 2023
  2. % Speech in noise study
  3. % Dependencies: eeglab, NoiseTools (2018), PrepPipeline
  5. % 70 one min long trials, Sampled at 1024 Hz
  6. % remember that the audio has 1 sec of zeros before and after it
  8. study_dir = pwd; % wherever you saved the study data
  10. eeg_dir = [study_dir '/Raw EEG/'];
  11. save_dir =[study_dir '/EEG/']; %
  14. nsubs = 25;
  15. nchans = 128;
  16. ntrials = 70;
  17. load('chanlocs.mat')
  20. % Filters
  21. fs_new = 128;
  22. fs = 1024; % Sampling frequency (Hz) of EEG
  23. apass = 1; % Passband attenuation (dB)
  24. fpass2 = 8; % Upper passband frequency (Hz)
  25. fstop2 = 8.5; % Upper stopband frequency (Hz)
  26. astop2 = 80; % Stopband attenuation (dB)
  28. h = fdesign.lowpass(fpass2,fstop2,apass,astop2,fs);
  29. lpf = design(h,'cheby2','MatchExactly','stopband'); clear h
  32. %for data trimming
  33. aud_len = 60.1; % audio length in sec
  34. startsamp = fs+1; % an extra second was added to start, so we're trimming it here
  35. endsamp128 = ceil(aud_len*fs_new); % how long in samples should it be.
  36. endsamp = endsamp128*(fs/fs_new); % did it this way so that the result would be an integer
  38. %%
  40. for s = 1:nsubs
  42. for trial = 1:ntrials
  44. load([eeg_dir 'Sub ' int2str(s) '/sub' int2str(s) '_' int2str(trial) '.mat'],'eegData'); %chan x time
  45. EEGstruct = create_eegstruct(eegData',fs,chanlocs_opt); % input data must be time x chan - all 130 ch
  48. % Prep pipeline
  50. disp('Detrend') %- mastoids too
  51. [signal,~] = removeTrend(EEGstruct);
  54. disp('line noise removal') %- mastoids too
  55. lineNoiseIn = struct('lineNoiseChannels', 1:130, 'lineFrequencies', [60, 120, 180, 212, 240],'Fs',fs,...
  56. 'p',0.01,'fScanBandWidth',2,'taperBandWidth',2,'taperWindowSize',4,'taperWindowStep',1,'tau',100,...
  57. 'pad',0,'fPassBand',[0 fs/2],'maximumIterations',10);
  58. [signal,~] = cleanLineNoise(signal,lineNoiseIn);
  61. disp('Robust reref and bad channel removal') %- don't include the mastoids
  62. referenceIn = struct('referenceChannels', [1:nchans],'evaluationChannels', [1:nchans],...
  63. 'rereference', [1:nchans],'channelInformation',signal.chanlocs);
  64. [signal,rereferenceOut] = performReference(signal,referenceIn);
  66. badchans_all{trial} = rereferenceOut.badChannels.all;
  67. data_prep = signal.data; %chn x time
  70. disp('LPF')
  71. data_cln = filtfilthd(lpf,data_prep');
  73. data_trm = zeros(endsamp,size(data_cln,2)); %time x chan
  75. if (fs+endsamp) > length(data_cln) %because trial 70 is short for first few subs
  76. len = length(data_cln(startsamp:end,:));
  77. data_trm(1:len,:) = data_cln(startsamp:end,:); %remove the edges
  78. else
  79. data_trm(1:endsamp,:) = data_cln(startsamp:fs+endsamp,:); %remove the edges
  80. end
  82. eegAll(:,:,trial) = data_trm(:,1:nchans);
  83. mastAll(:,:,trial) = data_trm(:,129:130);
  85. clear eegData data_trm data_prep EEGstruct data_cln signal lineNoiseIn rereferenceOut rereferenceIn
  86. end
  89. % ICA
  91. EEGstruct = create_eegstruct(eegAll,fs,chanlocs_opt(1,1:nchans)); % input data must be time x chan
  93. disp('Runing ICA')
  94. EEGstruct = pop_runica(EEGstruct, 'icatype', 'picard'); %,'interrupt','off'); 'runica', 'extended',1
  95. EEGstruct = pop_iclabel(EEGstruct, 'default');
  96. EEGstruct = pop_icflag(EEGstruct, [NaN NaN;0.9 1;0.5 1;NaN NaN;NaN NaN;NaN NaN;NaN NaN]); %for muscle and eye artifacts
  98. rem_comp = find(EEGstruct.reject.gcompreject);
  99. EEGstruct = pop_subcomp(EEGstruct,rem_comp); %find(EEGstruct.reject.gcompreject),0);
  100. data_ica = double(EEGstruct.data); %data ica -chan x time x trials
  103. for trial = 1:ntrials
  105. disp(' Downsampling')
  106. data_b4 = eegAll(:,:,trial);
  107. data_trm2 = data_ica(:,:,trial)';
  108. mastoids = mastAll(:,:,trial); % is time x chan, so ica is ch x time
  110. EEGdat_b4 = downsample(data_b4,(fs/fs_new)); % without ICA
  111. EEGdat = downsample(data_trm2,(fs/fs_new)); % input must be time x chan
  112. mastoids = downsample(mastoids,(fs/fs_new)); % input must be time x chan
  115. bad_chans = badchans_all{trial};
  117. disp(['Saving trial ' int2str(trial)])
  119. % I have 1 in the name since the data was detrended at 1Hz
  121. save([save_dir 'Sub ' int2str(s) '/sub' int2str(s) '_' int2str(trial) '_1-' num2str(fpass2) 'Hz_prep_ica.mat'],'EEGdat','bad_chans',...
  122. 'rem_comp','fs_new','mastoids','EEGdat_b4'); %
  124. clear bad_chans EEGdat mastoids data_trm2
  125. end
  127. end

sn_preprocess.m, no license · at the source

Overview

Authors: Shyanthony R. Synigal1,2, Andrew J. Anderson1,2,3,4,5, Edmund C. Lalor1,2,6,7
ORCID iDs: Shyanthony R. Synigal, Andrew J. Anderson, Edmund C. Lalor
  1. Department of Neuroscience, University of Rochester, Rochester, New York 14642
  2. Del Monte Institute for Neuroscience, University of Rochester, Rochester, New York 14642
  3. Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226
  4. Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin 53226
  5. Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin 53226
  6. Department of Biomedical Engineering, University of Rochester, Rochester, New York 14627
  7. Center for Visual Science, University of Rochester, Rochester, New York 14627
Institutions: University of Rochester (United States); Medical College of Wisconsin (United States)
Journal: eNeuro, volume 13, issue 9, pages ENEURO.0069-26.2026
Dates: received 4 March 2026; accepted 14 July 2026; published online 8 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0069-26.2026 · PMID 42642328 · PMCID PMC13560466 · OpenAlex W4362585872
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2023.03.30.534927
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency, Preprocessing
Keywords: EEG, hierarchical, language, speech comprehension, speech in noise, temporal response function
MeSH: Brain*, Comprehension*, Noise*, Speech Perception*, Acoustic Stimulation, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Journal subjects: Research Article: New Research, Sensory and Motor Systems
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: University of Rochester Department of Biomedical Engineering; University of Rochester Del Monte Institute for Neuroscience; Del Monte Institute Pilot Grant; Simons Foundation Autism Research Initiative (675250)
Citations: not cited yet (Europe PMC); 101 references in the paper

Abstract

The past few years have seen an increase in the use of encoding models to explain neural responses to natural speech. The goal of these models is to characterize how the human brain converts acoustic energy into distinct linguistic representations that enable everyday speech comprehension. For example, researchers have shown that electroencephalography (EEG) data can be modeled in terms of acoustic features of speech, such as its amplitude envelope or spectrogram, linguistic features such as phonemes and phoneme probability, and higher-level linguistic features like context-based word predictability. However, it is unclear how reliably EEG indices of these speech feature representations reflect comprehension in different listening conditions. To address this, we recorded EEG from 25 neurotypical adults (nine males) who listened to segments of an audiobook in various levels of background noise. We modeled how their EEG responses reflected a range of acoustic and linguistic speech features and how this tracking varied with behavior across noise levels. EEG tracking of nearly all examined features showed SNR-dependent changes in unique variance explained, with the largest changes occurring for linguistic features. We hypothesized that only higher-level feature tracking would predict behavior but instead found that both high- and low-level features were associated with behavioral scores depending on the noise level. EEG markers of the influence of top–down, context-based prediction on bottom–up acoustic processing also correlated with behavior. These findings help characterize the relationship between brain and behavior by comprehensively linking hierarchical indices of neural speech processing to language comprehension metrics.

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

Repository

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

OSF a285v

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: MATLAB (7), R (1)
Size: 350 files, 8 scripts
Software Heritage: not checked
Found in: “Code accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), EEGLAB (1 file), emmeans (1 file), ICLabel (1 file), lmerTest (1 file), psych (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
8 files
At the source: doi.org/10.17605/OSF.IO/A285V

Code accessibility

All data and scripts are available at https://doi.org/10.17605/OSF.IO/A285V.

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

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 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

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Versions

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Version 3, 28 September 2026

  • Authors: added Shyanthony R. Synigal (0000-0002-7380-4435); Andrew J. Anderson (0000-0003-0316-9787); Edmund C. Lalor (0000-0002-2498-6631); removed Shyanthony R. Synigal; Andrew J. Anderson; Edmund C. Lalor

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 11 MeSH terms, 4 funders, 89 references.

Cite

This paper

Synigal, S. R., Anderson, A. J., & Lalor, E. C. (2026). Electrophysiological Indices of Hierarchical Speech Processing Differentially Reflect the Comprehension of Speech in Noise. eNeuro, 13(9), ENEURO.0069-26.2026. https://doi.org/10.1523/eneuro.0069-26.2026

BibTeX

@article{synigal2026electrophysiological,
author = {Synigal, Shyanthony R. and Anderson, Andrew J. and Lalor, Edmund C.},
title = {{Electrophysiological Indices of Hierarchical Speech Processing Differentially Reflect the Comprehension of Speech in Noise}},
journal = {eNeuro},
year = {2026},
month = sep,
volume = {13},
number = {9},
pages = {ENEURO.0069--26.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0069-26.2026},
url = {https://doi.org/10.1523/eneuro.0069-26.2026},
pmid = {42642328},
pmcid = {PMC13560466}
}

RIS

TY - JOUR
AU - Synigal, Shyanthony R.
AU - Anderson, Andrew J.
AU - Lalor, Edmund C.
TI - Electrophysiological Indices of Hierarchical Speech Processing Differentially Reflect the Comprehension of Speech in Noise
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/09/08
VL - 13
IS - 9
SP - ENEURO.0069
EP - 26.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0069-26.2026
UR - https://doi.org/10.1523/eneuro.0069-26.2026
LA - en
ER -

CSL-JSON

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"PMID": "42642328",
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