Beyond neural oscillations: Stress-related aperiodic activity and aperiodic-oscillatory spectral covariation.

Code ↔ Paper

3 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 3 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § STAR★Methods › Method details › Electroencephalography acquisition, preprocessing, spectral decomposition ↔ Scripts/aperiodic_preprocessing.py, lines 66–120 · score 0.98 · peak width limit, 12–30 Hz, 2–40 Hz, 8–12 Hz, peak height, power spectral density
[2] § STAR★Methods › Quantification and statistical analysis ↔ Scripts/Stress.R, the whole file · a weak match · score 0.53 · perceived stress, Gaussian, identity, family, pairwise, GAM
[3] § STAR★Methods › Quantification and statistical analysis ↔ Scripts/Covariation.R, lines 246–304 · score 0.52 · aperiodic oscillatory, family, GAM, covariates, slope, exponent

Paper

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

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 120 lines · 4.7 KB · MIT · 1 match

#%%
import glob
import mne
import pandas as pd
import numpy as np
from fooof import FOOOF
from scipy.signal import welch
workingpath = 'D:\\aperiod\\'
segmentcode = [56, 63, 78]
segment = ['baseline', 'training', 'stress']
segmentlength = [5, 5, 5]
roi_channels = ['Fp1', 'Fp2', 'Fz', 'F4', 'F3', 'F8', 'F7', 'Cz', 'C4', 'C3', 'T4', 'T3', 'Pz', 'P4', 'P3', 'P8', 'P7', 'O2', 'O1']
subjectlist = []
no_subject = len(glob.glob(workingpath + "eeg\\*.vhdr"))
for i in range(0, no_subject):
subjectlist.append(glob.glob(workingpath + "eeg\\*.vhdr")[i][-9:-5])
subjectlist.sort()
with open('fooof_results.txt', 'w') as f:
f.write("Subject\tsegment\tROI\tOffset\tExponent\tR2\tMAE\tResidual_Delta\tResidual_Theta\tResidual_Alpha\tResidual_Beta\tDelta\tTheta\tAlpha\tBeta\t\n")
for subject in subjectlist:
filepathevent = workingpath + 'eeg\\' + str(subject) + '.vhdr'
filepath = workingpath + 'eeg_clean\\' + str(subject) + '.edf'
raw = mne.io.read_raw_brainvision(filepathevent, misc='auto', scale=1.0, preload=True, verbose=None)
raw_clean = mne.io.read_raw_edf(filepath, preload=True, verbose=None, stim_channel='Status')
raw_clean._data *= 1e6
eventcoding = pd.read_csv((workingpath + 'master_eventsII.txt'), delimiter='\t', header=0)
events = mne.find_events(raw, stim_channel="TRIGGER")
tempeventdf = pd.DataFrame(events)
tempeventdf = tempeventdf.loc[:, (tempeventdf != 0).any(axis=0)]
tempeventdf = tempeventdf.loc[:, (tempeventdf != 47831).any(axis=0)]
tempeventdf.columns = range(tempeventdf.shape[1])
tempeventdf = tempeventdf.rename(columns={0: 'trigger_time', 1: 'Code'})
tempeventdf = tempeventdf[tempeventdf['Code'] >= 50]
eventdf = pd.merge(eventcoding, tempeventdf, on='Code', how='inner')
for s in range(0, len(segment)):
starttime = eventdf[eventdf['Code'] == segmentcode[s]]['trigger_time'].values[0]
if segment[s] == 'baseline':
starttime = starttime + (15 * 60 * 500)
for roi in roi_channels:
rawdata = raw_clean.get_data(roi)
filterdata = rawdata[0]
data = filterdata[starttime:starttime + (segmentlength[s] * 60 * 500)]
freqs, psd = welch(
data,
fs=500,
window='hann',
nperseg=1000,
noverlap=500,
scaling='density',
average='mean',
detrend='constant'
)
df = pd.DataFrame({'Frequency_Hz': freqs, 'PSD': psd})
df.to_csv('D:\\aperiod\\export_eeg_psd\\' + segment[s] + '\\' + subject + '_' + roi + '.csv', index=False)
fm = FOOOF(
peak_width_limits=[1, 6],
max_n_peaks=6,
min_peak_height=0.05,
peak_threshold=1.5,
aperiodic_mode='fixed'
)
fm.fit(freqs, psd, [2, 40])
offset, exponent = fm.aperiodic_params_
aperiodic_freqs = fm.freqs
aperiodic_fit = fm._ap_fit
df_aperiod = pd.DataFrame({'Frequency_Hz': aperiodic_freqs, 'PSD': aperiodic_fit})
df_aperiod.to_csv('D:\\aperiod\\export_eeg_aperiod\\' + segment[s] + '\\' + subject + '_' + roi + '.csv', index=False)
mask = freqs > 0
freqs = freqs[mask]
psd = psd[mask]
aperiodic_fit_full = offset - exponent * np.log10(freqs)
peak_only_psd = np.log10(psd) - aperiodic_fit_full
with open('fooof_results.txt', 'a') as f:
f.write(str(subject) + '\t')
f.write(str(segment[s]) + '\t')
f.write(str(roi) + '\t')
f.write(str(offset) + '\t')
f.write(str(exponent) + '\t')
f.write(str(fm.r_squared_) + '\t')
f.write(str(fm.error_) + '\t')
bands = {
'delta': (1, 4),
'theta': (4, 8),
'alpha': (8, 12),
'beta': (12, 30)
}
for band_name, (fmin, fmax) in bands.items():
mask = (freqs >= fmin) & (freqs < fmax)
band_power = np.mean(peak_only_psd[mask])
with open('fooof_results.txt', 'a') as f:
f.write(str(band_power) + '\t')
for band_name, (fmin, fmax) in bands.items():
mask = (freqs >= fmin) & (freqs < fmax)
band_power = np.mean(psd[mask])
with open('fooof_results.txt', 'a') as f:
f.write(str(band_power) + '\t')
with open('fooof_results.txt', 'a') as f:
f.write('\n')

aperiodic_preprocessing.py at commit 5b93e6d, under MIT · at the source

Overview

Authors: Kar Fye Alvin Lee^1,2, Li Liang^1,2, Suhail T. Asharaf^1,2, Tatia M.C. Lee^1,2

ORCID iDs: Li Liang

Laboratory of Neuropsychology and Human Neuroscience, Department of Psychology, The University of Hong Kong, Hong Kong, China
InnoCentre of Clinical Neuropsychology, The University of Hong Kong, Hong Kong, China

Institutions: University of Hong Kong (Hong Kong SAR China)

Journal: iScience, volume 29, issue 8, article 116936

Dates: received 20 January 2026; accepted 9 July 2026; published online 10 August 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1016/j.isci.2026.116936 · PMID 42620688 · PMCID PMC13486866 · OpenAlex W7202090706

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: EEG (modality), systems (subfield)

Methods: Spectral & time-frequency, Statistics, Preprocessing

Keywords: psychological stress, aperiodic activity, neural oscillation

Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: University of Hong Kong; Guangdong-Hong Kong Joint Laboratory for Psychiatric Disorders (2023B1212120004)

Citations: not cited yet (Europe PMC); 64 references in the paper

Research resources: MATLAB R2025b RRID:SCR_001622, R 4.4.1 RRID:SCR_001905, EEGLAB v2025.1.0 RRID:SCR_007292, Python 3.10 RRID:SCR_008394

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

alvinleekarfye/alvin-aperiodic-stressinduction-analysis-2026

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 5b93e6d5e2f4094afe1063ef2cc8f7e5250cfbeb, 6 July 2026

Languages: R (12), Python (1), MATLAB (1)

Size: 16 files, 14 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: emmeans (11 files), mgcv (11 files), tidyverse (11 files), ggplot2 (9 files), eegUtils (8 files), patchwork (8 files), EEGLAB (1 file), Parallel Computing Toolbox (1 file), MNE-Python (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file), specparam (formerly FOOOF) (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

16 files

Scripts/Alpha.R, R, 158 lines
Scripts/Aperiod.R, R, 131 lines
Scripts/Beta.R, R, 158 lines
Scripts/CWB.R, R, 271 lines
Scripts/Covariation.R, R, 357 lines, 1 match
Scripts/Delta.R, R, 168 lines
Scripts/MIST.R, R, 79 lines
Scripts/Offset.R, R, 158 lines
Scripts/PSD.R, R, 104 lines
Scripts/R2.R, R, 129 lines
Scripts/Stress.R, R, 48 lines, 1 match
Scripts/Theta.R, R, 131 lines
Scripts/aperiodic_preprocessing.py, Python, 120 lines, 1 match
Scripts/eeg_cleaning.m, MATLAB, 24 lines
LICENSE, License, 21 lines
README.md, Text, 11 lines

At the source: github.com/alvinleekarfye/alvin-aperiodic-stressinduction-analysis-2026

Zenodo 21207822

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data and code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

16 files

Scripts/Alpha.R, R, 158 lines
Scripts/Aperiod.R, R, 131 lines
Scripts/Beta.R, R, 158 lines
Scripts/CWB.R, R, 271 lines
Scripts/Covariation.R, R, 357 lines
Scripts/Delta.R, R, 168 lines
Scripts/MIST.R, R, 79 lines
Scripts/Offset.R, R, 158 lines
Scripts/PSD.R, R, 104 lines
Scripts/R2.R, R, 129 lines
Scripts/Stress.R, R, 48 lines
Scripts/Theta.R, R, 131 lines
Scripts/aperiodic_preprocessing.py, Python, 120 lines
Scripts/eeg_cleaning.m, MATLAB, 24 lines
LICENSE, License, 21 lines
README.md, Text, 11 lines

At the source: doi.org/10.5281/zenodo.21207822

craddm/eegutils

License: other

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 2b2dbe123b1fabaac414aa0f62b8259194d40e5f, 10 February 2026

Languages: R (85), JavaScript (38), C++ (3)

Size: 1,508 files, 126 scripts

Software Heritage: not archived

Found in: the text, “Key resources table”

Holds: README, license file, CITATION.cff, environment (DESCRIPTION), tests, continuous integration, documentation, 7 notebooks

Tools: tidyverse (42 files), ggplot2 (17 files), eegUtils (8 files), data.table (4 files), mgcv (2 files), Plotly (2 files)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

128 files

R/RcppExports.R, R, 15 lines
R/ar-faster.R, R, 508 lines
R/ar_for_ica.R, R, 280 lines
R/artefact_rejection.R, R, 295 lines
R/artefact_viewer.R, R, 160 lines
R/baseline_correction.R, R, 406 lines
R/channel_management.R, R, 878 lines
R/check_items.R, R, 89 lines
R/class_handling.R, R, 416 lines
R/current_source_density.R, R, 397 lines
R/data.R, R, 16 lines
R/data_averaging.R, R, 441 lines
R/data_combine.R, R, 604 lines
R/data_epoching.R, R, 205 lines
R/data_modifiers.R, R, 393 lines
R/data_selection.R, R, 634 lines
R/data_viewers.R, R, 430 lines
R/deprecated.R, R, 7 lines
R/df_converters.R, R, 422 lines
R/dplyr-extensions.R, R, 350 lines
R/eegUtils-package.R, R, 8 lines
R/epoch_images.R, R, 480 lines
R/epoch_queries.R, R, 97 lines
R/erp_scalp.R, R, 447 lines
R/event_codes.R, R, 243 lines
R/fieldtrip_structures.R, R, 174 lines
R/file_bva_import.R, R, 229 lines
R/file_export.R, R, 205 lines
R/file_io.R, R, 1,056 lines
R/filtering.R, R, 525 lines
R/frequency_analysis.R, R, 335 lines
R/frequency_plotting.R, R, 353 lines
R/get_scalpmap.R, R, 394 lines
R/ggplot2-extensions.R, R, 663 lines
R/glm_fitting.R, R, 340 lines
R/import_erplab.R, R, 83 lines
R/parse_hdf5.R, R, 219 lines
R/plot_butterfly.R, R, 342 lines
R/plot_difference.R, R, 91 lines
R/plot_gfp.R, R, 73 lines
R/plot_timecourse.R, R, 572 lines
R/print_functions.R, R, 222 lines
R/run_ICA.R, R, 443 lines
R/signal_decomposition.R, R, 227 lines
R/stat_classes.R, R, 66 lines
R/summarise_eeg.R, R, 149 lines
R/summary_contour.R, R, 447 lines
R/tfr_analysis.R, R, 963 lines
R/topoplot.R, R, 629 lines
R/utils.R, R, 114 lines
R/view_ica.R, R, 360 lines
R/zzz.R, R, 64 lines
README.rmd, R, 89 lines
data-raw/proc_elecs_internal.R, R, 16 lines
docs/articles/data_structures_files/header-attrs-2.6/header-attrs.js, JavaScript, 12 lines
docs/articles/data_structures_files/header-attrs-2.7/header-attrs.js, JavaScript, 12 lines
docs/articles/data_structures_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/articles/eegUtils_files/header-attrs-2.6/header-attrs.js, JavaScript, 12 lines
docs/articles/eegUtils_files/header-attrs-2.7/header-attrs.js, JavaScript, 12 lines
docs/articles/epoch-handling_files/header-attrs-2.6/header-attrs.js, JavaScript, 12 lines
docs/articles/epoch-handling_files/header-attrs-2.7/header-attrs.js, JavaScript, 12 lines
docs/articles/linear_modelling/linear_modelling_files/header-attrs-2.6/header-attrs.js, JavaScript, 12 lines
docs/articles/linear_modelling/linear_modelling_files/header-attrs-2.7/header-attrs.js, JavaScript, 12 lines
docs/articles/time-frequency-analysis_files/header-attrs-2.6/header-attrs.js, JavaScript, 12 lines
docs/articles/time-frequency-analysis_files/header-attrs-2.7/header-attrs.js, JavaScript, 12 lines
docs/articles/topoplot/topoplot_files/header-attrs-2.6/header-attrs.js, JavaScript, 12 lines
docs/articles/topoplot/topoplot_files/header-attrs-2.7/header-attrs.js, JavaScript, 12 lines
docs/bootstrap-toc.js, JavaScript, 159 lines
docs/dev/articles/data_structures_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/dev/articles/eegUtils_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/dev/articles/epoch-handling_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/dev/articles/linear_modelling/linear_modelling_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/dev/articles/time-frequency-analysis_files/header-attrs-2.10/header-attrs.js, JavaScript, 12 lines
docs/dev/articles/time-frequency-analysis_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/dev/articles/topoplot/topoplot_files/header-attrs-2.9/header-attrs.js, JavaScript, 12 lines
docs/dev/bootstrap-toc.js, JavaScript, 159 lines
docs/dev/deps/bootstrap-5.3.1/bootstrap.bundle.min.js, JavaScript, 7 lines
docs/dev/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js, JavaScript, 5 lines
docs/dev/deps/clipboard.js-2.0.11/clipboard.min.js, JavaScript, 7 lines
docs/dev/deps/headroom-0.11.0/headroom.min.js, JavaScript, 7 lines
docs/dev/deps/headroom-0.11.0/jQuery.headroom.min.js, JavaScript, 7 lines
docs/dev/deps/jquery-3.6.0/jquery-3.6.0.js, JavaScript, 7,407 lines
docs/dev/deps/jquery-3.6.0/jquery-3.6.0.min.js, JavaScript, 2 lines
docs/dev/deps/search-1.0.0/autocomplete.jquery.min.js, JavaScript, 7 lines
docs/dev/deps/search-1.0.0/fuse.min.js, JavaScript, 9 lines
docs/dev/deps/search-1.0.0/mark.min.js, JavaScript, 7 lines
docs/dev/docsearch.js, JavaScript, 85 lines
docs/dev/katex-auto.js, JavaScript, 14 lines
docs/dev/lightswitch.js, JavaScript, 85 lines
docs/dev/pkgdown.js, JavaScript, 162 lines
docs/docsearch.js, JavaScript, 85 lines
docs/pkgdown.js, JavaScript, 108 lines
src/RcppExports.cpp, C++, 62 lines
src/base_mat.cpp, C++, 37 lines
src/ica_test.cpp, C++, 17 lines
tests/testthat.R, R, 3 lines
tests/testthat/test-ICA.R, R, 64 lines
tests/testthat/test-data_averaging.R, R, 8 lines
tests/testthat/test-data_combine.R, R, 139 lines
tests/testthat/test-dply.R, R, 51 lines
tests/testthat/test-eeg_combine.R, R, 48 lines
tests/testthat/test-epoch_images.R, R, 20 lines
tests/testthat/test-ggplot.R, R, 68 lines
tests/testthat/test-glm.R, R, 12 lines
tests/testthat/test-import-bdf.R, R, 48 lines
tests/testthat/test-parse_hdf5.R, R, 7 lines
tests/testthat/test-plot_gfp.R, R, 11 lines
tests/testthat/test-plotting_funs.R, R, 124 lines
tests/testthat/test-signal_decomposition.R, R, 12 lines
tests/testthat/test-topoplot.R, R, 43 lines
tests/testthat/test-utils.R, R, 29 lines
tests/testthat/test_artefact_rejection.R, R, 32 lines
tests/testthat/test_baseline_correction.R, R, 13 lines
tests/testthat/test_channel_management.R, R, 17 lines
tests/testthat/test_downsample.R, R, 11 lines
tests/testthat/test_filtering.R, R, 25 lines
tests/testthat/test_frequency_analysis.R, R, 58 lines
tests/testthat/test_frequency_plotting.R, R, 65 lines
tests/testthat/test_referencing.R, R, 24 lines
tests/testthat/test_selections.R, R, 108 lines
vignettes/data_structures.Rmd, R, 117 lines
vignettes/eegUtils.Rmd, R, 176 lines
vignettes/epoch-handling.Rmd, R, 106 lines
vignettes/linear_modelling/linear_modelling.Rmd, R, 120 lines
vignettes/time-frequency-analysis.Rmd, R, 69 lines
vignettes/topoplot/topoplot.Rmd, R, 115 lines
LICENSE, License, 2 lines
README.md, Text, 133 lines

At the source: github.com/craddm/eegutils

The paper's code and data availability statement is in the Data section.

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3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
154 scripts, each with its path and the digest of its content;
3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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it points to the authors' code: alvinleekarfye/alvin-aperiodic-stressinduction-analysis-2026, Zenodo 21207822
it says that the data are available on request
it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.116936.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 2 funders, 64 references, 4 RRIDs.

Cite

This paper

Lee, K. F. A., Liang, L., Asharaf, S. T., & Lee, T. M. (2026). Beyond neural oscillations: Stress-related aperiodic activity and aperiodic-oscillatory spectral covariation. iScience, 29(8), 116936. https://doi.org/10.1016/j.isci.2026.116936

BibTeX

@article{lee2026beyond, author = {Lee, Kar Fye Alvin and Liang, Li and Asharaf, Suhail T. and Lee, Tatia M.C.}, title = {{Beyond neural oscillations: Stress-related aperiodic activity and aperiodic-oscillatory spectral covariation}}, journal = {iScience}, year = {2026}, month = aug, volume = {29}, number = {8}, pages = {116936}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116936}, url = {https://doi.org/10.1016/j.isci.2026.116936}, pmid = {42620688}, pmcid = {PMC13486866} }

RIS

TY - JOUR AU - Lee, Kar Fye Alvin AU - Liang, Li AU - Asharaf, Suhail T. AU - Lee, Tatia M.C. TI - Beyond neural oscillations: Stress-related aperiodic activity and aperiodic-oscillatory spectral covariation T2 - iScience J2 - iScience PY - 2026 DA - 2026/08/10 VL - 29 IS - 8 SP - 116936 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116936 UR - https://doi.org/10.1016/j.isci.2026.116936 LA - en ER -

CSL-JSON

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Beyond neural oscillations: Stress-related aperiodic activity and aperiodic-oscillatory spectral covariation.

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alvinleekarfye/alvin-aperiodic-stressinduction-analysis-2026

Zenodo 21207822

craddm/eegutils

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