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Non-linear relationships between auditory mismatch responses and the inharmonicity of complex sounds.

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. [1] § Methods › Data processing and statistical analysis ↔ scripts/utility_funs.py, lines 92–144 · score 0.77 · 150–350 ms, 70–250 ms, negative peak, 150 ms, 70 ms, window
  2. [2] § Methods › Procedure ↔ paradigms/eeg/eeg_paradigm.py, lines 42–62 · score 0.71 · inter onset interval, EEG paradigm, IOI, block, 200 Hz, sounds
  3. [3] § Methods › Data processing and statistical analysis ↔ scripts/utility_funs.py, lines 35–68 · score 0.66 · notch filter, decimated, baseline, EOG, epochs, stimulus

Paper

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The authors' code

Python · 175 lines · 5 KB · no license · 2 matches

  1. import numpy as np
  2. import mne
  3. from scipy.signal import find_peaks
  5. front_channel_picks = ["Fz", "F1", "F2", "AF3", "AF4", "AFz", "F3", "F4", "FC1", "FC2", "FCz"]
  7. def participant_fname(pid):
  8. """Return a filename for given participant id."""
  9. # return participants[pid]
  10. return f'hm_{pid}'
  13. def recode_events(ev):
  14. # get triggers from events array
  15. trigs = ev[:, 2]
  17. # iterate over trigs
  18. for i, t in enumerate(trigs):
  20. # always ignore first 10 sounds in the block
  21. if t > 200 and t < 300:
  22. trigs[i:i+10] = 0
  24. # if deviant, set new trigger encoding
  25. elif t > 100 and t < 200:
  26. trigs[i] += 1000
  27. trigs[i+1] += 1200
  28. trigs[i+2] += 1300
  30. # put everything back
  31. ev[:, 2] = trigs
  32. return ev
  35. def read_and_epoch(fname, ch_eog, ch_exclude=None, inspect=False, bandpass_low=.2, bandpass_high=30, notch=50):
  36. raw = mne.io.read_raw(fname,
  37. eog=ch_eog,
  38. # misc=ch_ecg,
  39. exclude=ch_exclude,
  40. stim_channel='Status',
  41. preload=True)
  43. raw.set_montage('biosemi64')
  45. # filter
  46. raw.notch_filter(notch)
  47. raw.filter(bandpass_low, bandpass_high)
  49. # visual inspection
  50. if inspect:
  51. raw.plot()
  53. # epoch the data
  54. events_raw = mne.find_events(raw, stim_channel='Status')
  55. events = recode_events(events_raw)
  56. event_ids = {f'j{i}/std': i+1 for i in range(8)}
  57. event_ids.update({f'j{i}/dev_1': i+1101 for i in range(8)})
  58. event_ids.update({f'j{i}/dev_2': i+1201 for i in range(8)})
  59. event_ids.update({f'j{i}/dev_3': i+1301 for i in range(8)})
  60. epochs = mne.Epochs(raw,
  61. events,
  62. event_ids,
  63. tmin=-.1, tmax=.5,
  64. baseline=None,
  65. decim=2 # decimate so new sr is 1024
  66. )
  68. return raw, epochs
  71. def read_preprocessed_epochs(pid, epochs_path):
  72. epochs = mne.read_epochs(f'{epochs_path}/{pid}-epo.fif')
  73. return epochs
  77. def make_evokeds(epochs):
  78. jitters = [f'j{i}' for i in range(8)]
  79. evokeds = {}
  81. for jitter in jitters:
  82. evokeds[jitter] = {
  83. 'std': epochs[f'{jitter}/std'].average(),
  84. 'dev': epochs[f'{jitter}/dev'].average(),
  85. }
  86. evokeds[jitter]['mmn'] = mne.combine_evoked([evokeds[jitter]['dev'], evokeds[jitter]['std']], weights=[1, -1])
  88. return evokeds
  92. def participant_peaks(evoked, mean_window=(-0.025, 0.025)):
  93. e = evoked.copy().pick(front_channel_picks)
  94. p3_window = (.15, .35)
  95. mmn_window = (.07, .25)
  96. try:
  97. # numpy array of mmn window data
  98. mmn_epoch = e.copy().crop(*mmn_window)
  99. e_times = mmn_epoch.times
  100. np_epoch = np.mean(mmn_epoch.get_data(), axis=0)
  102. # find negative peaks (cause it's mmn)
  103. peak_indices, _ = find_peaks(-np_epoch)
  104. peak_index = np.argmin(np_epoch[peak_indices])
  105. mmn_lat = e_times[peak_indices[peak_index]]
  107. # calculate MMN mean amplitude
  108. e_mean_crop = e.copy().crop(
  109. tmin=mmn_lat + mean_window[0], tmax=mmn_lat + mean_window[1]
  110. )
  111. mmn_amp = e_mean_crop.data.mean() * 1e6
  113. except ValueError:
  114. mmn_lat, mmn_amp = np.nan, np.nan
  117. try:
  118. # numpy array of mmn window data
  119. p3_epoch = e.copy().crop(*p3_window)
  120. e_times = p3_epoch.times
  121. np_epoch = np.mean(p3_epoch.get_data(), axis=0)
  123. # find negative peaks (cause it's mmn)
  124. peak_indices, _ = find_peaks(np_epoch)
  125. peak_index = np.argmax(np_epoch[peak_indices])
  126. p3_lat = e_times[peak_indices[peak_index]]
  128. # calculate P3 mean amplitude
  129. e_mean_crop = e.copy().crop(
  130. tmin=p3_lat + mean_window[0], tmax=p3_lat + mean_window[1]
  131. )
  132. p3_amp = e_mean_crop.data.mean() * 1e6
  134. except ValueError:
  135. p3_lat, p3_amp = np.nan, np.nan
  137. ret = {
  138. "mmn_lat": mmn_lat,
  139. "mmn_amp": mmn_amp,
  140. "p3_lat": p3_lat,
  141. "p3_amp": p3_amp,
  142. }
  144. return ret
  147. def orn_peaks(evoked, mean_window=(-0.025, 0.025)):
  148. e = evoked.copy().pick(front_channel_picks)
  149. try:
  150. orn_ch, orn_lat, orn_amp = e.get_peak(
  151. tmin=0.1, tmax=0.3, mode="neg", return_amplitude=True
  152. )
  153. # calculate ORN mean amplitude
  154. e_mean_crop = e.copy().crop(
  155. tmin=orn_lat + mean_window[0], tmax=orn_lat + mean_window[1]
  156. )
  157. orn_mean_amp = e_mean_crop.data.mean() * 1e6
  158. except ValueError:
  159. orn_ch, orn_lat, orn_amp, orn_mean_amp = np.nan, np.nan, np.nan, np.nan
  161. ret = {
  162. "orn_peak_ch": orn_ch,
  163. "orn_peak_lat": orn_lat,
  164. "orn_peak_amp": orn_amp * 1e6,
  165. "orn_mean_amp": orn_mean_amp,
  166. }
  167. return ret
  170. def p2_peaks(evoked, peak_time, mean_window=(-0.025, 0.025)):
  171. e = evoked.copy().pick(front_channel_picks)
  172. tmin, tmax = peak_time + mean_window[0], peak_time + mean_window[1]
  173. p2_amp = e.copy().crop(tmin, tmax).data.mean() * 1e6
  175. return p2_amp

utility_funs.py at commit b033cca, no license · at the source

Overview

Authors: Aniela Brzezińska1, Bartosz Witkowski1, Małgorzata Basińska2, Tomasz Domżalski1, Krzysztof Basiński1
  1. Auditory Neuroscience Laboratory, Department of Psychology, Medical University of Gdańsk, Gdańsk, Poland
  2. Division of Quality of Life Research, Department of Psychology, Medical University of Gdańsk, Gdańsk, Poland
Institutions: Gdańsk Medical University (Poland)
Journal: Scientific reports, volume 16, issue 1, article 11836
Dates: received 15 October 2025; accepted 18 February 2026; published online 3 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-41129-7 · PMID 41775782 · PMCID PMC13066439 · OpenAlex W7133314128
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Evoked potentials
Keywords: Predictive processing, Inharmonicity, Mismatch negativity, P3a, Pitch perception, Auditory mismatch, Neuroscience, Psychology
MeSH: Auditory Perception*, Evoked Potentials, Auditory*, Pitch Perception*, Acoustic Stimulation, Adult, Electroencephalography, Female, Humans, Male, Pitch Discrimination, Sound, Young Adult (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Narodowe Centrum Nauki (2022/47/D/HS6/03323)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Harmonicity is a feature of sound that is important for many aspects of auditory perception and previous research has shown that harmonicity modulates the brain’s mismatch responses to oddball sounds. Predictive processing accounts of perception suggest that the brain generates predictions about the incoming stimuli. Since inharmonic sound spectra contain more information, inharmonicity has been suggested to be involved in precision weighting, an important component of predictive processing theories. In this study we explored this issue by parametrically modulating the level of inharmonicity applied to synthetic sounds and recording mismatch responses (MMN and P3a) from healthy volunteers (N = 37) using electroencephalography. Our results show that a sigmoid function models the relationship between inharmonicity and MMN amplitude better than any linear or polynomial function. Furthermore, P3a amplitude has an inverted-U relationship with inharmonicity and peaks at inharmonicity levels just below the threshold for pitch discrimination. These results are consistent with the hypothesis that inharmonicity impairs F0 extraction above a certain threshold.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-41129-7.

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 3 matches between paragraphs and lines of code.

Zenodo 17357764

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (15 files), pandas (13 files), Matplotlib (11 files), MNE-Python (7 files), seaborn (7 files), nlme (5 files), autoreject (3 files), lme4 (3 files), lmerTest (3 files), PsychoPy (3 files), SciPy (3 files), emmeans (2 files), tidyverse (2 files), broom (1 file), SymPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
31 files
At the source:

k-basinski/inharmonicity_modulation

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b033cca2f744363dcbfc94507ebcf73ff751b09c, 24 December 2025
Languages: Python (17), R (10), Jupyter (2)
Size: 596 files, 29 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, 8 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (15 files), pandas (12 files), Matplotlib (11 files), MNE-Python (7 files), seaborn (6 files), nlme (5 files), autoreject (3 files), lme4 (3 files), lmerTest (3 files), PsychoPy (3 files), SciPy (3 files), emmeans (2 files), tidyverse (2 files), broom (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
30 files
At the source: github.com/k-basinski/inharmonicity_modulation/tree/v0.2

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

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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;
  • 59 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);
  • neither the text of the paper nor the code itself.

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Data

Datasets cited

Data availability

The raw data for this experiment is available at 10.5281/zenodo.15341473 while code that was used to perform the analyses is available at 10.5281/zenodo.17357764.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 12 MeSH terms, 1 funder, 52 references.

Cite

This paper

Brzezińska, A., Witkowski, B., Basińska, M., Domżalski, T., & Basiński, K. (2026). Non-linear relationships between auditory mismatch responses and the inharmonicity of complex sounds. Scientific reports, 16(1), 11836. https://doi.org/10.1038/s41598-026-41129-7

BibTeX

@article{brzezinska2026non,
author = {Brzezińska, Aniela and Witkowski, Bartosz and Basińska, Małgorzata and Domżalski, Tomasz and Basiński, Krzysztof},
title = {{Non-linear relationships between auditory mismatch responses and the inharmonicity of complex sounds}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {11836},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-41129-7},
url = {https://doi.org/10.1038/s41598-026-41129-7},
pmid = {41775782},
pmcid = {PMC13066439}
}

RIS

TY - JOUR
AU - Brzezińska, Aniela
AU - Witkowski, Bartosz
AU - Basińska, Małgorzata
AU - Domżalski, Tomasz
AU - Basiński, Krzysztof
TI - Non-linear relationships between auditory mismatch responses and the inharmonicity of complex sounds
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/03
VL - 16
IS - 1
SP - 11836
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-41129-7
UR - https://doi.org/10.1038/s41598-026-41129-7
LA - en
ER -

CSL-JSON

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