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Linguistic structure and language familiarity sharpen phoneme encoding in the brain.

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5 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.

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  1. [1] § MEG data preprocessing ↔ Dataset2/Scripts/Generate_TRFs/utils_unified.py, lines 111 to 153 · score 0.97 · superior temporal gyrus, noise covariance, G_T_transv, G_temp_sup, Plan_polar, Plan_tempo · Report a mismatch
  2. [2] § MEG data preprocessing ↔ Dataset3/Scripts/Generate_TRFs/utils.py, lines 255 to 299 · score 0.97 · superior temporal gyrus, G_T_transv, G_temp_sup, Plan_polar, Plan_tempo, aparc.a2009s · Report a mismatch
  3. [3] § MEG data preprocessing ↔ Dataset1/Scripts/Preprocess_MEG_data.py, lines 232 to 285 · score 0.59 · low pass filtered, FIR, MNE, preprocessing, resampled, MEG · Report a mismatch
  4. [4] § MEG data preprocessing ↔ Dataset2/Scripts/Generate_TRFs/utils_unified.py, lines 197 to 246 · score 0.54 · low pass filtered, MNE, cropped, resampled, 100 Hz, onset · Report a mismatch
  5. [5] § Predictor variables › Acoustic features ↔ Dataset1/Scripts/make_predictors.py, lines 1 to 17 · score 0.50 · gammatone spectrogram, Acoustic features, detection, band, onset, models · Report a mismatch

Paper

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The authors' code

Python · 246 lines · 10 KB · no license · 2 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Unified Utility Functions for TRF Estimation
  6. This module contains functions for source space reconstruction that work
  7. with both Dutch and Chinese stimuli by accepting a stimulus_type parameter.
  9. @author: filiztezcan
  10. """
  12. import os
  13. from functools import reduce
  14. import numpy as np
  15. import mne
  16. from mne.minimum_norm import (read_inverse_operator, apply_inverse_raw,
  17. make_inverse_operator, write_inverse_operator)
  18. from mne import pick_types
  19. import eelbrain
  22. def make_source_space(condition, subject, processed_MEG_folder, subjects_dir,
  23. STIMULI, stimulus_type='Dutch'):
  24. """
  25. Create source space data for TRF estimation
  27. This function processes MEG data to create source-localized activity
  28. in bilateral superior temporal gyrus regions for TRF estimation.
  30. Parameters
  31. ----------
  32. condition : str
  33. Condition name ('Words' or 'Syllables')
  34. subject : str
  35. Subject ID (e.g., 'sub-003')
  36. processed_MEG_folder : Path
  37. Path to processed MEG data directory
  38. subjects_dir : str
  39. Path to FreeSurfer subjects directory
  40. STIMULI : list
  41. List of stimulus identifiers (kept for compatibility, not used)
  42. stimulus_type : str, optional
  43. Type of stimuli ('Dutch' or 'Chinese'), default is 'Dutch'
  44. This determines which MEG and events files to load
  46. Returns
  47. -------
  48. stc_lh_all : list of NDVar
  49. List of source space data for left hemisphere (one per trial)
  50. stc_rh_all : list of NDVar
  51. List of source space data for right hemisphere (one per trial)
  52. """
  54. # Parameters
  55. tmax = 0.7 # Maximum time for cropping
  56. fname_fsaverage_src = os.path.join(subjects_dir, 'fsaverage', 'bem',
  57. 'fsaverage-ico-4-src.fif')
  59. # File paths - dependent on stimulus type
  60. raw_file_name = os.path.join(processed_MEG_folder,
  61. f'{subject}_resampled_300Hz-1-100Hz_filtered-ICA-eyeblink_{stimulus_type}_stimuli.fif')
  62. frwfname = os.path.join(processed_MEG_folder, f'{subject}_forward.fif')
  63. invfname = os.path.join(processed_MEG_folder, f'{subject}_inverse.fif')
  64. events_file = os.path.join(subjects_dir, subject, 'meg',
  65. f'{subject}-eve_{stimulus_type}_stimuli.fif')
  67. # Verify files exist
  68. if not os.path.exists(raw_file_name):
  69. raise FileNotFoundError(f"MEG raw file not found: {raw_file_name}")
  70. if not os.path.exists(events_file):
  71. raise FileNotFoundError(f"Events file not found: {events_file}")
  73. # Load data
  74. print(f" Loading MEG data: {os.path.basename(raw_file_name)}")
  75. raw = mne.io.read_raw_fif(raw_file_name, preload=True, verbose=False)
  76. info = raw.info
  78. print(f" Loading events: {os.path.basename(events_file)}")
  79. events = mne.read_events(events_file, verbose=False)
  81. # Create or load forward solution
  82. if not os.path.isfile(frwfname):
  83. print(" Creating forward solution...")
  84. src = mne.setup_source_space(subject, spacing='ico4',
  85. subjects_dir=subjects_dir, verbose=False)
  87. # Single layer BEM
  88. conductivity = (0.3,)
  89. model = mne.make_bem_model(subject=subject,
  90. conductivity=conductivity,
  91. subjects_dir=subjects_dir,
  92. verbose=False)
  93. bem = mne.make_bem_solution(model, verbose=False)
  95. trans = os.path.join(subjects_dir, subject, 'meg', f'{subject}-trans.fif')
  97. if not os.path.exists(trans):
  98. raise FileNotFoundError(f"Transformation file not found: {trans}")
  100. fwd = mne.make_forward_solution(info, trans=trans, src=src, bem=bem,
  101. meg=True, eeg=False, mindist=5.0,
  102. n_jobs=-1, verbose=False)
  104. fwd = mne.convert_forward_solution(fwd, surf_ori=True, verbose=False)
  105. mne.write_forward_solution(frwfname, fwd, overwrite=True, verbose=False)
  106. print(f" ✓ Forward solution saved: {os.path.basename(frwfname)}")
  107. else:
  108. fwd = mne.read_forward_solution(frwfname, verbose=False)
  109. print(f" ✓ Forward solution loaded: {os.path.basename(frwfname)}")
  111. # Create or load inverse operator
  112. if not os.path.isfile(invfname):
  113. print(" Creating inverse operator...")
  114. event_id = [10, 20, 30]
  115. baseline = (None, None)
  116. picks = pick_types(raw.info, meg=True, ref_meg=False, eeg=False,
  117. eog=False, stim=False)
  119. epochs = mne.Epochs(raw, events, event_id=event_id, tmin=0, tmax=5.95,
  120. preload=True, baseline=baseline, picks=picks,
  121. verbose=False)
  123. # Compute noise covariance from silent period (4.9-5.9s)
  124. noise_cov = mne.compute_covariance(epochs, tmin=4.9, tmax=5.9,
  125. method='empirical', rank='info',
  126. verbose=False)
  128. inverse_operator = make_inverse_operator(info, fwd, noise_cov,
  129. fixed=True, verbose=False)
  130. write_inverse_operator(invfname, inverse_operator, verbose=False)
  131. print(f" ✓ Inverse operator saved: {os.path.basename(invfname)}")
  132. else:
  133. inverse_operator = read_inverse_operator(invfname, verbose=False)
  134. print(f" ✓ Inverse operator loaded: {os.path.basename(invfname)}")
  136. # Inverse parameters
  137. snr = 3.0
  138. lambda2 = 1.0 / snr ** 2
  139. method = "dSPM"
  141. raw = raw.pick_types(meg=True, ref_meg=False)
  143. # Define ROI labels (bilateral superior temporal gyrus)
  144. print(" Loading anatomical labels...")
  145. labels = mne.read_labels_from_annot(subject='fsaverage', parc='aparc.a2009s',
  146. subjects_dir=subjects_dir, verbose=False)
  147. labels_name = [l.name for l in labels]
  149. # Superior temporal gyrus regions
  150. search_lh = np.array(['G_temp_sup-G_T_transv-lh', 'G_temp_sup-Lateral-lh',
  151. 'G_temp_sup-Plan_polar-lh', 'G_temp_sup-Plan_tempo-lh'])
  152. search_rh = np.array(['G_temp_sup-G_T_transv-rh', 'G_temp_sup-Lateral-rh',
  153. 'G_temp_sup-Plan_polar-rh', 'G_temp_sup-Plan_tempo-rh'])
  155. # Merge labels for each hemisphere
  156. label_index_lh = [labels_name.index(l) for l in search_lh]
  157. label_index_rh = [labels_name.index(l) for l in search_rh]
  159. lh_label_list = [labels[i] for i in label_index_lh]
  160. rh_label_list = [labels[i] for i in label_index_rh]
  162. stc_lh_merged_label = reduce(lambda x, y: x + y, lh_label_list)
  163. stc_rh_merged_label = reduce(lambda x, y: x + y, rh_label_list)
  165. # Load fsaverage source space
  166. print(' Reading fsaverage source space...')
  167. src_to = mne.read_source_spaces(fname_fsaverage_src, verbose=False)
  169. # Select trials based on condition
  170. if condition == 'Words':
  171. onsets_all = np.where(events[:, 2] == 10)[0]
  172. onsets = onsets_all[:40].copy()
  173. elif condition == 'Syllables':
  174. onsets_all = np.where(events[:, 2] == 20)[0]
  175. onsets = onsets_all[:40].copy()
  176. else:
  177. raise ValueError(f"Unknown condition: {condition}. Must be 'Words' or 'Syllables'")
  179. print(f" Processing {len(onsets)} trials for {condition} condition...")
  181. # Process each trial
  182. stc_lh_all = []
  183. stc_rh_all = []
  185. for t, index in enumerate(onsets):
  186. start = events[index, 0]
  187. tstart = start / raw.info['sfreq']
  188. tend = tstart + 4
  190. if (t + 1) % 10 == 0:
  191. print(f' Trial {t+1}/{len(onsets)}')
  193. # Crop raw data around trial
  194. raw_cropped = raw.copy().crop(tmin=tstart - 0.05, tmax=tend + tmax)
  195. raw_cropped.load_data()
  197. # Apply inverse solution
  198. stc = apply_inverse_raw(raw_cropped, inverse_operator, lambda2, method,
  199. verbose=False)
  201. # Low-pass filter at 8.5 Hz
  202. stc._data = mne.filter.filter_data(stc.data, 300, l_freq=None, h_freq=8.5,
  203. verbose=False)
  205. # Morph to fsaverage
  206. morph = mne.compute_source_morph(stc, subject_from=subject,
  207. subject_to='fsaverage', src_to=src_to,
  208. subjects_dir=subjects_dir, verbose=False)
  209. stc = morph.apply(stc)
  211. # Resample to 100 Hz
  212. stc = stc.resample(100, verbose=False)
  214. # Extract ROI activity
  215. stc_lh = stc.in_label(stc_lh_merged_label)
  216. stc_rh = stc.in_label(stc_rh_merged_label)
  218. del raw_cropped, stc
  220. # Crop to expected length (474 samples = 4.74s at 100 Hz)
  221. expected_samples = 474
  222. actual_samples = len(stc_lh._data.T)
  223. crop = actual_samples - expected_samples
  225. if crop != 0:
  226. if (t + 1) % 10 == 0:
  227. print(f' Trimming {crop} samples (expected {expected_samples}, got {actual_samples})')
  228. stc_lh._data = stc_lh._data[:, :-crop]
  229. stc_lh._times = stc_lh.times[:-crop]
  230. stc_rh._data = stc_rh._data[:, :-crop]
  231. stc_rh._times = stc_rh.times[:-crop]
  233. # Convert to eelbrain NDVar
  234. stc_lh_ndvar = eelbrain.load.fiff.stc_ndvar(stc=stc_lh, subject='fsaverage',
  235. src='ico-4', subjects_dir=subjects_dir,
  236. parc='aparc', check=True)
  237. stc_rh_ndvar = eelbrain.load.fiff.stc_ndvar(stc=stc_rh, subject='fsaverage',
  238. src='ico-4', subjects_dir=subjects_dir,
  239. parc='aparc', check=True)
  241. stc_lh_all.append(stc_lh_ndvar)
  242. stc_rh_all.append(stc_rh_ndvar)
  244. print(f" ✓ Processed all {len(onsets)} trials")
  246. return stc_lh_all, stc_rh_all

utils_unified.py at commit 410ba05, no license · at the source

Overview

Authors: Filiz Tezcan1,2,3, Sanne Ten Oever1,2,3, Fan Bai1, Noémie te Rietmolen1, Andrea E. Martin1,3
ORCID iDs: Filiz Tezcan, Sanne Ten Oever, Fan Bai, Andrea E. Martin
  1. Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics,Nijmegen, The Netherlands
  2. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University,Maastricht, The Netherlands
  3. Donders Centre for Cognitive Neuroimaging, Radboud University,Nijmegen, The Netherlands
Institutions: Max Planck Institute for Psycholinguistics (Netherlands); Maastricht University (Netherlands); Donders Centre for Cognitive Neuroimaging (Netherlands)
Journal: Communications biology, volume 9, issue 1, article 638
Dates: received 11 June 2025; accepted 4 March 2026; published online 20 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-09865-8 · PMID 41862572 · PMCID PMC13168568 · OpenAlex W7139102411
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics
Keywords: Language, Human behaviour
MeSH: Brain*, Language*, Linguistics*, Phonetics*, Recognition, Psychology*, Speech Perception*, Comprehension, Female, Humans, Magnetoencephalography, Male (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Dutch Research Council (NWO) (016.Vidi.188.029); European Research Council (101116685, 101170162)
Citations: not cited yet (Europe PMC); 104 references in the paper

Abstract

How does the brain turn a physical signal like speech into meaning? It draws on two key sources: linguistic structure (e.g., phonemes, syntax) and statistical regularities from experience. Yet how these jointly shape neural representations of language remains unclear. We used MEG to track phonemic and acoustic encoding during spoken language comprehension in native Dutch, Mandarin-Chinese, and Turkish speakers. Phoneme-level encoding is stronger during sentence comprehension than in word lists, and more robust within words than random syllables. Surprisingly, similar encoding emerges even in an uncomprehended language but only with prior exposure. In contrast, acoustic edges are briefly suppressed early in comprehension. This suggests that the brain’s alignment to speech (in phase and power) is robustly tuned by structure and by learned statistical patterns. Our findings show how structured knowledge and experience-based learning interact to shape neural responses to language, offering insight into how the brain processes complex, meaningful signals.

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 5 matches between paragraphs and lines of code.

tezcanf/Phoneme_encoding_sentences_words_syllables

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 410ba05498c8eec7988e0723851ddc5a3fd36a4b, 23 February 2026
Languages: Python (42)
Size: 868 files, 42 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (environment.yml, requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (30 files), pandas (16 files), MNE-Python (12 files), Matplotlib (11 files), SciPy (10 files), seaborn (5 files), PyTorch (2 files), statsmodels (2 files), Hugging Face Transformers (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
43 files
At the source: github.com/tezcanf/Phoneme_encoding_sentences_words_syllables

Code availability

The code supporting the findings of this study is available on the GitHub repository at https://github.com/tezcanf/Phoneme_encoding_sentences_words_syllables.

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 42 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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No dataset and no data link were found in the paper.

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Processed MEG data and Source data underlying the figures in this paper are available in the Radboud University Repository database and are openly available with the identifier 10.34973/jedg-5009.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 11 MeSH terms, 2 funders, 88 references.

Cite

This paper

Tezcan, F., Ten Oever, S., Bai, F., te Rietmolen, N., & Martin, A. E. (2026). Linguistic structure and language familiarity sharpen phoneme encoding in the brain. Communications biology, 9(1), 638. https://doi.org/10.1038/s42003-026-09865-8

BibTeX

@article{tezcan2026linguistic,
author = {Tezcan, Filiz and Ten Oever, Sanne and Bai, Fan and te Rietmolen, Noémie and Martin, Andrea E.},
title = {{Linguistic structure and language familiarity sharpen phoneme encoding in the brain}},
journal = {Communications biology},
year = {2026},
month = mar,
volume = {9},
number = {1},
pages = {638},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09865-8},
url = {https://doi.org/10.1038/s42003-026-09865-8},
pmid = {41862572},
pmcid = {PMC13168568}
}

RIS

TY - JOUR
AU - Tezcan, Filiz
AU - Ten Oever, Sanne
AU - Bai, Fan
AU - te Rietmolen, Noémie
AU - Martin, Andrea E.
TI - Linguistic structure and language familiarity sharpen phoneme encoding in the brain
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/03/20
VL - 9
IS - 1
SP - 638
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09865-8
UR - https://doi.org/10.1038/s42003-026-09865-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-09865-8",
"type": "article-journal",
"title": "Linguistic structure and language familiarity sharpen phoneme encoding in the brain",
"container-title": "Communications biology",
"author": [
{
"family": "Tezcan",
"given": "Filiz"
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{
"family": "Ten Oever",
"given": "Sanne"
},
{
"family": "Bai",
"given": "Fan"
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{
"family": "te Rietmolen",
"given": "Noémie"
},
{
"family": "Martin",
"given": "Andrea E."
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "638",
"DOI": "10.1038/s42003-026-09865-8",
"PMID": "41862572",
"PMCID": "PMC13168568",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-09865-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
20
]
]
}
}

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