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A large-scale MEG and EEG dataset for object recognition in naturalistic scenes

Code ↔ Paper

10 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 10 matches
  1. [1] § Methods › Experiment design › MEG experiment ↔ scripts/step3a-head_motion.py, lines 18–33 · score 0.78 · ImageNet03, ImageNet04, ImageNet02, ImageNet01, ses, NOD
  2. [2] § Technical Validation › Temporal dynamics of face representation ↔ src/decoding.py, lines 158–241 · score 0.62 · occipitotemporal sensors, cross validation, SVM, classification, decoding, accuracy
  3. [3] § Data Records › Raw data ↔ scripts/step1c-epoching.py, lines 19–42 · score 0.62 · detailed_events, NOD MEG, NOD EEG, metadata, BIDS, derivatives
  4. [4] § Methods › Data acquisition › Data preprocessing ↔ scripts/step2a-psd_plot.py, lines 89–113 · score 0.58 · Power Spectral Density, PSD, raw MEG
  5. [5] § Data Records › Preprocessed data ↔ scripts/step2b-ica_plot.py, lines 1–66 · score 0.57 · ImageNet_run, clean fif, ses, derivatives, preprocessed, raw
  6. [6] § Technical Validation › M/EEG-fMRI fusion analysis ↔ src/rsa/pre.py, lines 123–205 · score 0.55 · representational dissimilarity matrix, spatiotemporal, sensors, RDM, distance, correlation
  7. [7] § Methods › Data acquisition › Data preprocessing ↔ src/preprocessing/prep_meg.py, lines 106–139 · score 0.55 · bad channel detection, Maxwell, noisy, preprocessing, MEG
  8. [8] § Data Records › Preprocessed data ↔ scripts/step2a-psd_plot.py, lines 62–87 · score 0.55 · ImageNet_run, clean fif, ses, raw, sub, MEG
  9. [9] § Technical Validation › M/EEG-fMRI fusion analysis ↔ src/rsa/corr.py, lines 108–149 · score 0.51 · confidence interval, correlation coefficients, Spearman, RDM
  10. [10] § Data Records ↔ scripts/step1c-epoching.py, lines 19–42 · score 0.51 · OpenNeuro, ds005810, ds005811, BIDS, epoch, derivatives

Paper

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The authors' code

Python · 239 lines · 7 KB · MIT · 2 matches

  1. # %%
  2. from __future__ import annotations
  4. import os
  5. import os.path as op
  6. import sys
  7. from dataclasses import dataclass
  9. import mne
  10. import pandas as pd
  11. from wasabi import msg
  13. from src.epoching import Epoching
  14. from src.epoching import InfoExtraction
  15. sys.path.append(op.abspath('..'))
  16. # %%
  17. ROOT = '../../NOD-MEEG_upload'
  19. # NOTE: the event roots point to the per-subject ``sub-XX_events.csv`` files.
  20. # On OpenNeuro (MEG: ds005810, EEG: ds005811) these are published under
  21. # ``derivatives/detailed_events``. The BIDS ``*_events.tsv`` files do NOT carry
  22. # the image_id / response / class metadata that epoching relies on, so the
  23. # detailed_events CSVs are required here.
  24. MEG_ROOT, MEG_EVENT_ROOT, MEG_SAVE_ROOT = (
  25. f'{ROOT}/NOD-MEG/{path}' for path in [
  26. 'derivatives/preprocessed/raw',
  27. 'derivatives/detailed_events',
  28. 'derivatives/preprocessed/epochs',
  29. ]
  30. )
  31. EEG_ROOT, EEG_EVENT_ROOT, EEG_SAVE_ROOT = (
  32. f'{ROOT}/NOD-EEG/{path}' for path in [
  33. 'derivatives/preprocessed/raw',
  34. 'derivatives/detailed_events',
  35. 'derivatives/preprocessed/epochs',
  36. ]
  37. )
  39. EVENT_ID = 'stim_on'
  40. TMIN, TMAX = -0.1, 0.8
  41. LFREQ, HFREQ = 0.1, 40
  42. SFREQ = 200
  44. # %%
  47. class MakeEpochs:
  48. def __init__(
  49. self,
  50. roots: dict[str, str],
  51. event_roots: dict[str, str],
  52. event_id: str,
  53. tmin: float,
  54. tmax: float,
  55. lfreq: float,
  56. hfreq: float,
  57. sfreq: float,
  58. data_types: list[str],
  59. save_roots: dict,
  60. ) -> None:
  61. """make epochs based on the NOD-MEG& EEG cleaned data.
  63. Parameters
  64. ----------
  65. roots : dict
  66. Paths to the cleaned data. Keys are data types(meg, eeg), values are the corresponding paths
  67. event_roots : dict
  68. Paths to the event data. Keys are data types(meg, eeg), values are the corresponding paths
  69. event_id : str
  70. The event ID to be used for epoching, shoud be stored in the mne.BaseRaw object
  71. tmin : float
  72. The start time of the epoch
  73. tmax : float
  74. The end time of the epoch
  75. lfreq : float
  76. The low pass filter frequency
  77. hfreq : float
  78. The high pass filter frequency
  79. sfreq : float
  80. The sampling frequency
  81. data_types : list
  82. the data types of the database, default is ['meg', 'eeg']
  83. save_roots : dict
  84. Paths to save the epochs. Keys are data types(meg, eeg), values are the corresponding paths
  85. """
  86. self.roots = roots # {'meg': MEG_ROOT, 'eeg': EEG_ROOT}
  87. # {'meg': MEG_EVENT_ROOT, 'eeg': EEG_EVENT_ROOT}
  88. self.event_roots = event_roots
  89. self.event_id = event_id
  90. self.tmin = tmin
  91. self.tmax = tmax
  92. self.lfreq = lfreq
  93. self.hfreq = hfreq
  94. self.sfreq = sfreq
  95. self.data_types = data_types # ['meg', 'eeg']
  96. self.save_roots = save_root
  98. self.infos = {}
  99. for datatype in self.data_types:
  100. self.infos[datatype] = InfoExtraction(
  101. self.roots[datatype], self.event_roots[datatype],
  102. )
  104. def make_sub(
  105. self,
  106. sub: str,
  107. align_method: str,
  108. ) -> dict:
  109. """Create epochs for all data types for a subject
  111. Parameters
  112. ----------
  113. sub : str
  114. Subject ID
  115. align_method : str
  116. Method to align the data.
  117. Options are 'info_with_data', 'info_only' and 'maxwell
  119. Returns
  120. -------
  121. dict
  122. Keys are data types, values are the corresponding epochs
  123. """
  124. epochs = {}
  125. for datatype in self.data_types:
  126. info = self.infos[datatype]
  127. try:
  128. sub_fps = info.get_sub_fp(sub)
  129. except KeyError:
  130. msg.warn(f"Subject {sub} not found in {datatype} data.")
  131. continue
  132. epochor = Epoching(
  133. event_csv=sub_fps['events'],
  134. raw_paths=sub_fps['rawps'],
  135. tmin=self.tmin,
  136. tmax=self.tmax,
  137. lfreq=self.lfreq,
  138. hfreq=self.hfreq,
  139. sfreq=self.sfreq,
  140. datatype=datatype,
  141. event_id=self.event_id,
  142. )
  143. epoched = epochor.run(align_method=align_method)
  144. epochs[datatype] = epoched
  146. return epochs
  148. def run_all(
  149. self,
  150. align_method: str,
  151. ) -> None:
  152. """Create and save epochs for all data types for all subjects"""
  153. subs = set()
  154. for info in self.infos.values():
  155. subs.update(info.subs)
  157. for sub in sorted(subs):
  158. epochs = self.make_sub(sub, align_method)
  159. for datatype, epoch in epochs.items():
  160. self._save(epoch, sub, self.save_roots[datatype], datatype)
  162. def _save(
  163. self,
  164. epochs: mne.Epochs,
  165. sub: str,
  166. save_root: str,
  167. datatype: str,
  168. ) -> None:
  170. save_dir = os.path.join(save_root)
  171. os.makedirs(save_dir, exist_ok=True)
  172. epochs.save(f'{save_dir}/sub-{sub}_{datatype}_epo.fif', overwrite=True)
  174. def _repr_html_(self):
  175. root = (
  176. '<ul style="list-style-type:none; padding-left:0;">' +
  177. ''.join([f'<li>{k} : {v} </li>' for k, v in self.roots.items()]) +
  178. '</ul>'
  179. )
  180. event_root = (
  181. '<ul style="list-style-type:none; padding-left:0;">' +
  182. ''.join([f'<li>{k} : {v} </li>' for k, v in self.event_roots.items()]) +
  183. '</ul>'
  184. )
  185. nSub = (
  186. '<ul style="list-style-type:none; padding-left:0;">' +
  187. ''.join([f'<li>{k} : {len(v.subs)} </li>' for k, v in self.infos.items()]) +
  188. '</ul>'
  189. )
  190. save_root = (
  191. '<ul style="list-style-type:none; padding-left:0;">' +
  192. ''.join([f'<li>{k} : {v} </li>' for k, v in self.save_roots.items()]) +
  193. '</ul>'
  194. )
  195. to_show = {
  196. 'rawRoot': root,
  197. 'eventRoot': event_root,
  198. 'nSub': nSub,
  199. 'eventId': self.event_id,
  200. 'timeMin': self.tmin,
  201. 'timeMax': self.tmax,
  202. 'lowFreq': self.lfreq,
  203. 'highFreq': self.hfreq,
  204. 'sampleFreq': self.sfreq,
  205. 'dataTypes': self.data_types,
  206. 'saveRoot': save_root,
  207. }
  208. to_show_df = pd.DataFrame(
  209. list(to_show.items()), columns=[
  210. '', 'NOD_MEEG-Epochor',
  211. ],
  212. )
  213. # escape=False to allow HTML in 'rawRoot'
  214. html_output = to_show_df.to_html(index=False, escape=False)
  216. return html_output
  219. # %%
  220. roots = {'meg': MEG_ROOT, 'eeg': EEG_ROOT}
  221. event_roots = {'meg': MEG_EVENT_ROOT, 'eeg': EEG_EVENT_ROOT}
  222. data_types = ['meg', 'eeg']
  223. save_root = {'meg': MEG_SAVE_ROOT, 'eeg': EEG_SAVE_ROOT}
  225. epochor = MakeEpochs(
  226. roots=roots,
  227. event_roots=event_roots,
  228. event_id=EVENT_ID,
  229. tmin=TMIN,
  230. tmax=TMAX,
  231. lfreq=LFREQ,
  232. hfreq=HFREQ,
  233. sfreq=SFREQ,
  234. data_types=data_types,
  235. save_roots=save_root,
  236. )
  238. epochor.run_all(align_method='info_with_data')
  239. # %%

step1c-epoching.py at commit 0fdcd9d, under MIT · at the source

Overview

Authors: Guohao Zhang1, Ming Zhou2, Shuyi Zhen2, Shaohua Tang3, Zheng Li4, Zonglei Zhen1,2
ORCID iDs: Guohao Zhang, Zonglei Zhen
  1. Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University,Beijing, 100875 China
  2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, 100875 China
  3. Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University,Zhuhai, 519087 China
  4. Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University,Zhuhai, 519087 China
Institutions: Beijing Normal University (China)
Journal: n/a, volume 12, issue 1, article 857
Dates: received 21 February 2025; accepted 9 May 2025; published online 23 May 2025
Type: Data paper · Language: English
License: none stated
Identifiers: DOI 10.1038/s41597-025-05174-7 · PMCID PMC12102372 · OpenAlex W4410631980
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fMRI (modality), MEG (modality), human (organism), methods / tools (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Perception, Object vision
MeSH: Brain*, Electroencephalography*, Magnetoencephalography*, Humans, Magnetic Resonance Imaging (* major topic)
Journal subjects: Data Descriptor
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (62433015, 31771251); Funder: STI 2030-Major Projects of the Ministry of Science and Technology of China Grant Reference Number: 2021ZD0200407
Citations: cited by 1 paper (Europe PMC); 58 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) 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 10 matches between paragraphs and lines of code.

BNUCNL/NaturalVisionProject

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: caea6fcb03df89c8e3839bb62f33f0f5e87f3dfa, 31 December 2021
Languages: MATLAB (26), Python (13), Jupyter (2)
Size: 65 files, 41 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (18 files), NumPy (14 files), pandas (11 files), Matplotlib (10 files), SciPy (10 files), Image Processing Toolbox (7 files), scikit-learn (7 files), PyTorch (5 files), NiBabel (2 files), h5py (1 file), OpenCV (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
42 files
At the source: github.com/BNUCNL/NaturalVisionProject

colehank/NOD-MEEG

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 0fdcd9d58c491bb3d36a53b24c1afe8f5607ab71, 15 July 2026
Languages: Python (32)
Size: 54 files, 32 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: MNE-Python (17 files), NumPy (17 files), Matplotlib (16 files), pandas (11 files), SciPy (8 files), MNE-BIDS (6 files), Pillow (3 files), seaborn (3 files), MEEGkit (2 files), Plotly (2 files), ICLabel (1 file), Keras (1 file), PyPREP (1 file), scikit-learn (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
34 files
At the source: github.com/colehank/NOD-MEEG

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The paper has a code availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41597-025-05174-7.

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Data

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 5 MeSH terms, 2 funders, 57 references.

Cite

This paper

Zhang, G., Zhou, M., Zhen, S., Tang, S., Li, Z., & Zhen, Z. (2025). A large-scale MEG and EEG dataset for object recognition in naturalistic scenes. Scientific Data, 12(1), 857. https://doi.org/10.1038/s41597-025-05174-7

BibTeX

@article{zhang2025large,
author = {Zhang, Guohao and Zhou, Ming and Zhen, Shuyi and Tang, Shaohua and Li, Zheng and Zhen, Zonglei},
title = {{A large-scale MEG and EEG dataset for object recognition in naturalistic scenes}},
journal = {Scientific Data},
year = {2025},
volume = {12},
number = {1},
pages = {857},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-025-05174-7},
url = {https://doi.org/10.1038/s41597-025-05174-7},
pmcid = {PMC12102372}
}

RIS

TY - JOUR
AU - Zhang, Guohao
AU - Zhou, Ming
AU - Zhen, Shuyi
AU - Tang, Shaohua
AU - Li, Zheng
AU - Zhen, Zonglei
TI - A large-scale MEG and EEG dataset for object recognition in naturalistic scenes
T2 - Scientific Data
J2 - Sci Data
PY - 2025
DA - 2025
VL - 12
IS - 1
SP - 857
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-025-05174-7
UR - https://doi.org/10.1038/s41597-025-05174-7
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41597-025-05174-7",
"type": "article-journal",
"title": "A large-scale MEG and EEG dataset for object recognition in naturalistic scenes",
"container-title": "Scientific Data",
"author": [
{
"family": "Zhang",
"given": "Guohao"
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{
"family": "Zhou",
"given": "Ming"
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{
"family": "Zhen",
"given": "Shuyi"
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{
"family": "Tang",
"given": "Shaohua"
},
{
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"given": "Zheng"
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{
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}
],
"container-title-short": "Sci Data",
"volume": "12",
"issue": "1",
"page": "857",
"DOI": "10.1038/s41597-025-05174-7",
"PMCID": "PMC12102372",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-025-05174-7",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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