OSCR

Novel 4 He-OPMs support waveform-specific beta burst analysis comparable to SQUID-MEG

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Burst detection ↔ python/burst_detection.py, lines 264–330 · score 0.86 · aperiodic spectrum, event related, burst detection, peak frequency, extracted burst, burst waveforms
  2. [2] § Methods › Burst detection ↔ matlab/extract_bursts_single_trial.m, lines 43–116 · score 0.84 · noise floor, aperiodic spectrum, single trial TF, peak frequency, extracted burst, Gaussian
  3. [3] § Methods › Data analysis ↔ A2_OPM_preproc.py, lines 266–314 · score 0.82 · linear regression, reference regressor, denoise, RMS, dtw, warping
  4. [4] § Methods › Burst analysis ↔ B4_SQUID_BurstMetrics.py, lines 128–195 · score 0.67 · bin width, burst duration, peak frequency, burst rate, timecourses, amplitudes
  5. [5] § Results › Beta burst metrics ↔ B5_OPM_BurstMetrics.py, lines 173–252 · score 0.60 · burst durations, Peak amplitude, Peak frequency, collapsed, metrics, motor
  6. [6] § Results › Beta burst metrics ↔ B4_SQUID_BurstMetrics.py, lines 128–195 · score 0.58 · burst durations, Peak amplitude, Peak frequency, metrics, motor, SQUID
  7. [7] § Methods › SNR ↔ C5_Fig6.py, lines 130–169 · score 0.55 · 500–1500 ms, percent baseline, burst rate, PC, sensor, SQUID
  8. [8] § Methods › Burst analysis ↔ C3_Fig4.py, lines 261–344 · score 0.52 · temporal cluster, slope, spatio, permutation, binning, burst rate

Paper

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

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The authors' code

Python · 247 lines · 8.6 KB · no license · 2 matches

  1. import pickle
  2. import os.path as op
  3. import numpy as np
  4. from os import listdir
  5. import mne
  6. from scipy.ndimage import gaussian_filter
  8. group_path = '/sps/cermep/opm/NEW_MEG/HV/group/Motor/'
  10. print('Loading data')
  11. #Get subject files and sort by type
  12. VIS_SQUID_bursts = [d for d in listdir(op.join(group_path,'bursts')) if 'SQUID_VIS' in d]
  13. MOT_SQUID_bursts = [d for d in listdir(op.join(group_path,'bursts')) if 'SQUID_MOT' in d]
  14. VIS_SQUID_bursts.sort()
  15. MOT_SQUID_bursts.sort()
  17. print('Getting SQUID waveforms')
  18. waveforms = []
  19. for vis_file in VIS_SQUID_bursts:
  20. file = open(op.join(group_path,'bursts',vis_file), 'rb')
  21. bs = pickle.load(file)
  22. for i in range(len(bs)):
  23. am_bs = len(bs[i]["waveform"])
  24. bs_samp_ix = np.random.choice(np.arange(am_bs), int(am_bs))
  25. bs_samp = np.vstack(bs[i]['waveform'])[bs_samp_ix, :]
  26. waveforms.append(bs_samp)
  28. waveforms = np.vstack(waveforms)
  29. waveforms_medians = np.median(waveforms, axis=1)
  31. print('Collecting subject metrics')
  32. #create subject metrics
  33. sub_metrics = {}
  34. for (vis,mot) in zip(VIS_SQUID_bursts, MOT_SQUID_bursts):
  35. pp_ix1=vis[:2]
  36. pp_ix2=mot[:2]
  37. if not pp_ix1==pp_ix2:
  38. print('File order mismatch, please check burst files')
  39. break
  40. file = open(op.join(group_path,'bursts',vis), 'rb')
  41. bs_vis = pickle.load(file)
  42. file = open(op.join(group_path,'bursts',mot), 'rb')
  43. bs_mot = pickle.load(file)
  45. metrics = {
  46. "vis": {
  47. "waveform": [],
  48. "peak_time": [],
  49. "peak_freq": [],
  50. "peak_amp_base": [],
  51. "fwhm_freq": [],
  52. "fwhm_time": [],
  53. "peak_adjustment": [],
  54. "trial": [],
  55. "pp_ix": [],
  56. },
  57. "mot": {
  58. "waveform": [],
  59. "peak_time": [],
  60. "peak_freq": [],
  61. "peak_amp_base": [],
  62. "fwhm_freq": [],
  63. "fwhm_time": [],
  64. "peak_adjustment": [],
  65. "trial": [],
  66. "pp_ix": [],
  67. }
  68. }
  69. #VIS:
  70. #collapse over channels
  71. wf_vis=[]
  72. for ch in range(len(bs_vis)):
  73. wf_vis.append(np.array(bs_vis[ch]['waveform']))
  74. wf_vis=np.vstack(wf_vis)
  76. #clear top and bottom 1 percentile (overall)
  77. wf_median = np.median(wf_vis, axis=1)
  78. wf_ixs = np.where(
  79. (wf_median > np.percentile(waveforms_medians, 1)) &
  80. (wf_median < np.percentile(waveforms_medians, 99)))[0]
  81. wf_vis = wf_vis[wf_ixs,:]
  83. #fill in the metrics, omitting omitted bursts
  84. metrics['vis']["waveform"].append(wf_vis)
  85. metrics['vis']["waveform"]=np.vstack(metrics['vis']["waveform"])
  86. for k in ["peak_time", "peak_amp_base", "fwhm_freq", "fwhm_time", "peak_freq", "trial"]:
  87. tmp=[]
  88. for ch in range(len(bs_vis)):
  89. tmp.append(np.array(bs_vis[ch][k]))
  90. tmp=np.hstack(tmp)
  91. metrics['vis'][k].append(tmp[wf_ixs])
  92. metrics['vis'][k]=np.vstack(metrics['vis'][k])
  93. metrics['vis']['pp_ix']=pp_ix1
  95. #MOT:
  96. #collapse over channels
  97. wf_mot=[]
  98. for ch in range(len(bs_mot)):
  99. wf_mot.append(np.array(bs_mot[ch]['waveform']))
  100. wf_mot=np.vstack(wf_mot)
  102. #clear top and bottom 1 percentile (overall)
  103. wf_median = np.median(wf_mot, axis=1)
  104. wf_ixs = np.where(
  105. (wf_median > np.percentile(waveforms_medians, 1)) &
  106. (wf_median < np.percentile(waveforms_medians, 99)))[0]
  107. wf_mot = wf_mot[wf_ixs,:]
  109. #fill in the metrics, omitting omitted bursts
  110. metrics['mot']["waveform"].append(wf_mot)
  111. metrics['mot']["waveform"]=np.vstack(metrics['mot']["waveform"])
  112. for k in ["peak_time", "peak_amp_base", "fwhm_freq", "fwhm_time", "peak_freq", "trial"]:
  113. tmp=[]
  114. for ch in range(len(bs_mot)):
  115. tmp.append(np.array(bs_mot[ch][k]))
  116. tmp=np.hstack(tmp)
  117. metrics['mot'][k].append(tmp[wf_ixs])
  118. metrics['mot'][k]=np.vstack(metrics['mot'][k])
  119. metrics['mot']['pp_ix']=pp_ix2
  121. sub_metrics[pp_ix1] = metrics
  123. #save metrics
  124. print('Saving subject metrics')
  125. sub_metrics_path = op.join(group_path, "SQUID_sub_metrics.pkl")
  126. pickle.dump(sub_metrics, open(sub_metrics_path, "wb"))
  128. #Now create group averages
  129. #We need to know the trial timecourse, so load an evoked for both conditions and extract times
  130. tmp=mne.read_evokeds(op.join(group_path,'evoked','01_VIS_MEG-ave.fif'))
  131. vis_times=tmp[0].times
  132. tmp=mne.read_evokeds(op.join(group_path,'evoked','01_MOT_MEG-ave.fif'))
  133. mot_times=tmp[0].times
  135. print('aggregating metrics')
  136. vis_wf=np.zeros([len(sub_metrics.keys()),np.shape(sub_metrics['01']['vis']['waveform'])[1]])
  137. mot_wf=np.zeros([len(sub_metrics.keys()),np.shape(sub_metrics['01']['mot']['waveform'])[1]])
  138. vis_burst_rate = []
  139. mot_burst_rate = []
  140. vis_peak_freq = []
  141. mot_peak_freq = []
  142. vis_peak_amp = []
  143. mot_peak_amp = []
  144. vis_fwhm_time = []
  145. mot_fwhm_time = []
  146. vis_fwhm_freq = []
  147. mot_fwhm_freq = []
  148. for ind,s in enumerate(sub_metrics.keys()):
  149. vis_wf[ind,]=np.average(sub_metrics[s]['vis']['waveform'],axis=0)
  150. mot_wf[ind,]=np.average(sub_metrics[s]['mot']['waveform'],axis=0)
  152. #burst rate
  153. buffer = 0.125
  154. bin_width=0.05
  155. baseline_range = [-0.5, -0.25]
  156. visual_time_bins = np.arange(vis_times[0] + buffer, vis_times[-1] - buffer, bin_width)
  157. vis_hist, t_bin_edges = np.histogram(sub_metrics[s]['vis']['peak_time'],bins=visual_time_bins)
  158. vis_hist = vis_hist / bin_width
  159. br_vis=gaussian_filter(vis_hist, 1)
  161. motor_time_bins = np.arange(mot_times[0] + buffer, mot_times[-1] - buffer, bin_width)
  162. mot_hist, t_bin_edges = np.histogram(sub_metrics[s]['mot']['peak_time'],bins=motor_time_bins)
  163. mot_hist = mot_hist / bin_width
  164. br_mot=gaussian_filter(mot_hist, 1)
  166. #now baseline burst rate
  167. vis_time_plot = visual_time_bins[:-1]
  168. mot_time_plot = motor_time_bins[:-1]
  169. baseline_ixs = np.where(
  170. (vis_time_plot >= baseline_range[0]) &
  171. (vis_time_plot <= baseline_range[-1]))
  172. base_bursts = np.mean(br_vis[baseline_ixs])
  173. br_vis = (br_vis - base_bursts) / base_bursts
  174. br_mot = (br_mot - base_bursts) / base_bursts
  176. vis_burst_rate.append(br_vis)
  177. mot_burst_rate.append(br_mot)
  179. #peak_freq: peak frequency distribution
  180. vis_peak_freq.append(sub_metrics[s]['vis']['peak_freq'])
  181. mot_peak_freq.append(sub_metrics[s]['mot']['peak_freq'])
  183. #peak_amp_base: peak amplitude distribution
  184. vis_peak_amp.append(sub_metrics[s]['vis']['peak_amp_base'])
  185. mot_peak_amp.append(sub_metrics[s]['mot']['peak_amp_base'])
  187. #fwhm_time: burst duration distribution
  188. vis_fwhm_time.append(sub_metrics[s]['vis']['fwhm_time'])
  189. mot_fwhm_time.append(sub_metrics[s]['mot']['fwhm_time'])
  191. #fwhm_freq: frequency span distribution
  192. vis_fwhm_freq.append(sub_metrics[s]['vis']['fwhm_freq'])
  193. mot_fwhm_freq.append(sub_metrics[s]['mot']['fwhm_freq'])
  195. print('Everything sorted.')
  197. #calculate burst rate average and SEM
  198. print('Elaborating burst rate')
  199. brate_mean_vis = np.mean(vis_burst_rate, axis=0)
  200. brate_sem_vis = np.std(vis_burst_rate, axis=0) / np.sqrt(np.shape(vis_burst_rate)[0])
  201. brate_mean_mot = np.mean(mot_burst_rate, axis=0)
  202. brate_sem_mot = np.std(mot_burst_rate, axis=0) / np.sqrt(np.shape(vis_burst_rate)[0])
  205. #collapse all data for the histograms
  206. print('Flattening some stuff')
  207. vis_peak_freq=np.hstack(vis_peak_freq).flatten()
  208. mot_peak_freq=np.hstack(mot_peak_freq).flatten()
  209. vis_peak_amp=np.hstack(vis_peak_amp).flatten()
  210. mot_peak_amp=np.hstack(mot_peak_amp).flatten()
  211. mot_peak_freq=np.hstack(mot_peak_freq).flatten()
  212. vis_fwhm_time=np.hstack(vis_fwhm_time).flatten()
  213. mot_fwhm_time=np.hstack(mot_fwhm_time).flatten()
  214. vis_fwhm_freq=np.hstack(vis_fwhm_freq).flatten()
  215. mot_fwhm_freq=np.hstack(mot_fwhm_freq).flatten()
  217. #Putting all eggs in one basket
  218. group_metrics = {"vis": {
  219. "waveform": vis_wf,
  220. "burst_rate_av": brate_mean_vis,
  221. "burst_rate": vis_burst_rate,
  222. "burst_rate_sem": brate_sem_vis,
  223. "burst_rate_time": vis_time_plot,
  224. "peak_freq": vis_peak_freq,
  225. "peak_amp": vis_peak_amp,
  226. "fwhm_freq": vis_fwhm_freq,
  227. "fwhm_time": vis_fwhm_time,
  228. },
  229. "mot": {
  230. "waveform": mot_wf,
  231. "burst_rate_av": brate_mean_mot,
  232. "burst_rate": mot_burst_rate,
  233. "burst_rate_sem": brate_sem_mot,
  234. "burst_rate_time": mot_time_plot,
  235. "peak_freq": mot_peak_freq,
  236. "peak_amp": mot_peak_amp,
  237. "fwhm_freq": mot_fwhm_freq,
  238. "fwhm_time": mot_fwhm_time,
  239. }
  240. }
  244. print('Saving results')
  245. group_metrics_path = op.join(group_path, "SQUID_group_metrics.pkl")
  246. pickle.dump(group_metrics, open(group_metrics_path, "wb"))
  247. print('All done.')

B4_SQUID_BurstMetrics.py at commit fb03737, no license · at the source

Overview

Authors: Tjerk P Gutteling1,2, Maciej J Szul3,4, Sébastien Daligault1, Etienne Labyt5, Julien Jung6, James J Bonaiuto3,4, Denis Schwartz1,2
ORCID iDs: Tjerk P Gutteling, Denis Schwartz
  1. CERMEP-Imagerie du Vivant, MEG Departement, Lyon, France
  2. Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028, UMR5292, COPHY, Bron, France
  3. Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
  4. Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
  5. MAG4Health, Grenoble, France
  6. Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028, UMR5292, EDUWELL, Bron, France
Institutions: Lyon 1 Université (France); Centre National de la Recherche Scientifique (France); Inserm (France); Centre de Recherche en Neurosciences de Lyon (France); Centre d'Exploration et de Recherche Médicale par Emission de Positons (France); Calcul, Cognition et Neurophysiologie (France); Université de Lyon (France); Institut des Sciences Cognitives Marc Jeannerod (France)
Journal: n/a, volume 3, article IMAG.a.1040
Dates: received 17 December 2024; accepted 4 November 2025; published online 2 December 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1162/imag.a.1040 · PMCID PMC12673217 · OpenAlex W4417076774
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: OPM, MEG, beta bursts, waveforms, reaching
Topic: Atomic and Subatomic Physics Research (Atomic and Molecular Physics, and Optics, Physics and Astronomy), according to OpenAlex
Funding: European Research Council (864550)
Citations: not cited yet (Europe PMC); 47 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 8 matches between paragraphs and lines of code.

tgutteling/OPM_Motor

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: fb03737ebd462588cea73449b915953d30ce7b13, 27 November 2024
Languages: Python (14)
Size: 15 files, 14 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (14 files), MNE-Python (13 files), Matplotlib (10 files), scikit-learn (5 files), SciPy (5 files), autoreject (2 files), specparam (formerly FOOOF) (2 files), MEEGkit (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
15 files
At the source: github.com/tgutteling/OPM_Motor

danclab/burst_detection

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 91fe42487dc701a1f60f7b6ef821c97b86ce506e, 8 February 2024
Languages: MATLAB (5), Python (2), Jupyter (2)
Size: 11 files, 9 scripts
Software Heritage: archived
Found in: “Data and Code Availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), Matplotlib (2 files), MNE-Python (2 files), FieldTrip (1 file), Signal Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file), specparam (formerly FOOOF) (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
10 files
At the source: github.com/danclab/burst_detection

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;
  • 23 scripts, each with its path and the digest of its content;
  • 8 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

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Code and data availability statement

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Read it in the paper: doi.org/10.1162/imag.a.1040.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 1 funder, 47 references.

Cite

This paper

Gutteling, T. P., Szul, M. J., Daligault, S., Labyt, E., Jung, J., Bonaiuto, J. J., & Schwartz, D. (2025). Novel 4 He-OPMs support waveform-specific beta burst analysis comparable to SQUID-MEG. Imaging Neuroscience, 3, IMAG.a.1040. https://doi.org/10.1162/imag.a.1040

BibTeX

@article{gutteling2025novel,
author = {Gutteling, Tjerk P and Szul, Maciej J and Daligault, Sébastien and Labyt, Etienne and Jung, Julien and Bonaiuto, James J and Schwartz, Denis},
title = {{Novel 4 He-OPMs support waveform-specific beta burst analysis comparable to SQUID-MEG}},
journal = {Imaging Neuroscience},
year = {2025},
volume = {3},
pages = {IMAG.a.1040},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1040},
url = {https://doi.org/10.1162/imag.a.1040},
pmcid = {PMC12673217}
}

RIS

TY - JOUR
AU - Gutteling, Tjerk P
AU - Szul, Maciej J
AU - Daligault, Sébastien
AU - Labyt, Etienne
AU - Jung, Julien
AU - Bonaiuto, James J
AU - Schwartz, Denis
TI - Novel 4 He-OPMs support waveform-specific beta burst analysis comparable to SQUID-MEG
T2 - Imaging Neuroscience
J2 - Imaging Neurosci (Camb)
PY - 2025
DA - 2025
VL - 3
SP - IMAG.a.1040
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1040
UR - https://doi.org/10.1162/imag.a.1040
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1040",
"type": "article-journal",
"title": "Novel 4 He-OPMs support waveform-specific beta burst analysis comparable to SQUID-MEG",
"container-title": "Imaging Neuroscience",
"author": [
{
"family": "Gutteling",
"given": "Tjerk P"
},
{
"family": "Szul",
"given": "Maciej J"
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{
"family": "Daligault",
"given": "Sébastien"
},
{
"family": "Labyt",
"given": "Etienne"
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{
"family": "Jung",
"given": "Julien"
},
{
"family": "Bonaiuto",
"given": "James J"
},
{
"family": "Schwartz",
"given": "Denis"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "3",
"page": "IMAG.a.1040",
"DOI": "10.1162/imag.a.1040",
"PMCID": "PMC12673217",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1040",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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