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A neuroscientist's guide to neural burst detection.

Code ↔ Paper

11 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 11 matches
  1. [1] § Tutorials ↔ 00_timecourse_localization.py, lines 2–35 · score 0.82 · empty room recordings, noise covariance matrix, dSPM, MRI, space, model
  2. [2] § Tutorials › eBOSC ↔ Tutorials/eBOSC.ipynb, lines 37–72 · score 0.75 · post processing, eBOSC, power threshold, Model parameters, segments, background
  3. [3] § Tutorials › PAPTO ↔ 01a_PAPTO_find_events.py, lines 162–212 · score 0.65 · Morlet wavelet convolution, normalized TFR, offset, exponent, aperiodic, fits
  4. [4] § Overview of Burst Detection Methods › Amplitude-based methods ↔ Tutorials/eBOSC.ipynb, lines 37–72 · score 0.65 · duration threshold, eBOSC, power threshold, background, cycles, detection
  5. [5] § Tutorials › Sliding window matching ↔ Tutorials/Sliding_window_matching.ipynb, lines 18–29 · score 0.65 · Sliding window matching, longer windows, window length, burst duration
  6. [6] § Tutorials › Amplitude thresholding ↔ Utils/papto_functions.py, lines 292–393 · score 0.56 · Python equivalent, peak power, imregionalmax, TFR, band, threshold
  7. [7] § Tutorials › Amplitude thresholding ↔ Utils/spectralevents_functions.py, lines 222–323 · score 0.56 · Python equivalent, peak power, imregionalmax, TFR, band, threshold
  8. [8] § Overview of Burst Detection Methods › Motif-based methods ↔ Utils/bg_SWM.m, lines 4–63 · score 0.54 · Sliding Window Matching, SWM, sparse, iteratively, spacing, algorithms
  9. [9] § Overview of Burst Detection Methods › Multichannel motif-based methods ↔ Utils/utils_csc.py, lines 105–157 · score 0.54 · Convolutional Dictionary Learning, evoked, topographies, CDL, atoms, channel
  10. [10] § Tutorials › Time-delay embedding hidden Markov modeling (TDE-HMM) ↔ Tutorials/HMM.ipynb, lines 73–84 · score 0.53 · sequence length, hyperparameters, batch, epochs, HMM, Model
  11. [11] § Tutorials ↔ Tutorials/Sliding_window_matching.ipynb, lines 18–29 · score 0.52 · sliding window matching, 15–30 Hz, burst rates, bandpass, filtered, 15 Hz

Paper

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The authors' code

Jupyter notebook · 151 lines · 6.8 KB · no license · 2 matches

  1. # %% [markdown]
  2. # ## eBOSC Tutorial
  3. #
  4. # The code used in this script is modified from https://github.com/jkosciessa/eBOSC_py
  5. #
  6. # This method takes in a 1-dimensional time series of preprocessed electrophysiological data. A dataframe containing the characteristics (timing, frequency, power, etc.) of each detected burst is returned.
  8. # %%
  9. # Imports
  10. import numpy as np
  11. import matplotlib.pyplot as plt
  12. import pandas as pd
  13. import os
  14. from ebosc.helpers import get_project_root
  15. from ebosc.eBOSC_wrapper import eBOSC_wrapper
  16. import scipy.io as io
  17. from papto_functions import *
  18. import time
  20. # %%
  21. # Read in one-dimensional time series (preprocessed)
  22. file_name = 'sub-0002_SomMotA_4_R.mat'
  23. scout_mean_timecourses = io.loadmat(file_name)
  24. raw_data = np.squeeze(scout_mean_timecourses['Value'])
  26. print(raw_data.shape)
  28. # %%
  29. # Configure data to pandas dataframe format expected by eBOSC algorithm
  30. Fs = 250
  31. region = 'SomMotA_4 R'
  32. times = np.arange(0,len(raw_data))/ Fs
  33. data = pd.DataFrame({'time':times, 'condition':[1]*len(times), 'epoch':[0]*len(times),region:raw_data})
  35. data.head(10)
  37. # %% [markdown]
  38. # ### 1. Set Model Parameters
  39. #
  40. # The eBOSC method takes in a dictionary containing information about the parameters and settings of the model.
  41. #
  42. # Important parameters to note include the 'F' parameter which specifies the frequency sample of interest, 'fsample' which must be set to the sampling rate of your data, and the threshold parameters which can be modified to influence the sensitivity of the model.
  43. #
  44. # More information on these parameters can found at https://github.com/jkosciessa/eBOSC_py
  46. # %%
  47. start_time = time.time()
  49. # eBOSC parameters
  50. cfg_eBOSC = dict()
  51. cfg_eBOSC['F'] = np.arange(1,30,1) # frequency sampling (we only want 15-30 Hz but in order to create PSD we need low frequencies)
  52. cfg_eBOSC['wavenumber'] = 6 # wavelet parameter (time-frequency tradeoff)
  53. cfg_eBOSC['fsample'] = Fs # current sampling frequency of MEG data
  54. cfg_eBOSC['pad.tfr_s'] = 1 # padding following wavelet transform to avoid edge artifacts in seconds (bi-lateral)
  55. cfg_eBOSC['pad.detection_s'] = .5 # padding following rhythm detection in seconds (bi-lateral); 'shoulder' for BOSC eBOSC.detected matrix to account for duration threshold
  56. cfg_eBOSC['pad.background_s'] = 1 # padding of segments for BG (only avoiding edge artifacts)
  58. # Threshold settings
  59. cfg_eBOSC['threshold.excludePeak'] = np.array([[8,15]]) # lower and upper bound of frequencies to be excluded during background fit (Hz)
  60. cfg_eBOSC['threshold.duration'] = np.array([2]*len(cfg_eBOSC['F'])).reshape(1,len(cfg_eBOSC['F'])) # vector of duration thresholds at each frequency (number of cycles, shape (1,number of freqs))
  61. cfg_eBOSC['threshold.percentile'] = .95 # percentile of background fit for power threshold
  63. # Rhythmic episode post-processing
  64. cfg_eBOSC['postproc.use'] = 'yes' # Post-processing of rhythmic eBOSC.episodes, i.e., wavelet 'deconvolution' (default = 'no')
  65. cfg_eBOSC['postproc.method'] = 'FWHM' # Deconvolution method (default = 'MaxBias', FWHM: 'FWHM')
  66. cfg_eBOSC['postproc.edgeOnly'] = 'yes' # Deconvolution only at on- and offsets of eBOSC.episodes? (default = 'yes')
  67. cfg_eBOSC['postproc.effSignal'] = 'PT' # Power deconvolution on whole signal or signal above power threshold? (default = 'PT')
  69. # General processing settings
  70. cfg_eBOSC['channel'] = [region] # select channel of interest
  71. cfg_eBOSC['trial'] = [] # select trials (default: all, indicate in natural trial number (not zero-starting))
  72. cfg_eBOSC['trial_background'] = [] # select trials for background (default: all, indicate in natural trial number (not zero-starting))
  74. # %% [markdown]
  75. # ### 2. Run eBOSC Detection
  76. #
  77. # The eBOSC method has a fully-integrated python package and therefore does not require manual processing steps or locally-stored utility functions. Calling the eBOSC_wrapper function as below will perform all necessary processing steps: calculating the TFR, fitting the aperiodic component and applying thresholds to detect bursts in the data.
  78. #
  79. # The function returns an eBOSC object containing information about the various outputs of the model. eBOSC['episodes'] provides information about the specific characteristics of the detected bursts.
  80. #
  81. # For more information on the specific processing steps completed by this function see: https://github.com/jkosciessa/eBOSC_py
  83. # %%
  84. # eBOSC method has some soon to be deprecated functions - keep this in mind but suppress for now to truncate output
  85. import warnings
  86. warnings.simplefilter(action='ignore', category=DeprecationWarning)
  88. # Run eBOSC
  89. [eBOSC, cfg] = eBOSC_wrapper(cfg_eBOSC, data)
  91. # %%
  92. # Find episodes with a beta (15-30 Hz) Frequency
  93. burst_df = pd.DataFrame(eBOSC['episodes'])
  95. peak_freq_list = []
  96. for i in range(0,len(burst_df)):
  97. peak_power_ind = np.argmax(burst_df['Power'].tolist()[i])
  98. peak_freq = (burst_df['Frequency'].tolist()[i])[peak_power_ind]
  99. peak_freq_list.append(peak_freq)
  100. burst_df['Peak Frequency'] = peak_freq_list
  101. burst_df['Event Duration'] = burst_df['DurationS']
  102. burst_df['Peak Time'] = burst_df['Onset']+(burst_df['Event Duration']/2)
  104. burst_df = burst_df[burst_df['Peak Frequency']>=15]
  105. burst_df = burst_df[burst_df['Peak Frequency']<=30]
  107. burst_df.head(5)
  109. # %%
  110. # Record burst rate
  111. burstRate = burst_df.shape[0]/(raw_data.shape[0]/Fs)
  112. print("Burst Rate:")
  113. print(burstRate)
  115. # %%
  116. # Make a TFR for data
  117. # TFR params #
  118. fmin = 15.0 # Hertz (integer)
  119. fmax = 30.0 # Hertz (integer)
  120. fstep = 1.0 # Hertz (integer)
  121. fVec = np.arange(fmin, fmax+1, fstep)
  122. chan_data = np.reshape(raw_data, (raw_data.shape[0],1)) #reshapes because function expects epochs
  123. TFR, tVec = TFR_via_morlet_wavelet(chan_data, fVec, Fs)
  125. # Plot showing where the bursts are identified in the TFR along with a visualization of the corresponding times in the raw signal
  126. fig, axs = plt.subplots(2,1, figsize=(15,4))
  127. scale_val = 20
  129. # Plot TFR with overlaid burst times/frequencies
  130. TFR_crop = np.asarray(TFR)[:,:,66500:68500]
  131. im = axs[0].pcolor(tVec[66500:68500], fVec, np.squeeze(TFR_crop), cmap='jet')
  132. axs[0].scatter(burst_df['Peak Time'], burst_df['Peak Frequency'] ,c='black',s=scale_val,zorder=1)
  133. axs[0].set_xlim(266,274)
  134. axs[0].set_ylim(15,30)
  136. # Plot indication of burst peak times with respect to raw (bandpassed) time series
  137. beta_filt_dat = mne.filter.filter_data(np.squeeze(chan_data), Fs, l_freq=15.0, h_freq=30.0,verbose=False)
  138. times = np.arange(0,chan_data.shape[0])/Fs
  139. axs[1].plot(times,beta_filt_dat,zorder=0,c='black')
  140. axs[1].scatter(burst_df['Peak Time'], [5]*len(burst_df['Peak Time']) ,c='lightseagreen',s=scale_val,zorder=1)
  141. axs[1].set_ylim(-6,6)
  142. axs[1].set_xlim(266,274)
  143. plt.xlabel('Time (s)')
  145. plt.show()
  147. # %%
  148. print("Total run time: %s seconds" % (time.time() - start_time))
  150. # %%
  151. burst_df.to_csv('ebosc_bursts.csv')

eBOSC.ipynb at commit 54961dc, no license · at the source

Overview

Authors: Lindsey Power1, Idil Aydin1, Timothy Bardouille2, Sylvain Baillet1,3,4
ORCID iDs: Lindsey Power, Idil Aydin
  1. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
  2. Department of Physics & Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
  3. Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montreal, Quebec, Canada
  4. Department of Neuroscience, Université de Montréal, Montreal, Quebec, Canada
Institutions: Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); Dalhousie University (Canada); Centre Hospitalier de l’Université de Montréal (Canada); Université de Montréal (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1226
Dates: received 31 October 2025; accepted 8 April 2026; published online 30 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1226 · PMID 42100763 · PMCID PMC13146973 · OpenAlex W7154471852
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging, Connectivity, Statistics
Keywords: neural bursts, electrophysiology, transient events, methods, tutorial
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (R01‐EB026299); Canadian Institutes of Health Research (Postdoctoral Fellowship:193925); Natural Sciences and Engineering Research Council of Canada (436355-13, 2024-03788)
Citations: not cited yet (Europe PMC); 85 references in the paper

Abstract

Neural bursts—brief episodes of heightened oscillatory activity—are increasingly studied as fundamental building blocks of brain function, with relevance to perception, cognition, and disease. As such, detecting and characterizing these bursts in electrophysiological recordings have driven rapid methodological innovation in neuroscience research. However, the growing number of analysis techniques can be overwhelming, making it difficult for researchers to select the most appropriate method for their specific goals. In this review, we provide a structured and practical guide for neuroscientists to measure and interpret neural burst data. We offer an overview of current detection methods, accompanied by a suite of tutorials, including code notebooks and data, to enable concrete implementation and critical evaluation. We then conclude with actionable recommendations to help researchers select the best burst detection strategy in diverse research contexts. This guide is intended for newcomers to the field as well as more experienced neuroscientists seeking to expand their methodological toolkit.

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 11 matches between paragraphs and lines of code.

tbardouille/papto_camcan

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 97314d619b21ec43d1586697ae2746b04104271f, 11 February 2022
Languages: Python (7)
Size: 9 files, 7 scripts
Software Heritage: archived
Found in: the resources table
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (7 files), MNE-Python (7 files), NumPy (7 files), pandas (6 files), SciPy (6 files), seaborn (6 files), specparam (formerly FOOOF) (5 files), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/tbardouille/papto_camcan

voytekresearch/Cole_2018_cyclebycycle

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 9da87d0e8daecf087dd755115997be39b9788f8b, 25 May 2020
Languages: Jupyter (6), Python (1)
Size: 12 files, 7 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, environment (environment.yml), 6 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: bycycle (6 files), Matplotlib (6 files), NeuroDSP (6 files), NumPy (6 files), pandas (6 files), seaborn (6 files), SciPy (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/voytekresearch/Cole_2018_cyclebycycle

lindseypower/BurstDetection_Tutorials

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 54961dc1421ace0fd0032dd310aa61744f36d8e0, 21 April 2026
Languages: Jupyter (7), Python (4), MATLAB (3)
Size: 17 files, 14 scripts
Software Heritage: not archived
Found in: the text, “Tutorials”
Holds: README, 7 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), SciPy (9 files), Matplotlib (8 files), MNE-Python (8 files), pandas (8 files), seaborn (5 files), FieldTrip (2 files), Statistics and Machine Learning Toolbox (2 files), MNE-BIDS (2 files), scikit-learn (2 files), bycycle (1 file), specparam (formerly FOOOF) (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
15 files
At the source: github.com/lindseypower/BurstDetection_Tutorials

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

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 28 scripts, each with its path and the digest of its content;
  • 11 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 and Code Availability

Data used in this work are freely available at: https://openneuro.org/datasets/ds000247/versions/00001. Tutorial code is available at: https://github.com/lindseypower/BurstDetection_Tutorials.

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, pages, dates, 4 authors, 5 keywords, 3 funders, 83 references.

Cite

This paper

Power, L., Aydin, I., Bardouille, T., & Baillet, S. (2026). A neuroscientist's guide to neural burst detection. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1226. https://doi.org/10.1162/imag.a.1226

BibTeX

@article{power2026neuroscientist,
author = {Power, Lindsey and Aydin, Idil and Bardouille, Timothy and Baillet, Sylvain},
title = {{A neuroscientist's guide to neural burst detection}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1226},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1226},
url = {https://doi.org/10.1162/imag.a.1226},
pmid = {42100763},
pmcid = {PMC13146973}
}

RIS

TY - JOUR
AU - Power, Lindsey
AU - Aydin, Idil
AU - Bardouille, Timothy
AU - Baillet, Sylvain
TI - A neuroscientist's guide to neural burst detection
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/30
VL - 4
SP - IMAG.a.1226
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1226
UR - https://doi.org/10.1162/imag.a.1226
LA - en
ER -

CSL-JSON

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