OSCR

Neurocognitive Dynamics of Translating Information From a Spatial Map Into Action.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › EEG Acquisition and Processing ↔ Rotation_theta_bursts/Clement_bemobil_config_script_ivtt.m, lines 105–157 · score 0.94 · channel noise, residual variance, ICLabel, Lite, algorithm, classes
  2. [2] § Methods › Statistical Analyses ↔ Code_for_behavioral_analyses/gamm_angleXperspective.Rmd, lines 102–114 · score 0.59 · varPower, nlme, mgcv, ML, GAMMs, fitted
  3. [3] § Methods › EEG Acquisition and Processing ↔ Merged_examples/Merging_data_iVTT.m, lines 1–29 · score 0.55 · EEGLAB, LSL, bit, pipeline, BeMobil, MATLAB
  4. [4] § Methods › Statistical Analyses ↔ Code_for_behavioral_analyses/main_code_extraction_behavioural_data.m, lines 2–101 · score 0.50 · iVTT, behavioral, rotation angle, angular error, absolute

Paper

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

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The authors' code

MATLAB · 174 lines · 9.3 KB · no license · 1 match

  1. clear bemobil_config
  2. %% General Setup
  3. % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
  4. % foldernames (NEED to have a filesep at the end, sorry!)
  5. % !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
  6. bemobil_config.filename_prefix = 'sub-';
  7. % ------------ IMPORTANT --------------
  8. % Adapt this data path to the place where you put the example data!
  9. bemobil_config.study_folder = 'C:\Users\cleme\Desktop\iVTT\';
  10. % ------------ IMPORTANT --------------
  11. bemobil_config.source_data_folder = ['0_source-data' filesep];
  12. bemobil_config.bids_data_folder = ['1_BIDS-data' filesep];
  13. bemobil_config.raw_EEGLAB_data_folder = ['2_raw-EEGLAB' filesep];
  14. bemobil_config.EEG_preprocessing_data_folder = ['3_EEG-preprocessing' filesep];
  15. bemobil_config.spatial_filters_folder = ['4_spatial-filters' filesep];
  16. bemobil_config.spatial_filters_folder_AMICA = ['4-1_AMICA' filesep];
  17. bemobil_config.single_subject_analysis_folder = ['5_single-subject-EEG-analysis' filesep];
  18. bemobil_config.motion_analysis_folder = ['6_single-subject-motion-analysis' filesep];
  19. % filenames
  20. bemobil_config.merged_filename = 'merged_EEG.set';
  21. bemobil_config.basic_prepared_filename = 'basic_prepared.set';
  22. bemobil_config.preprocessed_filename = 'preprocessed.set';
  23. bemobil_config.filtered_filename = 'filtered.set';
  24. bemobil_config.amica_filename_output = 'AMICA.set';
  25. bemobil_config.dipfitted_filename = 'dipfitted.set';
  26. bemobil_config.preprocessed_and_ICA_filename = 'preprocessed_and_ICA.set';
  27. bemobil_config.single_subject_cleaned_ICA_filename = 'cleaned_with_ICA.set';
  28. bemobil_config.merged_motion_filename = 'merged_MOTION.set';
  29. bemobil_config.processed_motion_filename = 'motion_processed.set';
  30. %% Preprocessing
  31. % enter channels that you did not use at all (e.g. with our custom MoBI 160 chan layout, only 157 chans are used), leave
  32. % empty, if all channels are used
  33. % process_config.channels_to_remove = {'N29' 'N30' 'N31'};
  34. bemobil_config.channels_to_remove = [];
  35. % enter EOG channel names here:
  36. % bemobil_config.eog_channels = {'VEOG', 'HEOG'};
  37. bemobil_config.eog_channels = {'EOG'};
  38. % if you add a channel here it needs to have a location as well. this means a new channel will be created and the old
  39. % reference will be back in the dataset. if you don't know or don't need the reference, leave this empty
  40. %bemobil_config.ref_channel = 'CPz';
  41. bemobil_config.ref_channel = [];
  42. % If all channels have a prefix it can be removed here, by entering a single char in the cell array. it's also possible
  43. % to rename single channels here if needed. for this, enter a matrix of channel names (nbchans,2 (from->to))
  44. bemobil_config.rename_channels = {};
  45. % resample frequency during preprocessing (leave empty if you resample before, or your data is already correctly
  46. % sampled)
  47. bemobil_config.resample_freq = 250;
  48. % bemobil_config.resample_freq = [];
  49. % automatic channel cleaning:
  50. % chancorr_crit - Correlation threshold. If a channel is correlated at less than this value
  51. % to its robust estimate (based on other channels), it is considered abnormal in
  52. % the given time window. OPTIONAL, default = 0.8.
  53. % chan_max_broken_time - Maximum time (either in seconds or as fraction of the recording) during which a
  54. % retained channel may be broken. Reasonable range: 0.1 (very aggressive) to 0.6
  55. % (very lax). OPTIONAL, default = 0.5.
  56. % chan_detect_num_iter - Number of iterations the bad channel detection should run (default = 10)
  57. % chan_detected_fraction_threshold - Fraction how often a channel has to be detected to be rejected in the final
  58. % rejection (default 0.5)
  59. % flatline_crit - Maximum duration a channel can be flat in seconds (default 'off')
  60. % line_noise_crit - If a channel has more line noise relative to its signal than this value, in
  61. % standard deviations based on the total channel population, it is considered
  62. % abnormal. (default: 'off')
  63. % num_chan_rej_max_target - Target max amount of channel rejection. Actual num of rejections might be higher if
  64. % there are a lot of bad channels. Target precision can be increased with higher chan_detect_num_iter
  65. % If empty, use only chan_detected_fraction_threshold. Can be either a fraction
  66. % of all channels (will be rounded, e.g. 1/5 of chans) or a specific integer number
  67. bemobil_config.chancorr_crit = 0.8;
  68. bemobil_config.chan_max_broken_time = 0.5;
  69. bemobil_config.chan_detect_num_iter = 20;
  70. bemobil_config.chan_detected_fraction_threshold = 0.5;
  71. bemobil_config.flatline_crit = 'off';
  72. bemobil_config.line_noise_crit = 'off';
  73. bemobil_config.num_chan_rej_max_target = 1/5;
  74. % channel locations: leave this empty if you have standard channel names that should use standard 10-20 locations,
  75. % otherwise every dataset needs to have a channel locations file in the raw_data folder, and the chanloc file needs to
  76. % have the correct participant prefix!
  77. bemobil_config.channel_locations_filename = 'Location.ced';
  78. %bemobil_config.channel_locations_filename = [];
  79. % ZapLine-Plus to reduce line noise frequencies. Automatically finds noise frequencies and removes them as good as
  80. % possible with Zapline. See 'help clean_data_with_zapline_plus' for more info about parameter tweaking.
  81. % If the 'noisefreqs' field is set to empty, searches automatically, but you can also enter predefined noise frequencies
  82. % here as a vector.
  83. % Set the whole 'bemobil_config.zaplineConfig' field to [] if no noise is present in your data (haha).
  84. bemobil_config.zaplineConfig.noisefreqs = [];
  85. %% AMICA Parameters
  86. % filter for AMICA:
  87. % See Klug & Gramann (2020) for an investigation of filter effect on AMICA -> 1.25 Hz should be a good compromise if you
  88. % don't know how much movement exists, otherwise even higher may be good, up to 2Hz, and you need to subtract 0.25 to
  89. % obtain the correct cutoff value for a filter order of 1650
  90. bemobil_config.filter_lowCutoffFreqAMICA = 1.75; % 1.75 is 1.5Hz cutoff!
  91. bemobil_config.filter_AMICA_highPassOrder = 1650; % was used by Klug & Gramann (2020)
  92. bemobil_config.filter_highCutoffFreqAMICA = []; % not used
  93. bemobil_config.filter_AMICA_lowPassOrder = [];
  94. % additional AMICA settings
  95. bemobil_config.num_models = 1; % default 1
  96. bemobil_config.AMICA_autoreject = 1; % uses automatic rejection method of AMICA. no time-cleaning (manual or automatic) is needed then!
  97. bemobil_config.AMICA_n_rej = 20; % default 10
  98. bemobil_config.AMICA_reject_sigma_threshold = 3; % default 3
  99. bemobil_config.AMICA_max_iter = 2000; % default 2000
  100. % on some PCs AMICA may crash before the first iteration if the number of threads and the amount the data does not suit
  101. % the algorithm. Jason Palmer has been informed, but no fix so far. just roll with it. if you see the first iteration
  102. % working there won't be any further crashes. in this case just press "close program" or the like and the
  103. % bemobil_spatial_filter algorithm will AUTOMATICALLY reduce the number of threads and start AMICA again. this way you
  104. % will always have the maximum number of threads that should be used for AMICA. check in the task manager how many
  105. % threads you have theoretically available and think how much computing power you want to devote for AMICA.
  106. % 4 threads are most effective for single subject speed, more threads don't really shorten the calculation time much.
  107. % best efficiency is using just 1 thread and have as many matlab instances open as possible (limited by the CPU usage).
  108. % Remember your RAM limit in this case.
  109. bemobil_config.max_threads = 4; % default 4
  110. % for warping the electrode locations to the standard 10-20 locations (leave
  111. % empty if using standard locations)
  112. % bemobil_config.warping_channel_names = {3,'FTT9h';45,'FTT10h';84,'AFz';87,'Cz'};
  113. bemobil_config.warping_channel_names = [];
  114. % dipfit settings
  115. bemobil_config.residualVariance_threshold = 100;
  116. bemobil_config.do_remove_outside_head = 'off';
  117. bemobil_config.number_of_dipoles = 1;
  118. % IC_label settings
  119. % 'default' classifier did not lead to good classification of muscles (see Klug & Gramann (2020)), 'lite' was better
  120. % overall.
  121. bemobil_config.iclabel_classifier = 'lite';
  122. % 'Brain', 'Muscle', 'Eye', 'Heart', 'Line Noise', 'Channel Noise', 'Other'
  123. bemobil_config.iclabel_classes = [1,7];
  124. % if the threshold is set to -1, the popularity classifier is used (i.e. every IC gets the class with the highest
  125. % probability), if it is set to a value, the summed score of the iclabel_classes must be higher than this threshold to
  126. % keep an IC. Must be in the [0 1] range!
  127. bemobil_config.iclabel_threshold = -1;
  128. %% finalization
  129. % filtering the final dataset
  130. bemobil_config.final_filter_lower_edge = 0.3; % this should not lead to any issues downstream but remove all very slow drifts
  131. bemobil_config.final_filter_higher_edge = 80;
  132. %% Motion Processing Parameters
  133. bemobil_config.lowpass_motion = 8;
  134. bemobil_config.lowpass_motion_after_derivative = 24;
  135. try
  136. pop_editoptions('option_saveversion6', 0, 'option_single', 0, 'option_memmapdata', 0, 'option_savetwofiles', 1, 'option_storedisk', 0);
  137. catch
  138. warning('Could NOT edit EEGLAB memory options!!');
  139. end

Clement_bemobil_config_script_ivtt.m, no license · at the source

Overview

Authors: Maud Saulay‐Carret1, Clément Naveilhan1, Xavier Corveleyn2, Stephen Ramanoël1,3
  1. Université Côte d'Azur LAMHESS, Nice, France
  2. Université Côte d'Azur, LAPCOS, Nice, France
  3. Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
Journal: Psychophysiology, volume 63, issue 3, article e70265
Dates: received 23 October 2025; accepted 10 February 2026; published online 6 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/psyp.70265 · PMID 41789739 · PMCID PMC12965046 · OpenAlex W7134046987
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: mobile EEG, perspective‐taking, spatial navigation, virtual reality
MeSH: Psychomotor Performance*, Space Perception*, Spatial Navigation*, Adult, Beta Rhythm, Electroencephalography, Female, Humans, Male, Virtual Reality, Young Adult (* major topic)
Topic: Spatial Cognition and Navigation (Automotive Engineering, Engineering), according to OpenAlex
Funding: UCA-JEDI (ANR-15- IDEX-01)
Citations: cited by 1 paper (Europe PMC); 93 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

OSF 7bzua

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (9), R (8)
Size: 58 files, 17 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Holds: 8 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), easystats (7 files), ggplot2 (6 files), lme4 (6 files), lmerTest (6 files), EEGLAB (5 files), emmeans (5 files), ggpubr (4 files), car (3 files), Signal Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (3 files), mgcv (3 files), cowplot (2 files), nlme (2 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
17 files

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

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

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Read it in the paper: doi.org/10.1111/psyp.70265.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 11 MeSH terms, 1 funder, 90 references.

Cite

This paper

Saulay‐Carret, M., Naveilhan, C., Corveleyn, X., & Ramanoël, S. (2026). Neurocognitive Dynamics of Translating Information From a Spatial Map Into Action. Psychophysiology, 63(3), e70265. https://doi.org/10.1111/psyp.70265

BibTeX

@article{saulaycarret2026neurocognitive,
author = {Saulay‐Carret, Maud and Naveilhan, Clément and Corveleyn, Xavier and Ramanoël, Stephen},
title = {{Neurocognitive Dynamics of Translating Information From a Spatial Map Into Action}},
journal = {Psychophysiology},
year = {2026},
month = mar,
volume = {63},
number = {3},
pages = {e70265},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70265},
url = {https://doi.org/10.1111/psyp.70265},
pmid = {41789739},
pmcid = {PMC12965046}
}

RIS

TY - JOUR
AU - Saulay‐Carret, Maud
AU - Naveilhan, Clément
AU - Corveleyn, Xavier
AU - Ramanoël, Stephen
TI - Neurocognitive Dynamics of Translating Information From a Spatial Map Into Action
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/03/01
VL - 63
IS - 3
SP - e70265
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70265
UR - https://doi.org/10.1111/psyp.70265
LA - en
ER -

CSL-JSON

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