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Auditory-motor interactions are sensitive to adaptation and learning on multiple timescales

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  1. [1] § Materials and method › EEG acquisition and preprocessing ↔ preprocessing/pp_prepost.py, lines 230 to 282 · score 0.59 · notch filtered, bandpass, MNE, preprocessed, EEG, channels · Report a mismatch
  2. [2] § Results › The effects of musical expertise ↔ ARTICLE_FIGS/[FIGURE_REVISION_S10A]_GSI_scores.ipynb, lines 105 to 198 · score 0.57 · musical sophistication, musical training, active, score, formal, musicians · Report a mismatch

Paper

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The authors' code

Python · 394 lines · 15 KB · MIT · 1 match

  1. import mne
  2. import os
  3. import glob
  4. import numpy as np
  5. import pandas as pd
  6. from scipy.io import savemat
  7. import matplotlib.pyplot as plt
  8. import pickle
  10. import sys
  11. sys.path.append('../utils')
  12. from pp_utils import *
  15. #======================================================================================
  16. # SPECIFY SUBJECTS AND WHAT PART OF THE EXPERIMENT
  17. #======================================================================================
  18. subjects_to_process = ['01']
  19. periods = ['post']
  22. bad_chs = [] #put the bad channel names here; process subjects with bad channels separately
  26. #======================================================================================
  27. # PREPROCESSING PARAMETERS
  28. #======================================================================================
  29. plot = False
  30. check_events = True
  31. FS_ORIG = 2048 # Hz
  32. #fs was 1024 for participant 03
  34. # Printing general info
  35. print_info = False
  37. # Notch filtering
  38. notch_applied = True
  39. freq_notch = 50
  41. # Bandpass filtering
  42. bpf_applied = True
  43. freq_low = 1
  44. freq_high = 30
  45. bandpass = str(freq_low) + '-' + str(freq_high)
  46. ftype = 'butter'
  47. order = 3
  49. # Spherical interpolation
  50. int_applied = False
  51. interpolation = 'spline'
  53. # Rereferencing using average of mastoids electrodes
  54. reref_applied = True
  55. reref_type = 'Mastoids' #Mastoids #Average
  57. # Downsampling
  58. down_applied = True
  59. downfreq = 128
  60. if not down_applied:
  61. downfreq = FS_ORIG
  62. downfreq_factor =int(FS_ORIG/downfreq)
  64. #======================================================================================
  65. # INITIALIZE DIRECTORIES
  66. #======================================================================================
  67. root_dir = "/Users/cindyzhang/Documents/M2/Audiomotor_Piano/AM-EEG/data_raw" #where the raw bdf files are
  68. output_base = f'/Users/cindyzhang/Documents/M2/Audiomotor_Piano/AM-EEG/data_preprocessed_{freq_high}Hz' #where all the preprocessed .mat files and other info go
  70. #======================================================================================
  71. # LOOP THROUGH SUBJECTS
  72. #======================================================================================
  74. for subject in subjects_to_process:
  76. for period in periods:
  77. if period == 'pre':
  78. file = f'/Users/cindyzhang/Documents/M2/Audiomotor_Piano/AM-EEG/data_raw/sub_{subject}/sub_{subject}_01.bdf'
  79. elif period == 'post':
  80. file = f'/Users/cindyzhang/Documents/M2/Audiomotor_Piano/AM-EEG/data_raw/sub_{subject}/sub_{subject}_03.bdf'
  81. else: #just pick a random file for a test
  82. file = f'/Users/cindyzhang/Documents/M2/Audiomotor_Piano/AM-EEG/data_raw/sub_{subject}/sub_{subject}_01.bdf'
  84. print('Processing file: ', file)
  85. #======================================================================================
  86. # check events
  87. #======================================================================================
  89. if check_events == True:
  91. #load file
  92. raw = mne.io.read_raw_bdf(file, eog=None, misc=None, stim_channel='Status',
  93. infer_types=False, preload=True, verbose=None)
  94. #mark bad channels
  95. raw.info['bads'] = bad_chs
  97. events = mne.find_events(raw, stim_channel='Status', shortest_event=1)
  98. events_2, events_3, events_4, events_5, events_6, trial_starts = sort_events(events)
  101. #check events
  102. plot_subset = False
  103. start = 0
  104. end = 100
  107. plt.figure(figsize = (20,10))
  109. if plot_subset:
  110. #keystrokes
  111. plt.eventplot(events_2[:,0][start:end], lineoffsets = 0)
  112. plt.eventplot(events_3[:,0][start:end], color = 'green', lineoffsets = -2)
  113. plt.eventplot(events_4[:,0][start:end], color = 'orange', lineoffsets = -3)
  114. plt.eventplot(events_5[:,0][start:end], color = 'red', lineoffsets=-4)
  115. else:
  116. plt.eventplot(events_2[:,0], lineoffsets = 0)
  117. plt.eventplot(events_3[:,0], color = 'green', lineoffsets = -2)
  118. plt.eventplot(events_4[:,0], color = 'orange', lineoffsets = -3)
  119. plt.eventplot(events_5[:,0], color = 'red', lineoffsets=-4)
  120. plt.title(f'Events subject {subject} {period} training')
  121. plt.show()
  124. df_pre = pd.DataFrame()
  125. subject_ID = file.split('/')[-2][-2:]
  126. if subject_ID == '03':
  127. FS_ORIG = 1024
  130. output_dir = os.path.join(output_base, subject_ID)
  131. if not os.path.exists(output_dir):
  132. os.makedirs(output_dir)
  137. #======================================================================================
  138. # READ EEG FILES
  139. #======================================================================================
  141. raw = mne.io.read_raw_bdf(file, eog=['EXG3', 'EXG4'], misc=None, stim_channel='Status',
  142. infer_types=False, preload=True, verbose=None)
  145. # Check metadata
  146. n_time_samps = raw.n_times
  147. time_secs = raw.times
  148. ch_names = raw.ch_names
  149. n_chan = len(ch_names)
  151. if print_info == True:
  152. print('the (cropped) sample data object has {} time samples and {} channels.'
  153. ''.format(n_time_samps, n_chan))
  154. print('The last time sample is at {} seconds.'.format(time_secs[-1]))
  155. print('The first few channel names are {}.'.format(', '.join(ch_names[:3])))
  156. print('bad channels:', raw.info['bads']) # chs marked "bad" during acquisition
  157. print(raw.info['sfreq'], 'Hz') # sampling frequency
  158. print(raw.info['description'], '\n') # miscellaneous acquisition info
  159. print(raw.info)
  161. if plot:
  162. raw.plot(start=100, duration=10)
  165. #======================================================================================
  166. # FIND TRIGGERS FOR SEGMENTING
  167. #======================================================================================
  168. #loading the raw events
  169. #the stim channel is called 'Status'
  170. events = mne.find_events(raw, stim_channel='Status', shortest_event=1) #raises exception if shortest event is default 2...?
  173. #...TO DO: import bad triggers noted in experiment from corresponding csv
  174. bad_triggers = []
  175. #events.pop(bad_triggers indices)
  177. #cleaned triggers according to channel
  178. #trial start and end are indicated in channel 5, filtered for 10+ min duration
  179. events_2, events_3, events_4, events_5, events_6, trial_starts = sort_events(events)
  180. #_, _, _, _, _, trial_starts = sort_events(events)
  184. #======================================================================================
  185. # CROPPING FILES TO THE TRIAL
  186. #======================================================================================
  187. print("trial starts: \n", trial_starts)
  189. #trial durations
  190. listen_dur = 665
  191. motor_dur = 600
  192. error_dur = 600
  194. #start times for cropping
  195. listen_start, motor_start, error_start = trial_starts[:,0]/FS_ORIG
  197. #end times
  198. listen_end = listen_start+listen_dur
  199. motor_end = motor_start+motor_dur
  200. error_end = error_start + error_dur
  204. #.....TO DO
  205. #add stuff for post
  206. #approximate crop for training?
  207. eeg_listen = raw.copy().crop(tmin = listen_start, tmax = listen_end)
  208. eeg_motor = raw.copy().crop(tmin = motor_start, tmax = motor_end)
  209. eeg_error = raw.copy().crop(tmin = error_start, tmax = error_end)
  212. #------------------------
  213. # CHOOSING FILES TO PROCESS
  214. #---------------------------
  216. #get the eeg data
  217. #eegs_to_process = [eeg_listen_pre, eeg_motor_pre, eeg_error_pre, eeg_listen_post, eeg_motor_post, eeg_error_post]
  218. eegs_to_process = [eeg_listen, eeg_motor, eeg_error]
  222. if period == 'pre':
  223. eeg_names = ['eeg_listen_pre', 'eeg_motor_pre', 'eeg_error_pre']
  224. elif period == 'post':
  225. eeg_names = ['eeg_listen_post', 'eeg_motor_post', 'eeg_error_post']
  226. else:
  227. eeg_names = [f'eeg_listen_{period}', f'eeg_motor_{period}', f'eeg_error_{period}']
  230. #======================================================================================
  231. # FILTERING
  232. #======================================================================================
  233. for i, data in enumerate(eegs_to_process):
  234. ## -------------
  235. ## Select channels
  236. ## -------------
  238. #eeg_channels = ch_names[:66] + [ch_names[-1]]
  239. eeg_channels = ch_names[0:72]
  240. eeg = data.copy().pick_channels(eeg_channels)
  241. if plot:
  242. eeg.plot(start=100, duration=10, n_channels=len(raw.ch_names))
  245. """eeg_channels = ch_names[0:66]
  246. eeg = eeg.pick_channels(eeg_channels)
  247. if plot:
  248. eeg.plot(start=100, duration=10, n_channels=len(raw.ch_names))"""
  250. ## -------------
  251. ## Notch filtering
  252. ## -------------
  253. df_pre['notch_applied'] = [notch_applied]
  254. if notch_applied:
  255. eeg = eeg.notch_filter(freqs=freq_notch)
  256. df_pre['notch'] = [freq_notch]
  257. if plot:
  258. eeg.plot()
  260. ## -------------
  261. ## BPFiltering
  262. ## -------------
  263. df_pre['bpf_applied'] = [bpf_applied]
  264. if bpf_applied:
  265. iir_params = dict(order=order, ftype=ftype)
  266. filter_params = mne.filter.create_filter(eeg.get_data(), eeg.info['sfreq'],
  267. l_freq=freq_low, h_freq=freq_high,
  268. method='iir', iir_params=iir_params)
  270. if plot:
  271. flim = (1., eeg.info['sfreq'] / 2.) # frequencies
  272. dlim = (-0.001, 0.001) # delays
  273. kwargs = dict(flim=flim, dlim=dlim)
  274. mne.viz.plot_filter(filter_params, eeg.info['sfreq'], compensate=True, **kwargs)
  275. # plt.savefig(os.path.join(output_dir, 'bpf_ffilt_shape.png'))
  277. eeg = eeg.filter(l_freq=freq_low, h_freq=freq_high, method='iir', iir_params=iir_params)
  278. df_pre['bandpass'] = [iir_params]
  279. df_pre['HPF'] = [freq_low]
  280. df_pre['LPF'] = [freq_high]
  281. if plot:
  282. eeg.plot()
  285. ## -------------
  286. ## Interpolation
  287. ## -------------
  288. df_pre['int_applied'] = [int_applied]
  289. if int_applied:
  290. eeg = eeg.interpolate_bads(reset_bads=False) #, method=interpolation
  292. # Get the indices and names of the interpolated channels
  293. interp_inds = eeg.info['bads']
  294. interp_names = [eeg.info['ch_names'][i] for i in interp_inds]
  296. # Print the number and names of the interpolated channels
  297. print(f'{len(interp_inds)} channels interpolated: {interp_names}')
  299. df_pre['interpolation'] = [interpolation]
  300. df_pre['interp_inds'] = [interp_inds]
  301. df_pre['interp_names'] = [interp_names]
  303. if plot:
  304. eeg.plot()
  307. ## -------------
  308. ## Rereferencing
  309. ## -------------
  310. df_pre['reref_applied'] = [reref_applied]
  311. if reref_applied:
  312. # Set electrodes for rereferencing
  313. if reref_type == 'Mastoids':
  314. #accounting for different channel names on different computers
  315. if 'M1' in eeg.ch_names:
  316. reref_channels = ['M1', 'M2']
  317. else:
  318. reref_channels = ['EXG1', 'EXG2']
  319. else:
  320. reref_channels = 'average'
  322. # Actually r-referencing signals
  323. eeg = eeg.set_eeg_reference(ref_channels=reref_channels)
  324. df_pre['reref_type'] = [reref_type]
  325. df_pre['reref_channels'] = [reref_channels]
  326. if plot:
  327. eeg.plot()
  330. ## -------------
  331. ## Downsampling
  332. ## -------------
  333. df_pre['down_applied'] = [down_applied]
  334. df_pre['downfreq'] = [downfreq]
  335. if down_applied:
  336. eeg = eeg.resample(sfreq=downfreq)
  337. if plot:
  338. eeg.plot()
  341. #======================================================================================
  342. # UPDATE TRIGGERS
  343. #======================================================================================
  345. #create support vectors
  346. #zero array with downsampled dimensions
  347. events_original = np.zeros((5, data.get_data().shape[1])) #5 rows = event types, along time axis zero when there is an event and 1 everywhere else
  350. #get triggers
  351. section_triggers = mne.find_events(data, stim_channel='Status', shortest_event=1)
  352. events_2, events_3, events_4, events_5, events_6, section_start = sort_events(section_triggers, clean = True)
  354. #get trial start time
  355. section_start = section_start[0][0]
  357. #find indices corrected for start time
  358. indices_2 = (events_2[:,0]) - section_start
  359. indices_3 = (events_3[:,0]) - section_start
  360. indices_4 = (events_4[:,0]) - section_start
  361. indices_5 = (events_5[:,0]) - section_start
  363. #populate event array with 1s where there are events
  365. events_original[0][indices_2] = 1
  366. events_original[1][indices_3] = 1
  367. events_original[2][indices_4] = 1
  368. events_original[3][indices_5] = 1
  370. #resample while preserving events
  371. cropped_length = eeg.get_data().shape[1]
  372. events_resampled = np.zeros((5, cropped_length))
  373. for row in range(events_resampled.shape[0]):
  374. events_resampled[row] = discretize(events_original[row], final_length = cropped_length, downfreq_factor = downfreq_factor)
  376. #======================================================================================
  377. # SAVING CROPPED FILES
  378. #======================================================================================
  380. name = eeg_names[i]
  381. eeg_tosave = eeg.get_data()
  383. savemat(os.path.join(output_dir,f'{name}_{subject_ID}.mat'),
  384. {'all_electrodes': eeg_tosave[0:72],
  385. 'trial_data': eeg_tosave[0:64, :],
  386. 'trial_mastoids': eeg_tosave[64:66, :],
  387. 'trial_exg': eeg_tosave[66:72, :],
  388. 'events': events_resampled})
  391. ## -------------
  392. ## Save preprocessing stages
  393. ## -------------
  394. df_pre.to_csv(os.path.join(output_dir, f"preprocess_record_{subject_ID}_{period}.csv"), index=False)

pp_prepost.py at commit 2aa6de3, under MIT · at the source

Overview

Authors: Haiqin Zhang1, Giorgia Cantisani2, Shihab Shamma3
ORCID iDs: Haiqin Zhang, Giorgia Cantisani
  1. Laboratoire des systèmes perceptifs, CNRS, École Normale Supérieure, PSL University, Paris, France
  2. Sciences et Technologies de la Musique et du Son Lab, CNRS, IRCAM, Sorbonne Université, Paris, France
  3. Neural Systems Laboratory, University of Maryland College Park, College Park, MD, USA
Institutions: Laboratoire des Systèmes Perceptifs (France); Centre National de la Recherche Scientifique (France); Université Paris Sciences et Lettres (France); Sciences et Technologies de la Musique et du Son (France); Institut de Recherche et Coordination Acoustique Musique (France); Sorbonne Université (France); University of Maryland, College Park (United States)
Journal: bioRxiv (preprint)
Dates: published online 25 March 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.64898/2026.03.23.712909 · OpenAlex W7140317042
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, fMRI & imaging, Physiology & signal measures
Keywords: auditory-motor learning, surprisal, music, N100, P50
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (787836)
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

Auditory-motor learning is critical in mastering the production of complex sounds, such as speaking and playing music. It is anchored upon internal models of interactions between actions and their sensory consequences, which are fine-tuned by minimizing the errors between the predicted and received sound. Here, we applied the concept of surprisal to a piano-playing task to probe the neural dynamics of sensorimotor learning. Specifically, during play, the key-pitch map was changed unpredictably among three map configurations: normal, inverted, and shifted-inverted. At the change boundaries, a signature of violated motor-to-auditory predictions was found in the auditory evoked responses at N100 which could not be attributed to either purely auditory surprisals or motor execution errors. This surprisal is modulated by short-term context, with greater surprise following longer periods of no map change, indicating that the brain continuously tracks short-term map contexts and rapidly adapts to them. In contrast, 30 minutes of extended goal-directed training on a single map modulated P50 amplitude only for that map, which can be explained by a slow, persistent modulation of motor predictions from the auditory signals. Hence, while auditory predictions from motor actions are rapidly and implicitly learned within short-term contexts, the complementary process of adjusting motor inferences from auditory inputs requires targeted training sustained over time. Our approach of studying auditory-motor surprisal in time-varying sequences reveals that auditory-motor learning is fast, context-sensitive, and shaped by both short- and long-term experience.

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 2 matches between paragraphs and lines of code.

haiqin-zhang/AuditoryMotorPiano

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2aa6de3aa0e1d963119f02fce4d93ec23e40c829, 12 August 2026
Languages: Jupyter (15), Python (14)
Size: 496 files, 29 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 15 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (26 files), pandas (22 files), Matplotlib (20 files), SciPy (20 files), MNE-Python (17 files), seaborn (11 files), statannotations (9 files), statsmodels (9 files), Pingouin (6 files), ICLabel (1 file), lme4 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
31 files
At the source: github.com/haiqin-zhang/AuditoryMotorPiano

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

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.

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;
  • 29 scripts, each with its path and the digest of its content;
  • 2 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

Datasets cited

Data availability

The code for analyzing data and generating the figures is available on Github at https://github.com/haiqin-zhang/AuditoryMotorPiano. EEG data is available on Zenodo (DOI: 10.5281/zenodo.17949131).

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, journal, dates, 3 authors, 5 keywords, 1 funder, 63 references.

Cite

This paper

Zhang, H., Cantisani, G., & Shamma, S. (2026). Auditory-motor interactions are sensitive to adaptation and learning on multiple timescales. bioRxiv (preprint). https://doi.org/10.64898/2026.03.23.712909

BibTeX

@article{zhang2026auditory,
author = {Zhang, Haiqin and Cantisani, Giorgia and Shamma, Shihab},
title = {{Auditory-motor interactions are sensitive to adaptation and learning on multiple timescales}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.23.712909},
url = {https://doi.org/10.64898/2026.03.23.712909}
}

RIS

TY - JOUR
AU - Zhang, Haiqin
AU - Cantisani, Giorgia
AU - Shamma, Shihab
TI - Auditory-motor interactions are sensitive to adaptation and learning on multiple timescales
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/25
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.23.712909
UR - https://doi.org/10.64898/2026.03.23.712909
ER -

CSL-JSON

{
"id": "10.64898/2026.03.23.712909",
"type": "article",
"title": "Auditory-motor interactions are sensitive to adaptation and learning on multiple timescales",
"container-title": "bioRxiv (preprint)",
"author": [
{
"family": "Zhang",
"given": "Haiqin"
},
{
"family": "Cantisani",
"given": "Giorgia"
},
{
"family": "Shamma",
"given": "Shihab"
}
],
"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.23.712909",
"ISSN": "2692-8205",
"publisher": "bioRxiv",
"URL": "https://doi.org/10.64898/2026.03.23.712909",
"issued": {
"date-parts": [
[
2026,
3,
25
]
]
}
}

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In common: Pingouin, lme4, seaborn, 2 other tools, cognitive, 5 references
[10] doi:10.1038/s42003-026-09825-2 [code]
Normative modeling of MEG brain oscillations across the human lifespan.
Journal: Communications biology
In common: ICLabel, Pingouin, MNE-Python, 6 other tools, 1 reference

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