OSCR

Objective assessment of familiarity in music using imagery and EEG-based machine learning.

Overview

Authors: Benjamin Darçot1, Magali Nicolier2,3, Julie Giustiniani1,3, Djamila Bennabi2,4, Emmanuel Haffen1,4, Pierre Vandel1,5, Damien Gabriel1,2
ORCID iDs: Benjamin Darçot, Pierre Vandel
  1. Université Marie et Louis Pasteur, INSERM, UMR 1322 LINC,Besançon, 25000 France
  2. CHU Besançon, Inserm CIC 1431, Domaine Santé Mentale et Neurosciences,Besançon, 25000 France
  3. Service de Psychiatrie de l’Adulte, CHU Besançon,Besançon, 25000 France
  4. Centre Expert Bipolaire et Dépression Résistante FondaMental, CHU Besançon,Besançon, 25000 France
  5. Service of Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL),Prilly, Switzerland
Institutions: Inserm (France); Clinical Investigation Center Plurithematic Tours (France); Centre Hospitalier Universitaire de Besançon (France); University of Lausanne (Switzerland)
Journal: Scientific reports, volume 16, issue 1, article 8689
Dates: received 9 September 2025; accepted 24 February 2026; published online 6 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-41988-0 · PMID 41792362 · PMCID PMC12979593 · OpenAlex W7134040822
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Statistics, Physiology & signal measures
Keywords: EEG, Musical familiarity, Music listening, Machine learning, Riemannian geometry, Neuroscience, Psychology
MeSH: Auditory Perception*, Electroencephalography*, Imagination*, Machine Learning*, Music*, Recognition, Psychology*, Adult, Auditory Cortex, Female, Humans, Male, Young Adult (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Agence Nationale de la Recherche (ANR-22-EXPR-0014)
Citations: not cited yet (Europe PMC); 42 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.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

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

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41598-026-41988-0.

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, issue, pages, dates, 7 authors, 7 keywords, 12 MeSH terms, 1 funder, 42 references.

Cite

This paper

Darçot, B., Nicolier, M., Giustiniani, J., Bennabi, D., Haffen, E., Vandel, P., & Gabriel, D. (2026). Objective assessment of familiarity in music using imagery and EEG-based machine learning. Scientific reports, 16(1), 8689. https://doi.org/10.1038/s41598-026-41988-0

BibTeX

@article{darcot2026objective,
author = {Darçot, Benjamin and Nicolier, Magali and Giustiniani, Julie and Bennabi, Djamila and Haffen, Emmanuel and Vandel, Pierre and Gabriel, Damien},
title = {{Objective assessment of familiarity in music using imagery and EEG-based machine learning}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {8689},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-41988-0},
url = {https://doi.org/10.1038/s41598-026-41988-0},
pmid = {41792362},
pmcid = {PMC12979593}
}

RIS

TY - JOUR
AU - Darçot, Benjamin
AU - Nicolier, Magali
AU - Giustiniani, Julie
AU - Bennabi, Djamila
AU - Haffen, Emmanuel
AU - Vandel, Pierre
AU - Gabriel, Damien
TI - Objective assessment of familiarity in music using imagery and EEG-based machine learning
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/06
VL - 16
IS - 1
SP - 8689
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-41988-0
UR - https://doi.org/10.1038/s41598-026-41988-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-41988-0",
"type": "article-journal",
"title": "Objective assessment of familiarity in music using imagery and EEG-based machine learning",
"container-title": "Scientific reports",
"author": [
{
"family": "Darçot",
"given": "Benjamin"
},
{
"family": "Nicolier",
"given": "Magali"
},
{
"family": "Giustiniani",
"given": "Julie"
},
{
"family": "Bennabi",
"given": "Djamila"
},
{
"family": "Haffen",
"given": "Emmanuel"
},
{
"family": "Vandel",
"given": "Pierre"
},
{
"family": "Gabriel",
"given": "Damien"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "8689",
"DOI": "10.1038/s41598-026-41988-0",
"PMID": "41792362",
"PMCID": "PMC12979593",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-41988-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
6
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1111/psyp.70385 [code]
Exploring EEG and ECG in Music Listening: A Scoping Review.
Journal: Psychophysiology
In common: EEG, cognitive, 3 references
[2] doi:10.1523/eneuro.0289-25.2026 [code]
Reconstruction of Imagined Melody with Relative Pitch Decoding in Electrocorticography.
Journal: eNeuro
In common: cognitive, 3 references
[3] doi:10.1093/braincomms/fcag161 [code]
Functional neuroanatomy of musical object processing in Alzheimer's disease and frontotemporal dementia.
Journal: Brain communications
In common: cognitive, 3 references
[4] doi:10.3390/e28060644 [code]
Riemannian Geometry for Noise-Robust Covariance Network Analysis of Schizophrenia EEG: Geometric-Entropic Signatures of Dysconnectivity.
Journal: Entropy (Basel, Switzerland)
In common: EEG, 3 references
[5] doi:10.3389/fnhum.2026.1807535
The ASME-speller: 30-class auditory brain-computer interface speller using stream segregation and the QWERTY layout.
Journal: Frontiers in human neuroscience
In common: EEG, 3 references
[6] doi:10.3389/fnhum.2026.1895016 [code]
A confidence-gated source selection strategy for cross-session transfer in brain-computer interfaces.
Journal: Frontiers in human neuroscience
In common: EEG, 3 references
[7] doi:10.1371/journal.pone.0355737
How artists experience their own art.
Journal: PloS one
In common: cognitive, 2 references
[8] doi:10.3390/s26134045
Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.
Journal: Sensors (Basel, Switzerland)
In common: EEG, 2 references
[9] doi:10.1038/s41598-026-56070-y [code]
SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.
Journal: Scientific reports
In common: EEG, 2 references
[10] doi:10.1186/s12911-026-03591-1 [code]
Different pattern: a new EEG-based method for mental performance detection.
Journal: BMC medical informatics and decision making
In common: EEG, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.