{"dataset":{"id":"31920","dataset_id":"on007523","name":"LittlePrince_MEG_French_Listen_Pallier2025","description":"This dataset comprises magnetoencephalography (MEG) recordings from 58 healthy French-speaking adults listening to the French audiobook of *Le Petit Prince*. Participants completed 9 runs of continuous listening while MEG signals were recorded alongside electrooculography and electrocardiography. High-resolution anatomical MRI scans were acquired for each participant to enable source localization and cortical surface reconstruction, supporting investigations of neural language processing during naturalistic speech comprehension.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on007523","concept_doi":"10.82901/nemar.on007523","latest_version_doi":"10.82901/nemar.on007523.v1.0.0","created_at":"2026-06-14 07:03:05","updated_at":"2026-07-10 23:15:09","zenodo_concept_id":"20687476","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"LittlePrince_MEG_French_Listen_Pallier2025\",\n  \"description\": \"This dataset comprises magnetoencephalography (MEG) recordings from 58 healthy French-speaking adults listening to the French audiobook of *Le Petit Prince*. Participants completed 9 runs of continuous listening while MEG signals were recorded alongside electrooculography and electrocardiography. High-resolution anatomical MRI scans were acquired for each participant to enable source localization and cortical surface reconstruction, supporting investigations of neural language processing during naturalistic speech comprehension.\",\n  \"methods_description\": \"MEG data were recorded using a whole-head Elekta Neuromag TRIUX system (102 magnetometers, 204 planar gradiometers) at 1000 Hz sampling rate with online filtering (0.1–330 Hz). Simultaneous EOG and ECG recordings monitored eye movements and cardiac activity. Auditory stimuli were delivered via MEG-compatible earphones at individually adjusted comfortable listening levels. High-resolution T1-weighted anatomical MRI scans were acquired on a 3T Siemens Magnetom Prisma scanner using MPRAGE sequence for coregistration and source analysis.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Corentin Bel\": {},\n    \"Julie Bonnaire\": {},\n    \"Christophe Pallier\": {\n      \"orcid\": \"0000-0002-3324-666X\"\n    },\n    \"Jean-Rémi King\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"MEG\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D015225\"\n    },\n    {\n      \"term\": \"Magnetoencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D015225\"\n    },\n    {\n      \"term\": \"language processing\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D007806\"\n    },\n    {\n      \"term\": \"naturalistic listening\"\n    },\n    {\n      \"term\": \"speech comprehension\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D013066\"\n    },\n    {\n      \"term\": \"auditory processing\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D001307\"\n    },\n    {\n      \"term\": \"source localization\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on007523\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on007523\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41467-025-65499-0\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/sdata.2018.110\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-022-01625-7\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsSupplementedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007524\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsPartOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds007523.v1.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"meg\"\n  ],\n  \"sizes\": [\n    \"477.7 GB (642 files)\"\n  ],\n  \"formats\": [\n    \".TextGrid\",\n    \".csv\",\n    \".dat\",\n    \".fif\",\n    \".gz\",\n    \".json\",\n    \".md\",\n    \".py\",\n    \".sh\",\n    \".tsv\",\n    \".txt\",\n    \".wav\",\n    \".yml\"\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"French National Research Agency (Agence Nationale de la Recherche)\",\n      \"award_number\": \"ANR-17-EURE-0017\"\n    }\n  ],\n  \"source_hash\": \"66e20636c4b7f5fc7fa32d4a95b5b05d170cf39ec1992ebc4d91358a101112c3\"\n}","last_activity_at":"2026-06-14 07:03:05","source":"openneuro","source_id":"ds007523","subject_count":58,"modalities":"anat,meg","age_min":18,"age_max":43,"file_size":477749225935,"total_files":642,"tasks":"listen","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Corentin Bel, Julie Bonnaire, Christophe Pallier, Jean-Rémi King","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on007523-blue)](https://doi.org/10.82901/nemar.on007523)\n\n## Summary\n\nThis dataset contains magnetoencephalography (MEG) recordings collected\nwhile participants listened to the French audiobook of *Le Petit Prince*\nby Antoine de Saint-Exupéry.\n\nA complementary MEG dataset from the same project, using a reading (RSVP) paradigm, is available on OpenNeuro (accession number: ds007524).\n\nThis data is analyzed in:\n\nd’Ascoli, S., Bel, C., Rapin, J. et al. Towards decoding individual words from non-invasive brain recordings. Nature Communications 16, 10521 (2025). https://doi.org/10.1038/s41467-025-65499-0\n\n------------------------------------------------------------------------\n\n## Participants\n\nFifty-eight healthy adults participated in the listening experiment (17\nfemales; mean age = 27.8 years, SD = 5.5 years).\n\nAll participants were native French speakers, right-handed, and reported\nno history of neurological disorders. Written informed consent was\nobtained prior to participation. The study was approved by the relevant\nlocal ethics committee.\n\n------------------------------------------------------------------------\n\n## Stimuli\n\nThe auditory stimulus consisted of the French audiobook version of *Le\nPetit Prince*.\n\n- Language: French\n- Format: Continuous audiobook\n- Segmentation: 9 parts\n- Mean duration per part: 10min50s\n- Standard deviation: 55s\n- Minimum duration: 9min40s\n- Maximum duration: 12min30s\n\nThe same audiobook version was previously used in a publicly available\nfMRI dataset (Li et al., 2022). \n\n------------------------------------------------------------------------\n\n## Experimental Procedure\n\nParticipants were seated in the MEG system after informed consent and\nfamiliarization with the recording environment.\n\nAuditory stimuli were delivered through MEG-compatible earphones. Sound\nintensity was individually adjusted to a comfortable listening level\nbefore the experiment. Participants were instructed to listen\nattentively and remain as still as possible.\n\nThe experiment consisted of 9 runs, corresponding to the 9 audiobook\nsegments. Between runs, participants completed 4 multiple-choice\ncomprehension questions presented visually on a screen (not reported here). \nShort breaks were provided between runs. Alertness and movement were monitored \nvia camera during recording.\n\n------------------------------------------------------------------------\n\n## Acquisition\n\n### MEG\n\nMEG data for all three tasks were recorded inside the same magnetically shielded room using a whole-head Elekta Neuromag TRIUX MEG system (Elekta Oy, Helsinki, Finland), equipped with 102 magnetometers and 204 planar gradiometers. Data were recorded continuously with a sampling rate of 1000 Hz and an online low-pass filter at 330 Hz and high-pass filter at 0.1 Hz.\nVertical and horizontal electrooculograms (EOG) and an electrocardiogram (ECG) were recorded simultaneously using bipolar electrodes to monitor eye movements and heartbeats.\n\n### Anatomical MRI\n\nFor each participant, a high-resolution T1-weighted anatomical MRI scan was acquired using a 3T Siemens Magnetom Prisma MRI scanner (Siemens Healthcare, Erlangen, Germany).\nA standard MPRAGE sequence was used. MRI scans were typically acquired right after the MEG recording. Scans were used for coregistration and cortical surface reconstruction for source analysis. \n\n------------------------------------------------------------------------\n\n## Data Organization\n\n### Raw Data\n\nThe root directory includes:\n\n-   `dataset_description.json`\n-   `participants.tsv` and `participants.json`\n-   `task-listen_events.json`\n-   `sub-01` to `sub-58`\n-   `sourcedata/`\n\nEach subject directory (`sub-XX`) contains one session (`ses-01`) with:\n\n-   `anat/`: T1-weighted MRI (`sub-XX_ses-01_T1w.nii.gz`) and\n    corresponding JSON sidecar\n-   `meg/`: 9 MEG runs (`task-listen_run-01` to `run-09`), each\n    including:\n    -   continuous MEG data (`*_meg.fif`)\n    -   sidecar JSON files\n    -   `events.tsv` and `channels.tsv` files\n    -   coordinate system file (`*_coordsystem.json`)\n    -   calibration and crosstalk files\n-   `sub-XX_ses-01_scans.tsv`: scan-level metadata\n\nEach run corresponds to one audiobook segment.\n\nAcquisition parameters are provided in the corresponding sidecar JSON\nfiles.\n\n------------------------------------------------------------------------\n\n## References\n\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G.,\nGramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J.,\nOostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., & Baillet, S.\n(2018). MEG-BIDS, the brain imaging data structure extended to\nmagnetoencephalography. *Scientific Data*, 5, 180110.\nhttps://doi.org/10.1038/sdata.2018.110\n\n\nLi, Jixing, et al. “Le Petit Prince Multilingual Naturalistic fMRI Corpus.” Scientific Data, vol. 9, no. 1, Aug. 2022, p. 530. www.nature.com, https://doi.org/10.1038/s41597-022-01625-7.\n\n","bids_version":"1.7.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-06 01:22:47","zarr_store_count":521,"zarr_index_etag":"b3cc4c3c8742d91506da5ae13cf6745c","zarr_source_commit":"9c0316aa682055d5c57cd6d2c3336aafa452591c","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":null,"archive_skip_reason":"dataset 444.9 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":29,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":null,"data_complete":null,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":341297,"recording_duration_min":366,"recording_duration_max":783,"recording_count":521,"recordings_unavailable":0,"recordings_measured":521,"channel_count_min":329,"channel_count_max":404,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-15 22:41:56\",\"metadata_updated_at\":\"2026-06-15 22:41:56\",\"archive_checked_at\":\"2026-06-16 15:57:09\",\"zarr_checked_at\":null,\"records_checked_at\":null,\"citations_updated_at\":\"2026-09-08 03:00:48\",\"channel_montage_checked_at\":null,\"hed_checked_at\":\"2026-06-30 05:44:41\",\"data_checked_at\":null,\"availability_report_at\":\"2026-07-23 01:32:48\",\"signal_defaults_at\":\"2026-09-02 12:55:30\",\"recording_stats_at\":\"2026-09-06 03:01:11\"}","participants":58,"num_citations":29,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"445 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on007523/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}