{"dataset":{"id":"50771","dataset_id":"on002338","name":"A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP2","description":"This dataset comprises simultaneous 64-channel EEG and 3T fMRI recordings from 16 subjects performing motor imagery and neurofeedback tasks. Participants were randomly assigned to receive either mono-dimensional or bi-dimensional neurofeedback displays during five experimental runs with alternating rest and task blocks. The dataset includes raw EEG data in Brain Vision format, preprocessed EEG data, BOLD fMRI acquisitions, computed neurofeedback scores from both modalities (EEG and fMRI), and event timing files, providing a comprehensive resource for multimodal neuroimaging data integration studies.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on002338","concept_doi":"10.82901/nemar.on002338","latest_version_doi":"10.82901/nemar.on002338.v1.0.0","created_at":"2026-06-21 04:31:05","updated_at":"2026-07-10 22:10:03","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP2\",\n  \"description\": \"This dataset comprises simultaneous 64-channel EEG and 3T fMRI recordings from 16 subjects performing motor imagery and neurofeedback tasks. Participants were randomly assigned to receive either mono-dimensional or bi-dimensional neurofeedback displays during five experimental runs with alternating rest and task blocks. The dataset includes raw EEG data in Brain Vision format, preprocessed EEG data, BOLD fMRI acquisitions, computed neurofeedback scores from both modalities (EEG and fMRI), and event timing files, providing a comprehensive resource for multimodal neuroimaging data integration studies.\",\n  \"methods_description\": \"EEG was recorded at 5 kHz using a 64-channel MR-compatible system (Brain Products) with FCz reference and AFz ground. fMRI was acquired using 3T Siemens Verio with echo-planar imaging (EPI): TR=1s, TE=23ms, resolution 2×2×4mm, 16 slices. EEG preprocessing included gradient artifact correction, downsampling to 200Hz, low-pass filtering (50Hz), ballistocardiogram artifact correction, and segmentation. The experimental protocol consisted of 5 runs with 20-second block design alternating rest and task periods.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Giulia Lioi\": {},\n    \"Claire Cury\": {},\n    \"Lorraine Perronnet\": {},\n    \"Marsel Mano\": {\n      \"orcid\": \"0000-0002-7757-3355\"\n    },\n    \"Elise Bannier\": {},\n    \"Anatole Lecuyer\": {},\n    \"Christian Barillot\": {\n      \"orcid\": \"0000-0002-1589-7696\"\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"fMRI\"\n    },\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"neurofeedback\"\n    },\n    {\n      \"term\": \"multimodal neuroimaging\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"simultaneous EEG-fMRI\"\n    },\n    {\n      \"term\": \"motor cortex\"\n    },\n    {\n      \"term\": \"real-time feedback\"\n    },\n    {\n      \"term\": \"event-related desynchronization\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.1101/397729.\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on002338\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on002338\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/862375\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.3389/fnins.2017.00140\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/397729\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds002336\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsPartOf\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds002338.v2.0.1\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"French National Research Agency\",\n      \"award_number\": \"ANR-10-LABX-07-01\"\n    }\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"eeg\",\n    \"func\"\n  ],\n  \"sizes\": [\n    \"27.8 GB (804 files)\"\n  ],\n  \"formats\": [\n    \".dat\",\n    \".eeg\",\n    \".gitattributes\",\n    \".gz\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".png\",\n    \".tiff\",\n    \".tsv\",\n    \".vhdr\",\n    \".vmrk\",\n    \".yml\"\n  ],\n  \"source_hash\": \"7fc92c7e9718f8728e94e5e25059dfbaa32bc9318ecfdc8b674dce81dd2b5281\"\n}","last_activity_at":"2026-06-21 04:31:05","source":"openneuro","source_id":"ds002338","subject_count":17,"modalities":"anat,eeg,func","age_min":18,"age_max":66,"file_size":27802577817,"total_files":1007,"tasks":"1dNF,2dNF,MIpost,MIpre","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Giulia Lioi, Claire Cury, Lorraine Perronnet, Marsel Mano, Elise Bannier, Anatole Lecuyer, Christian Barillot","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on002338-blue)](https://doi.org/10.82901/nemar.on002338)\n\n————————————————————————————————\nORIGINAL PAPERS\n————————————————————————————————\nLioi, G., Cury, C., Perronnet, L., Mano, M., Bannier, E., Lécuyer, A., & Barillot, C. (2019). Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration Authors. Accepted for publication in Scientific Data. https://doi.org/https://doi.org/10.1101/862375\nMano, Marsel, Anatole Lécuyer, Elise Bannier, Lorraine Perronnet, Saman Noorzadeh, and Christian Barillot. 2017. “How to Build a Hybrid Neurofeedback Platform Combining EEG and FMRI.” Frontiers in Neuroscience 11 (140). https://doi.org/10.3389/fnins.2017.00140\nLorraine Perronnet, Anatole Lecuyer, Marsel Mano, Mathis Fleury, Giulia Lioi, Claire Cury, Maureen Clerc, Fabien Lotte, and Christian Barillot. 2018. “Learning 2-in-1 : Towards Integrated EEG-FMRI-Neurofeedback.” BioRxiv, no. 397729. https://doi.org/10.1101/397729.\n\n————————————————————————————————\nOVERVIEW\n————————————————————————————————\nThis dataset XP2 can be pull together with the dataset XP1, available here : https://openneuro.org/datasets/ds002336.\nData acquisition methods have been described in Perronnet et al. (2017, Frontiers in Human Neuroscience).\nSimultaneous 64 channel EEG and fMRI during right-hand motor imagery and neurofeedback (NF) were acquired in this study (as well as in XP1). This study involved 16 subjects randomly assigned to two groups: in a first group they performed bimodal EEG-fMRI NF with a bi-dimensional feedback metaphor, in the second group the same task was executed with a mono-dimensional feedback.\n\n————————————————————————————————\nEXPERIMENTAL PARADIGM\n————————————————————————————————\n\nThe experimental protocol consisted of 5 EEG-fMRI runs with a 20s block design alternating rest and task. 1 block = 20s rest + 20s task. Task description :\n_task-MIpre : motor imagery run without NF. 8 blocks.\n_task-1dNF or _task-2dNF : bimodal neurofeedback, with either a mono-dimensional neurofeedback display (mean of EEG NF and fMRI NF scores), either a bi-dimensional display (one modality per dimension). The list of subjects with 1d or 2d is given above.\nEach subjects had 3 runs. 8 blocks per run.\n_task-MIpost : motor imagery run without NF. 8 blocks.\nSubjects with mono-dimensional feedback display :\nxp201 : 1D\nxp202 : 1D\nxp203 : 1D\nxp206 : 1D\nxp211 : 1D\nxp218 : 1D\nxp219 : 1D\nxp220 : 1D\nxp222 : 1D\n\nSubjects with bi-dimensional feedback display :\nxp204 : 2D\nxp205 : 2D\nxp207 : 2D\nxp210:  2D\nxp213 : 2D\nxp216 : 2D\nxp217 : 2D\nxp221 : 2D\n\n————————————————————————————————\nEEG DATA\n————————————————————————————————\nEEG data was recorded using a 64-channel MR compatible solution from Brain Products (Brain Products GmbH, Gilching, Germany).\n\nRAW EEG DATA\n\nEEG was sampled at 5kHz with FCz as the reference electrode and AFz as the ground electrode, and a resolution of 0.5 microV. Following the BIDs arborescence, raw eeg data for each task can be found for each subject in\n\nXP2/sub-xp2*/eeg\n\nin Brain Vision Recorder format (File Version 1.0). Each raw EEG recording includes three files: the data file (*.eeg), the header file (*.vhdr) and the marker file (*.vmrk). \nThe header file contains information about acquisition parameters and amplifier setup. For each electrode, the impedance at the beginning of the recording  is also specified. For all subjects, channel 32 is the ECG channel. The 63 other channels are EEG channels.\n\nThe marker file contains the list of markers assigned to the EEG recordings and their properties (marker type, marker ID and position in data points). Three type of markers are relevant for the EEG processing:\nR128 (Response): is the fMRI volume marker to correct for the gradient artifact\nS 99 (Stimulus): is the protocol marker indicating the start of the Rest block\nS  2 (Stimulus): is the protocol marker indicating the start of the Task (Motor Execution Motor Imagery or Neurofeedback)  \nWarning : in few EEG data, the first S99 marker might be missing, but can be easily “added” 20 s before the first S 2.  \n\nPREPROCESSED EEG DATA\n\nFollowing the BIDs arborescence, processed eeg data for each task can be found for each subject in\n\nXP2/derivatives/sub-xp2*/eeg_pp/*eeg_pp.*\n\nand following the Brain Analyzer format. Each processed EEG recording includes three files: the data file (*.dat), the header file (*.vhdr) and the marker file (*.vmrk), containing information similar to those described for raw data. In the header file of preprocessed data channels location are also specified. In the marker file the location in data points of the identified heart pulse (R marker) are specified as well. \n\nEEG data were pre-processed using BrainVision Analyzer II Software, with the following steps:\nAutomatic gradient artifact correction using the artifact template subtraction method (Sliding average calculation with 21 intervals for sliding average and all channels enabled for correction.\nDownsampling with factor: 25 (200 Hz)\nLow Pass FIR Filter:Cut-off Frequency: 50 Hz.\nBallistocardiogram (pulse) artifact correction using a semiautomatic procedure (Pulse Template searched between 40 s and 240 s in the ECG channel with the following parameters:Coherence Trigger = 0.5, Minimal Amplitude = 0.5, Maximal Amplitude = 1.3). A Pulse Artifact marker  R was associated to each identified pulse.\nSegmentation relative to the first block marker (S 99) for all the length of the training protocol (las S 2 + 20 s).\n\nEEG-NF SCORES\n\nNeurofeedback scores can be found in the .mat structures in\n\nXP2/derivatives/sub-xp2*/NF_eeg/d_sub*NFeeg_scores.mat\n\nStructures names NF_eeg are composed by the following subfields:\nID : Subject ID, for example sub-xp201\nlapC3_ERD : a 1x1280 vector of neurofeedback scores. 4 scores per secondes, for the whole session.\neeg : a 64x80200 matrix, with the pre-processed EEG signals with the step described above, filtered between 8 and 30 Hz.\nlapC3_bandpower_8Hz_30Hz : 1x1280 vector. Bandpower of the filtered signal with a laplacian centred on C3, used to estimate the lapC3_ERD.\nlapC3_filter : 1x64 vector. Laplacian filter centred above C3 channel.\n————————————————————————————————\nBOLD fMRI DATA\n————————————————————————————————\nAll DICOM files were converted to Nifti-1 and then in BIDs format (version 2.1.4) using the software dcm2niix (version v1.0.20190720 GVV7.4.0)\n\nfMRI acquisitions were performed using echo- planar imaging (EPI) and covered the superior half of the brain with the following parameters \n3T Siemens Verio\nEPI sequence\nTR=1 s\nTE=23 ms\nResolution 2x2x4 mm\nN of slices: 16\nNo slice gap\n\n\nAs specified in the relative task event files in XP2\\ *events.tsv files onset, the scanner began the EPI pulse sequence two seconds prior to the start of the protocol (first rest block), so the the first two TRs should be discarded. \n\nThe useful TRs for the runs are therefore\n\n-task-MIpre and task-MIpost: 320 s (2 to 302)\n-task-1dNF and task-2dNF:  320 s (2 to 302)\n\nIn task events files for the different tasks, each column represents:\n\n- 'onset': onset time (sec) of an event\n- 'duration': duration (sec) of the event\n- 'trial_type': trial (block) type: rest or task (Rest, Task-MI, Task-NF)\n- 'stim_file': image presented in a stimulus block. During Rest or  Motor Imagery (Task-MI) instructions were presented to the subject. On the other hand, during Neurofeedback blocks (Task-NF) the image presented was a ball moving in a square for the bidimensional NF (task-2dNF) or a ball moving along a gauge for the unidimensional NF (task-1dNF)  that the subject could control self-regulating his EEG and fMRI brain activity.\n\nFollowing the BIDs arborescence, the functional data and relative metadata are found for each subject in the following directory\n\nXP2/sub-xp2*/func\n\nBOLD-NF SCORES\n\nFor each subject and NF session, a matlab structure with BOLD-NF features can be found in \n\nXP2/derivatives/sub-xp2*/NF_bold/\n\nIn view of BOLD-NF scores computation, fMRI data were preprocessed using AutoMRI, a software based on spm8 and with the following steps: slice-time correction, sp","bids_version":"1.2.0","sessions_count":null,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-09-06 16:43:28","zarr_store_count":85,"zarr_index_etag":"05809d4578a57dea44584abede17751f","zarr_source_commit":"1305ba24a700a56e2dc664e6c95ed154474a6b74","archive_status":"ready","archive_size":24120660287,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":0,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":null,"num_dataset_citations":0,"num_datapaper_citations":0,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":27799994434,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":0,"total_recording_duration":30164.84,"recording_duration_min":330.46,"recording_duration_max":733.06,"recording_count":85,"recordings_unavailable":0,"recordings_measured":85,"channel_count_min":64,"channel_count_max":64,"sampling_frequency":5000,"power_line_frequency":50,"eeg_reference":"FCz","placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-21 04:41:40\",\"metadata_updated_at\":\"2026-06-21 04:41:44\",\"archive_checked_at\":\"2026-06-21 04:59:02\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-21 04:52:09\",\"citations_updated_at\":\"2026-09-10 03:00:21\",\"channel_montage_checked_at\":\"2026-06-28 23:07:18\",\"hed_checked_at\":\"2026-06-30 04:37:57\",\"data_checked_at\":\"2026-07-28 03:00:28\",\"availability_report_at\":\"2026-07-23 01:12:00\",\"signal_defaults_at\":\"2026-09-02 11:55:29\",\"recording_stats_at\":\"2026-09-07 03:00:55\"}","participants":17,"num_citations":0,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"25.89 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on002338/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}}