ON007554: eeg, fnirs dataset, 30 subjects#
Multimodal dataset from the CMx7-MM Experiment
Access recordings and metadata through EEGDash.
Citation: Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey, Lilian Evra, Simon Pla, Gérard Dray, Jacky Montmain, Binbin Xu (—). Multimodal dataset from the CMx7-MM Experiment. 10.82901/nemar.on007554
Modality: eeg, fnirs Subjects: 30 Recordings: 1034 License: CC0 Source: nemar
Metadata: Complete (100%)
30-participant EEG, fNIRS dataset — Multimodal dataset from the CMx7-MM Experiment.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007554
dataset = ON007554(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007554(cache_dir="./data", subject="01")
Advanced query
dataset = ON007554(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{on007554,
title = {Multimodal dataset from the CMx7-MM Experiment},
author = {Zaineb Ajra and Grégoire Vergotte and Stéphane Perrey and Lilian Evra and Simon Pla and Gérard Dray and Jacky Montmain and Binbin Xu},
doi = {10.82901/nemar.on007554},
url = {https://doi.org/10.82901/nemar.on007554},
}
About This Dataset#
This repository contains a raw multimodal dataset acquired in healthy adults performing a hierarchy of cognitive, motor, and combined cognitive-motor tasks. Data include neurophysiological (EEG, fNIRS), physiological (ECG and EMG), behavioral (push-button, torque), and subjective measures (sleepiness and cognitive load ratings), organized according to the Brain Imaging Data Structure (BIDS).
Mental arithmetic (MA)
N-back (NB, 2-back, auditory)
Motor imagery (MI)
Passive motor (Pass-Mot, Biodex-driven arm movement)
Active motor (Act-Mot, voluntary movement with Biodex)
N-back arithmetic (NB-MA, combined N-back and mental arithmetic)
Full task (NB-MA-Act-Mot, combined cognitive-motor condition)
Multimodal EEG-fNIRS-physio dataset during hierarchical cognitive-motor tasks
All recordings shared here are raw exports from the acquisition systems with no offline preprocessing applied.
2. Experimental paradigm
Each participant performed the seven tasks within each session. Tasks were presented in random order, with: - Task duration: 3 minutes per task
View full README
Multimodal EEG-fNIRS-physio dataset during hierarchical cognitive-motor tasks
All recordings shared here are raw exports from the acquisition systems with no offline preprocessing applied.
2. Experimental paradigm
Each participant performed the seven tasks within each session. Tasks were presented in random order, with: - Task duration: 3 minutes per task - Inter-task rest: 30 seconds - Stimuli: auditory digits (0-9) and beeps, delivered via MATLAB/Psychtoolbox
Brief task descriptions: - MA (Mental arithmetic)
Auditory digits (0-9). On each trial, participants add or subtract numbers so that the result stays in the 0-9 range. - 72 events per task - Event duration: 1.5 s - Inter-event interval: 2.5 s
NB (N-back, 2-back) Auditory digits (0-9). Participants press a button when the current digit matches the digit presented two trials earlier. - 72 events total, 18 targets - Event duration: 0.5 s - Inter-event interval: 1.5 s
MI (Motor imagery) Participants imagine moving the right arm (no actual movement) when they hear a beep. - 18 targets - Inter-target interval: 10 � 3 s
Pass-Mot (Passive motor) The participant�s right arm is moved by the Biodex device (external and internal rotation, 60� range). - Movement duration: 2 s - 18 movements per task - Inter-movement interval: 10 � 3 s
Act-Mot (Active motor) Participants actively move the robotic arm in response to beeps. - 18 movements per task - Inter-target interval: 10 � 3 s
NB-MA (N-back arithmetic) Participants press a button when the running sum of the last digits corresponds to the 2-back condition (combined N-back and arithmetic). - 72 events total, 18 targets - Event duration: 1.5 s - Inter-event interval: 2.5 s
Full task (NB-MA-Act-Mot) Same cognitive demands as NB-MA, but participants move their arm (instead of pressing a button) when the NB-MA condition is met. - 72 events total, 18 targets - Event duration: 1.5 s - Inter-event interval: 2.5 s
Subjective ratings: - Karolinska Sleepiness Scale (KSS) before each session - Mental load ratings after each task (9-point Likert and visual analogue scale)
Cohort#
Dataset Statistics#
Age distribution by gender (n=19, range 21–40 yr, mean 25.5 yr)
Sex composition
Channel counts: 32 ch (n=1034 recordings)
Sampling frequencies (Hz)
Total recording duration: 61 h
Signal · Electrodes & live trace#
Live trace viewer — sub-001 · ses-03 · task-activemotor
Showing one representative recording out of
30 subjects and 1034 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 32 sensors — 32 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Multimodal dataset from the CMx7-MM Experiment |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey, Lilian Evra, Simon Pla, Gérard Dray, Jacky Montmain, Binbin Xu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007554,
title = {Multimodal dataset from the CMx7-MM Experiment},
author = {Zaineb Ajra and Grégoire Vergotte and Stéphane Perrey and Lilian Evra and Simon Pla and Gérard Dray and Jacky Montmain and Binbin Xu},
doi = {10.82901/nemar.on007554},
url = {https://doi.org/10.82901/nemar.on007554},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Multimodal dataset from the CMx7-MM Experiment
- Study:
on007554(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON007554.Modality:
eeg, fnirs; Subject type:Unknown. Subjects: 30; recordings: 1034; tasks: 7.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/on007554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007554 DOI: https://doi.org/10.82901/nemar.on007554
Examples
>>> from eegdash.dataset import ON007554 >>> dataset = ON007554(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for on007554 to reproduce the tutorial on this dataset.
Citation
Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey, Lilian Evra, Simon Pla, … (n.d.). Multimodal dataset from the CMx7-MM Experiment. 10.82901/nemar.on007554
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007554.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset