DS005811: eeg dataset, 19 subjects#
NOD-EEG
Citation: Guohao Zhang, Ming Zhou, Shuyi Zhen, Shaohua Tang, Zheng Li, Zonglei Zhen (2019). NOD-EEG. 10.18112/openneuro.ds005811.v1.0.9
19-participant EEG dataset — NOD-EEG.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005811
dataset = DS005811(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005811(cache_dir="./data", subject="01")
Advanced query
dataset = DS005811(
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{ds005811,
title = {NOD-EEG},
author = {Guohao Zhang and Ming Zhou and Shuyi Zhen and Shaohua Tang and Zheng Li and Zonglei Zhen},
doi = {10.18112/openneuro.ds005811.v1.0.9},
url = {https://doi.org/10.18112/openneuro.ds005811.v1.0.9},
}
About This Dataset#
The human brain can rapidly recognize meaningful objects from natural scenes encountered in everyday life. Neuroimaging with large-scale naturalistic stimuli is increasingly employed to elucidate these neural mechanisms of object recognition across these rich and daily natural scenes. However, most existing large-scale neuroimaging datasets with naturalistic stimuli primarily rely on functional magnetic resonance imaging (fMRI), which provides high spatial resolution to characterize spatial representation patterns but is limited in capturing the temporal dynamics inherent in visual cognitive processing.
To address this limitation, we extended our previously collected Natural Object Dataset-fMRI (NOD-fMRI) by collecting both magnetoencephalography (MEG) and electroencephalography (EEG) data from the same subjects while viewing the same set of naturalistic stimuli. As a result, the NOD uniquely integrates three different modalities—fMRI, MEG, and EEG—thus offering promising avenues to examine brain activity induced by naturalistic stimuli with both high spatial and high temporal resolutions. Additionally, the NOD encompasses a diverse array of naturalistic stimuli and a broader subject pool, enabling researchers to explore differences in neural activation patterns across both stimuli and subjects.
We anticipate that the NOD dataset will serve as a valuable resource for advancing our understanding of the cognitive and neural mechanisms underlying object recognition.
Summary
The MEG data’s accession number is ds005810.
Data Records
Directory Structure
View full README
Summary
The MEG data’s accession number is ds005810.
Data Records
Directory Structure
The raw data from each subject are stored in the
sub-subIDdirectory, while preprocessed data and epoch data are stored in the following directories: - Preprocessed Data:derivatives/preprocessed/raw- Epoch Data:derivatives/preprocessed/epochsStimulus Images
The stimulus images used for MEG and EEG are identical and are stored in the
stimuli/ImageNetdirectory. Images within this folder are named in thesynsetID_imageID.JPEGWhere: -synsetIDis the ILSVRC category information. -imageIDis the unique number for the image within that category.The image metadata, including category information, is available in the table files under the
stimuli/metadatadirectory.Raw Data
Raw EEG data are stored in BIDS format. Each subject’s directory contains multiple session folders, designated as
ses-sesID. Comprehensive trial information for each subject is documented in the file:derivatives/detailed_events/sub-subID_events.csvWhere each row corresponds to a trial, and each column contains metadata for that trial, including the session and run number, category information of the stimuli, and subject response.Preprocessed Data
The full time series data of preprocessed data are archived in the
derivatives/rawdirectory, named as:sub-subID_ses-sesID_task-ImageNet_run-runID_eeg_clean.fif. The epoch data derived from preprocessed data are stored within thederivatives/epochsdirectory. In this directory, all data for each subject are concatenated into a single file, labeled as:sub-subID_epo.fifThe trial information within each subject’s epochs data can be accessed via the metadata of the epochs data, which are aligned with the content of the subject’ssub-subID_events.csvfile.References
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A., and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. *Journal of Open Source Software, 4*(1896). https://doi.org/10.21105/joss.01896
Cohort#
Dataset Statistics#
Age distribution by gender (n=19, range 18–26 yr, mean 21.3 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 23 h 42 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-ImageNet01 · task-ImageNet · run-08
Showing one representative recording out of
19 subjects and 448 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 · 62 sensors — 62 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 |
NOD-EEG |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Guohao Zhang, Ming Zhou, Shuyi Zhen, Shaohua Tang, Zheng Li, Zonglei Zhen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005811,
title = {NOD-EEG},
author = {Guohao Zhang and Ming Zhou and Shuyi Zhen and Shaohua Tang and Zheng Li and Zonglei Zhen},
doi = {10.18112/openneuro.ds005811.v1.0.9},
url = {https://doi.org/10.18112/openneuro.ds005811.v1.0.9},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005811 · Zhang2025_EEGeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005811(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
NOD-EEG
- Study:
ds005811(OpenNeuro)- Author (year):
Zhang2025_EEG- Canonical:
—
Also importable as:
DS005811,Zhang2025_EEG.Modality:
eeg; Experiment type:Perception; Subject type:Healthy. Subjects: 19; recordings: 448; tasks: 1.- 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/ds005811 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005811 DOI: https://doi.org/10.18112/openneuro.ds005811.v1.0.9
Examples
>>> from eegdash.dataset import DS005811 >>> dataset = DS005811(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.pytorchdatasets.load_dataset("EEGDash/ds005811").huggingface
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 ds005811 to reproduce the tutorial on this dataset.
Citation
Guohao Zhang, Ming Zhou, Shuyi Zhen, Shaohua Tang, Zheng Li, … (2019). NOD-EEG. 10.18112/openneuro.ds005811.v1.0.9
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005811.v1.0.9.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset