DS005811: eeg dataset, 19 subjects#

NOD-EEG

Access recordings and metadata through EEGDash.

Citation: Guohao Zhang, Ming Zhou, Shuyi Zhen, Shaohua Tang, Zheng Li, Zonglei Zhen (2025). NOD-EEG. 10.18112/openneuro.ds005811.v1.0.9

Modality: eeg Subjects: 19 Recordings: 448 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

Summary

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.

The MEG data’s accession number is ds005810.

View full README

Summary

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.

The MEG data’s accession number is ds005810.

Data Records

Directory Structure

The raw data from each subject are stored in the sub-subID directory, while preprocessed data and epoch data are stored in the following directories: - Preprocessed Data: derivatives/preprocessed/raw - Epoch Data: derivatives/preprocessed/epochs

Stimulus Images

The stimulus images used for MEG and EEG are identical and are stored in the stimuli/ImageNet directory. Images within this folder are named in the synsetID_imageID.JPEG Where: - synsetID is the ILSVRC category information. - imageID is the unique number for the image within that category.

The image metadata, including category information, is available in the table files under the stimuli/metadata directory.

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.csv Where 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/raw directory, named as: sub-subID_ses-sesID_task-ImageNet_run-runID_eeg_clean.fif. The epoch data derived from preprocessed data are stored within the derivatives/epochs directory. In this directory, all data for each subject are concatenated into a single file, labeled as: sub-subID_epo.fif The 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’s sub-subID_events.csv file.

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

Dataset Information#

Dataset ID	`DS005811`
Title	NOD-EEG
Author (year)	`Zhang2025_EEG`
Canonical	—
Importable as	`DS005811`, `Zhang2025_EEG`
Year	2025
Authors	Guohao Zhang, Ming Zhou, Shuyi Zhen, Shaohua Tang, Zheng Li, Zonglei Zhen
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005811.v1.0.9
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 19
Recordings: 448
Tasks: 1

Channels & sampling rate

Channels: 64 (440), 66 (8)
Sampling rate (Hz): 500.0 (288), 1000.0 (160)
Duration (hours): 23.7022

Electrode Layout#

Electrode layout — EEG · 62 sensors — 62 channels

Dataset Statistics#

Age distribution (n=19, range 18–26 yr)

152025

Sex distribution

8

11

Female Male Total: 19

Channel counts (ch)

6466

Sampling frequencies (Hz)

5001000

Total recording duration: 23 h 42 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS005811 class to access this dataset programmatically.

class eegdash.dataset.DS005811(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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)