NM000232: eeg dataset, 10 subjects#

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Access recordings and metadata through EEGDash.

Citation: Alessandro T. Gifford, Kshitij Dwivedi, Gemma Roig, Radoslaw M. Cichy (2022). THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition. 10.17605/OSF.IO/3JK45

Modality: eeg Subjects: 10 Recordings: 638 License: CC-BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000232

dataset = NM000232(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000232(cache_dir="./data", subject="01")

Advanced query

dataset = NM000232(
    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{nm000232,
  title = {THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition},
  author = {Alessandro T. Gifford and Kshitij Dwivedi and Gemma Roig and Radoslaw M. Cichy},
  doi = {10.17605/OSF.IO/3JK45},
  url = {https://doi.org/10.17605/OSF.IO/3JK45},
}

About This Dataset#

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Overview

EEG dataset of 10 subjects who viewed 16,540 distinct training images and 200 test images (each repeated ~80 times) using rapid serial visual presentation (RSVP) at 5 Hz, recorded on a BrainVision actiCHamp system at 1000 Hz. The source files store 63 EEG channels (the online reference electrode is not stored). Stimuli are drawn from the THINGS database (Hebart et al. 2019). Each subject completed 4 separate sessions; each session contained:

View full README

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Overview

EEG dataset of 10 subjects who viewed 16,540 distinct training images and 200 test images (each repeated ~80 times) using rapid serial visual presentation (RSVP) at 5 Hz, recorded on a BrainVision actiCHamp system at 1000 Hz. The source files store 63 EEG channels (the online reference electrode is not stored). Stimuli are drawn from the THINGS database (Hebart et al. 2019). Each subject completed 4 separate sessions; each session contained:

  • 5 training runs (~3,360 trials each) covering ~16,540 unique images

  • 1 test run (~4,080 trials) of 200 images repeated 20× per session

  • 2 resting-state runs (one before, one after the main experiment)

Total: ~32,540 training trials + ~16,000 test trials per subject across 4 sessions.

Recording setup

  • Manufacturer: Brain Products (actiCHamp)

  • 63 EEG channels (one electrode served as online reference and is not stored in the source files)

  • 10-10 cap layout

  • Sampling rate: 1000 Hz

  • Online band-pass: 0.01-100 Hz

  • Triggers recorded as BrainVision stimulus annotations (not as a dedicated stim channel)

Tasks (BIDS labels)

  • task-train: training run (RSVP of unique images)

  • task-test: test run (RSVP of repeated test images)

  • task-rest: resting state (eyes open, fixation cross)

Run numbering

  • task-train: run-01..run-05 per session (5 training parts)

  • task-test: single run per session

  • task-rest: run-01 (before main task) and run-02 (after main task)

Events

events.tsv columns:

onset, duration, sample, value, trial_type tot_img_number - global image ID (1-16540 for train; 1-200 for test;

‘n/a’ for target catch trials)

img_category - integer category index category_name - human-readable category, e.g. “01175_roller_coaster” block, sequence - hierarchical position within the run img_in_sequence - image position within its 20-image sequence soa - actual stimulus onset asynchrony (~200 ms)

trial_type values:

image - normal training/test image presentation target - random catch trial (subject must press a button) rest_marker - resting-state start/end marker

Subject information

participants.tsv contains age and sex (both extracted from the behavioural .mat files in the source data).

Folder layout

/sub-XX/ses-YY/eeg/ - main BIDS data (BDF + sidecars) /sourcedata/ - original BrainVision .eeg/.vhdr/.vmrk and

behavioural .mat files

/derivatives/preprocessed_eeg/ - authors’ preprocessed train/test epochs /derivatives/resting_state/ - authors’ preprocessed resting state /stimuli/ - image set (training_images.zip, test_images.zip)

plus image_metadata.npy

/code/ - this conversion script

Reference

Gifford, A.T., Dwivedi, K., Roig, G., & Cichy, R.M. (2022). A large and rich EEG dataset for modeling human visual object recognition. NeuroImage, 264, 119754. https://doi.org/10.1016/j.neuroimage.2022.119754

Code: https://github.com/gifale95/eeg_encoding OSF: https://osf.io/3jk45/

Dataset Information#

Dataset ID

NM000232

Title

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Author (year)

Gifford2019

Canonical

—

Importable as

NM000232, Gifford2019

Year

2022

Authors

Alessandro T. Gifford, Kshitij Dwivedi, Gemma Roig, Radoslaw M. Cichy

License

CC-BY 4.0

Citation / DOI

doi:10.17605/OSF.IO/3JK45

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000232,
  title = {THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition},
  author = {Alessandro T. Gifford and Kshitij Dwivedi and Gemma Roig and Radoslaw M. Cichy},
  doi = {10.17605/OSF.IO/3JK45},
  url = {https://doi.org/10.17605/OSF.IO/3JK45},
}

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: 10

  • Recordings: 638

  • Tasks: 5

Channels & sampling rate

  • Channels: 63

  • Sampling rate (Hz): 1000

  • Duration (hours): 87.2788263888889

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 203.9 GB

  • File count: 638

  • Format: BIDS

License & citation

  • License: CC-BY 4.0

  • DOI: doi:10.17605/OSF.IO/3JK45

Provenance

API Reference#

Use the NM000232 class to access this dataset programmatically.

class eegdash.dataset.NM000232(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition

Study:

nm000232 (NeMAR)

Author (year):

Gifford2019

Canonical:

—

Also importable as: NM000232, Gifford2019.

Modality: eeg. Subjects: 10; recordings: 638; tasks: 5.

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/nm000232 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000232 DOI: https://doi.org/10.17605/OSF.IO/3JK45

Examples

>>> from eegdash.dataset import NM000232
>>> dataset = NM000232(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#