EEGdash›NeMAR›NM000278

Iss. 278 · 12 subjects · 273 recordings · CC-BY-4.0

Dataset Brief · ZuCo 1.0

NM000278: eeg dataset, 12 subjects#

ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading

Access recordings and metadata through EEGDash.

Citation: Nora Hollenstein, Jonathan Rotsztejn, Marius Tröndle, Andreas Pedroni, Ce Zhang, Nicolas Langer (2018). ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading. 10.82901/nemar.nm000278

Modality: eeg Subjects: 12 Recordings: 273 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

12-participant EEG dataset — ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading.

Data & curation Nora Hollenstein · Jonathan Rotsztejn · Marius Tröndle · Andreas Pedroni · Ce Zhang · Nicolas Langer
Senior author Nicolas Langer
Year 2018 · Distributed via NeMAR

EEG · 128 ch500 HzBIDS 1.9.03 tasks

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000278

dataset = NM000278(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 = NM000278(cache_dir="./data", subject="01")

Advanced query

dataset = NM000278(
    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{nm000278,
  title = {ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading},
  author = {Nora Hollenstein and Jonathan Rotsztejn and Marius Tröndle and Andreas Pedroni and Ce Zhang and Nicolas Langer},
  doi = {10.82901/nemar.nm000278},
  url = {https://doi.org/10.82901/nemar.nm000278},
}

§ 02Study · The README

About This Dataset#

Simultaneous 128-channel EEG (EGI Geodesic, 500 Hz, Cz reference) and eye-tracking (EyeLink 1000, 500 Hz, left eye) while adult native English speakers read natural sentences.

Tasks: sr (Sentiment reading (task 1): reading movie-review sentences and rating sentiment.), nr (Normal reading (task 2): naturalistic reading of Wikipedia sentences.), tsr (Task-specific reading (task 3): reading Wikipedia sentences while annotating specific semantic relations.)

Each reading block is a run; eye-tracking gaze/pupil samples are co-located _recording-eyetrack_physio, fixations/saccades/blinks are in events.tsv.

DOI

ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading

Cite: Hollenstein, N., Rotsztejn, J., Troendle, M., Pedroni, A., Zhang, C., & Langer, N. (2018). ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading. Scientific Data, 5, 180291.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000278-blue)](https://doi.org/10.82901/nemar.nm000278) # ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading Simultaneous 128-channel EEG (EGI Geodesic, 500 Hz, Cz reference) and eye-tracking (EyeLink 1000, 500 Hz, left eye) while adult native English speakers read natural sentences. Tasks: sr (Sentiment reading (task 1): reading movie-review sentences and rating sentiment.), nr (Normal reading (task 2): naturalistic reading of Wikipedia sentences.), tsr (Task-specific reading (task 3): reading Wikipedia sentences while annotating specific semantic relations.) Each reading block is a run; eye-tracking gaze/pupil samples are co-located _recording-eyetrack_physio, fixations/saccades/blinks are in events.tsv. Cite: Hollenstein, N., Rotsztejn, J., Troendle, M., Pedroni, A., Zhang, C., & Langer, N. (2018). ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading. Scientific Data, 5, 180291.

License: CC-BY-4.0

Authors:

Nora Hollenstein
Jonathan Rotsztejn
Marius Tröndle
Andreas Pedroni
Ce Zhang
… and 1 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000278

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 128 ch (n=273 recordings)

Sampling frequencies: 500.0 Hz (n=273 recordings)

Total recording duration: 22 h 9 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 500 Hz · 12 subjects, 273 recordings

Live trace viewer — sub-ZAB · task-nr · run-1

Showing one representative recording out of 12 subjects and 273 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 · 128 sensors — 128 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000278(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000278

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000278(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading

Study:: nm000278 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000278.

Modality: eeg; Subject type: Unknown. Subjects: 12; recordings: 273; tasks: 3.

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/nm000278 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000278 DOI: https://doi.org/10.82901/nemar.nm000278

Examples

>>> from eegdash.dataset import NM000278
>>> dataset = NM000278(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000278.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 nm000278 to reproduce the tutorial on this dataset.

Citation

Nora Hollenstein, Jonathan Rotsztejn, Marius Tröndle, Andreas Pedroni, Ce Zhang, … (2018). ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading. 10.82901/nemar.nm000278

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000278.

Related & sibling datasets

NM000279EEG · 18 subj ON005262EEG · 12 subj ON001971EEG · 20 subj ON004408EEG · 19 subj ON005170EEG · 5 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000278

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000278`
Title	ZuCo 1.0: Simultaneous EEG and Eye-Tracking during Natural Reading
Author (year)	—
Canonical	—
Importable as	`NM000278`
Year	2018
Authors	Nora Hollenstein, Jonathan Rotsztejn, Marius Tröndle, Andreas Pedroni, Ce Zhang, Nicolas Langer
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000278
Source links	OpenNeuro \| NeMAR \| Source URL

NM000278: eeg dataset, 12 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#