EEGdash›NeMAR›NM000228
Iss. 228 · 356 subjects · 397 recordings · CC-BY 4.0
Dataset Brief · Nieuwland et al. 2018

NM000228: eeg dataset, 356 subjects#

Nieuwland et al. 2018: Multi-site N400 Replication Study

Citation: Mante S. Nieuwland, Stephen Politzer-Ahles, Evelien Heyselaar, Katrien Segaert, Emily Darley, Nina Kazanina, Sarah Von Grebmer Zu Wolfsthurn, Federica Bartolozzi, Vita Kogan, Aine Ito, Diane Mézière, Dale J. Barr, Guillaume A. Rousselet, Heather J. Ferguson, Simon Busch-Moreno, Xiao Fu, Jyrki Tuomainen, Eugenia Kulakova, E. Matthew Husband, David I. Donaldson, Zdenko Kohút, Shirley-Ann Rueschemeyer, Falk Huettig (2005). Nieuwland et al. 2018: Multi-site N400 Replication Study. 10.7554/eLife.33468

356-participant EEG dataset — Nieuwland et al. 2018: Multi-site N400 Replication Study.

Data & curation Mante S. Nieuwland · Stephen Politzer-Ahles · Evelien Heyselaar · Katrien Segaert · Emily Darley · Nina Kazanina · …
Year 2005 · Distributed via NeMAR
Funding Max Planck Society · Economic and Social Research Council (ESRC) grants · + 1 more
EEG · 66 (81), 32 (78), 73 (77), 65 (43), 41 (40), 64 (38), 144 (37), 138 (3) ch250, 500, 512, 1000, 2048 HzBIDS 1.9.02 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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000228

dataset = NM000228(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000228(cache_dir="./data", subject="01")

Advanced query

dataset = NM000228(
    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{nm000228,
  title = {Nieuwland et al. 2018: Multi-site N400 Replication Study},
  author = {Mante S. Nieuwland and Stephen Politzer-Ahles and Evelien Heyselaar and Katrien Segaert and Emily Darley and Nina Kazanina and Sarah Von Grebmer Zu Wolfsthurn and Federica Bartolozzi and Vita Kogan and Aine Ito and Diane Mézière and Dale J. Barr and Guillaume A. Rousselet and Heather J. Ferguson and Simon Busch-Moreno and Xiao Fu and Jyrki Tuomainen and Eugenia Kulakova and E. Matthew Husband and David I. Donaldson and Zdenko Kohút and Shirley-Ann Rueschemeyer and Falk Huettig},
  doi = {10.7554/eLife.33468},
  url = {https://doi.org/10.7554/eLife.33468},
}
§ 02Study · The README

About This Dataset#

This is a large-scale (N=356) multi-laboratory replication of DeLong, Urbach &

Kutas (2005), testing whether readers pre-activate the phonological form of upcoming nouns during sentence comprehension. Participants read sentences word-by-word (RSVP, 2 words per second) that contained indefinite articles (a/an) preceding either highly expected or unexpected nouns (based on cloze probability), while EEG was recorded.

Nine laboratories in the UK collected data following a pre-registered

replication protocol (https://osf.io/eyzaq). The original study by DeLong et al. reported N400-like effects on the indefinite articles (larger negativity for unexpected articles). Nieuwland et al. found reliable N400 effects on the target nouns but no statistically significant effect on the preceding articles, challenging strong prediction accounts.

Nieuwland et al. 2018: Multi-site N400 Replication Study

Overview

Participants

  • 356 total participants (222 women / 134 men)

  • All right-handed, native English speakers

  • Age 18–35 years (mean 19.8)

View full README

Nieuwland et al. 2018: Multi-site N400 Replication Study

Overview

Participants

  • 356 total participants (222 women / 134 men)

  • All right-handed, native English speakers

  • Age 18–35 years (mean 19.8)

  • Normal or corrected-to-normal vision

  • Free from known language or learning disorders

  • 89 reported a left-handed parent or sibling

After applying the paper’s quality threshold (<60/80 article or noun trials), 334 subjects were retained in the statistical analyses. In this BIDS release we include ALL subjects for which raw data is available, with an included_in_paper flag in participants.tsv so users can filter themselves.

Laboratories

| Lab (paper #) | Institution                | Format       | Sfreq    | Channels          |
|---------------|----------------------------|--------------|----------|-------------------|
| BIRM (1)      | University of Birmingham   | BrainVision  | 500 Hz   | 64 EEG            |
| BRIS (2)      | University of Bristol      | BrainVision  | 1000 Hz  | 32 EEG            |
| EDIN (3)      | University of Edinburgh    | BioSemi BDF  | 512 Hz   | 64 EEG + 8 EXG    |
| GLAS (4)      | University of Glasgow      | BioSemi BDF  | 512 Hz   | 128 EEG + 8 EXG   |
| KENT (5)      | University of Kent         | BrainVision  | 500 Hz   | 64 EEG + HEOG/VEOG|
| LOND (6)      | University College London  | BioSemi BDF  | 512 Hz   | 32 EEG + 8 EXG    |
| OXFO (7)      | University of Oxford       | BioSemi BDF  | 2048 Hz  | 64 EEG + 8 EXG    |
| STIR (8)      | University of Stirling     | Neuroscan CNT| 250 Hz   | 64 EEG + EOG      |
| YORK (9)      | University of York         | BrainVision  | 500 Hz   | 64 EEG + HEOG/VEOG|

Paradigm

  • Word-by-word RSVP: 200 ms word duration + 300 ms blank (2 words/sec)

  • 80 Delong replication sentences + 80 control sentences

  • Comprehension questions on a subset of trials (yes/no button response)

  • Two counter-balanced stimulus lists (list 1 / list 2)

Tasks

  • task-delong: Main experiment (all subjects, all labs)

  • task-control: Control grammaticality experiment (BRIS subjects, LOND 1-2)

Events (trial_type values)

Delong experiment:

a_expected — article “a”, expected (high cloze) context an_expected — article “an”, expected (high cloze) context a_unexpected — article “a”, unexpected (low cloze) context an_unexpected — article “an”, unexpected (low cloze) context noun_expected — target noun, expected condition noun_unexpected — target noun, unexpected condition final_expected — sentence-final word, expected condition final_unexpected — sentence-final word, unexpected condition

Control experiment:

control_correct — grammatically correct article control_incorrect — grammatically incorrect article

General:

cloze_marker — cloze probability marker (trigger 1-100 or 200) item_marker — stimulus item marker (trigger 101-180) question — comprehension question onset filler_word — any other (non-critical) word in sentence unknown_trigger — trigger code not matched to any known category

Event enrichment

Each event in events.tsv is enriched (when applicable) with:

  • sequence_id, item_number, list, task_type, condition

  • expected_article / unexpected_article (a or an)

  • expected_noun / unexpected_noun (strings)

  • expected_cloze / unexpected_cloze (0-100)

  • plausibility_expected / plausibility_unexpected (1-7 Likert)

  • sentence_context / sentence_ending (strings)

  • has_question, question_text, question_answer

These come from the authors’ REPLICATION_ITEMS.xlsx file on OSF.

participants.tsv columns

participant_id — sub-<lab><num> lab — birm/bris/edin/glas/kent/lond/oxfo/stir/york lab_number — 1-9 (paper’s numbering) institution — full institution name list — stimulus list (1 or 2) accuracy — % correct on comprehension questions (from OSF) n_article_trials — article trials kept (out of 80) n_noun_trials — noun trials kept (out of 80) included_in_paper — True if >=60/80 trials (paper’s threshold) exclusion_note — e.g. “random_answers”, “non_native”, “low_trials” hand — R (all right-handed) age_range — 18-35 (all participants) native_language — English (all participants) recording_system — manufacturer + model

Notes

  • Original raw data is kept — no filtering, no resampling, no artifact rejection

  • Channel types: EEG, EOG, and misc (peripheral) channels are labeled

  • For BDF labs, channels EXG1-8, GSR1/2, Erg1/2, Resp, Plet, Temp are marked misc

  • GLAS has a 128-channel BioSemi montage (biosemi128)

  • STIR data is read with a custom Neuroscan CNT parser (MNE’s built-in reader has a bug with the corrupted total_samples header field)

  • OXFO has 3 subjects recorded with BrainVision instead of BDF

Reference

Nieuwland, M.S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., …, Huettig, F. (2018). Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468. https://doi.org/10.7554/eLife.33468

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 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

324164656673138144

Sampling frequencies (Hz)

25050051210002048

Total recording duration: 232 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 66 (81), 32 (78), 73 (77), 65 (43), 41 (40), 64 (38), 144 (37), 138 (3) ch · EEG · 250, 500, 512, 1000, 2048 Hz · 356 subjects, 397 recordings
Live trace viewer — sub-kent0034 · task-delong

Showing one representative recording out of 356 subjects and 397 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.

NEMAR pipeline statistics — NM000228
HED event descriptors word cloud HED event descriptors word cloud — NM000228
§ 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…
Full dataset metadata table

Dataset ID

NM000228

Title

Nieuwland et al. 2018: Multi-site N400 Replication Study

Author (year)

Nieuwland2018

Canonical

—

Importable as

NM000228, Nieuwland2018

Year

2005

Authors

Mante S. Nieuwland, Stephen Politzer-Ahles, Evelien Heyselaar, Katrien Segaert, Emily Darley, Nina Kazanina, Sarah Von Grebmer Zu Wolfsthurn, Federica Bartolozzi, Vita Kogan, Aine Ito, Diane Mézière, Dale J. Barr, Guillaume A. Rousselet, Heather J. Ferguson, Simon Busch-Moreno, Xiao Fu, Jyrki Tuomainen, Eugenia Kulakova, E. Matthew Husband, David I. Donaldson, Zdenko Kohút, Shirley-Ann Rueschemeyer, Falk Huettig

License

CC-BY 4.0

Citation / DOI

doi:10.7554/eLife.33468

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000228,
  title = {Nieuwland et al. 2018: Multi-site N400 Replication Study},
  author = {Mante S. Nieuwland and Stephen Politzer-Ahles and Evelien Heyselaar and Katrien Segaert and Emily Darley and Nina Kazanina and Sarah Von Grebmer Zu Wolfsthurn and Federica Bartolozzi and Vita Kogan and Aine Ito and Diane Mézière and Dale J. Barr and Guillaume A. Rousselet and Heather J. Ferguson and Simon Busch-Moreno and Xiao Fu and Jyrki Tuomainen and Eugenia Kulakova and E. Matthew Husband and David I. Donaldson and Zdenko Kohút and Shirley-Ann Rueschemeyer and Falk Huettig},
  doi = {10.7554/eLife.33468},
  url = {https://doi.org/10.7554/eLife.33468},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000228(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Nieuwland2018
Canonical—
Importable asNM000228 · Nieuwland2018
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000228(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Nieuwland et al. 2018: Multi-site N400 Replication Study

Study:

nm000228 (NeMAR)

Author (year):

Nieuwland2018

Canonical:

—

Also importable as: NM000228, Nieuwland2018.

Modality: eeg. Subjects: 356; recordings: 397; tasks: 2.

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/nm000228 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000228 DOI: https://doi.org/10.7554/eLife.33468

Examples

>>> from eegdash.dataset import NM000228
>>> dataset = NM000228(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 — NM000228.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 nm000228 to reproduce the tutorial on this dataset.

Citation

Mante S. Nieuwland, Stephen Politzer-Ahles, Evelien Heyselaar, Katrien Segaert, Emily Darley, … (2005). Nieuwland et al. 2018: Multi-site N400 Replication Study. 10.7554/eLife.33468

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.7554/eLife.33468.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY 4.0 · 10.7554/eLife.33468
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#