NM000228: eeg dataset, 356 subjects#

Name: Nieuwland et al. 2018: Multi-site N400 Replication Study
Published: 2005
License: CC-BY 4.0

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

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 356 Recordings: 397 License: CC-BY 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

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

Overview

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

View full README

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

Overview

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.

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

Dataset Information#

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

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: 356
Recordings: 397
Tasks: 2

Channels & sampling rate

Channels: 66 (81), 32 (78), 73 (77), 65 (43), 41 (40), 64 (38), 144 (37), 138 (3)
Sampling rate (Hz): 500 (122), 512 (116), 1000 (78), 2048 (41), 250 (40)
Duration (hours): 232.39741483832464

API Reference#

Use the NM000228 class to access this dataset programmatically.

class eegdash.dataset.NM000228(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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, 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