EEGdash›NeMAR›NM000480
Iss. 480 · 11 subjects · 11 recordings · CC-BY-4.0
Dataset Brief · s500-literature-171

NM000480: ieeg dataset, 11 subjects#

s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative)

Access recordings and metadata through EEGDash.

Citation: Elizabeth L. Johnson, Jack J. Lin, David King-Stephens, Peter B. Weber, Kenneth D. Laxer, Ignacio Saez, Fady Girgis, Mark D’Esposito, Robert T. Knight, David Badre (2023). s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative). 10.82901/nemar.nm000480

Modality: ieeg Subjects: 11 Recordings: 11 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

11-participant iEEG dataset — s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative).

Data & curation Elizabeth L. Johnson · Jack J. Lin · David King-Stephens · Peter B. Weber · Kenneth D. Laxer · Ignacio Saez · …
Senior author David Badre
Year 2023 · Distributed via NeMAR
iEEG · 49 (2), 42 (2), 5, 29, 33, 43, 80, 57, 20 ch1000 Hz · mixedBIDS 1.10.0Task · wmgating
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 NM000480

dataset = NM000480(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000480(cache_dir="./data", subject="01")

Advanced query

dataset = NM000480(
    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{nm000480,
  title = {s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative)},
  author = {Elizabeth L. Johnson and Jack J. Lin and David King-Stephens and Peter B. Weber and Kenneth D. Laxer and Ignacio Saez and Fady Girgis and Mark D'Esposito and Robert T. Knight and David Badre},
  doi = {10.82901/nemar.nm000480},
  url = {https://doi.org/10.82901/nemar.nm000480},
}
§ 02Study · The README

About This Dataset#

Preprocessed, bipolar-referenced intracranial EEG from 11 neurosurgical patients (11 recordings, 1.56 h of

epochs) performing a single-trial working-memory gating task (context-first / context-last), released by the authors on OSF (https://osf.io/rx2zd/, CC-BY 4.0) together with the analysis scripts. THIS IS A DERIVATIVE DATASET: the release holds the authors’ epoched, filtered, artifact-cleaned bipolar channels (article Methods), not the raw clinical recordings.

per trial; events.tsv gives each trial’s position, its authors’ time axis start (-1 s) and every trialinfo field as

released: trl_num, context, congruency, stimuli, rt, acc, Rel, Irrel, stim1, stim2, stim3, digitStimuli, digit_position, target_position, distractor_position, s2_onset, s3_onset, resp_onset). Unit µV assumed (not stated in the release); values unchanged.

DOI

Johnson et al. (2023), “A rapid theta network mechanism for flexible information encoding”: epoched iEEG

  • Channel names are the bipolar pairs (e.g. LF9-LF1). The channel type is INFERRED from the pair numbering following the article’s montage rule (sEEG: deep-surface neighbours, adjacent numbers; ECoG grids: row-by-row neighbours, non-adjacent numbers); the release itself does not state the electrode type per channel.

  • sub-NN_space-Other_electrodes.tsv: data.elec.chanpos of each bipolar channel as released. The release does not state whether chanpos is in native or MNI space (the article localizes in native space and transforms to MNI for group display), hence space-Other; see coordsystem.json. The per-subject yeo.mat (Yeo-7 network per task-responsive electrode) lists fewer entries than channels and carries no channel names, so it could not be mapped to channels; it is kept in sourcedata/ (counts per subject in code/conversion_report.json, key yeo_unmapped). Where its length matches the channel count the network is written to the yeo7_network column.

  • participants.tsv: no per-subject age or sex in the release (article Table S1).

  • sourcedata/osf-rx2zd/: every release file (data incl. behavior.mat, trialinfo.mat, scripts) unchanged apart from MAT text-header dates reduced to month/year; DEIDENTIFICATION_MANIFEST.tsv lists sha-256 before and after.

Privacy

Subjects are numbered S1-S11; files hold trial parameters, responses and reaction times only.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000480-blue)](https://doi.org/10.82901/nemar.nm000480) # Johnson et al. (2023), “A rapid theta network mechanism for flexible information encoding”: epoched iEEG Preprocessed, bipolar-referenced intracranial EEG from 11 neurosurgical patients (11 recordings, 1.56 h of epochs) performing a single-trial working-memory gating task (context-first / context-last), released by the authors on OSF (https://osf.io/rx2zd/, CC-BY 4.0) together with the analysis scripts. THIS IS A DERIVATIVE DATASET: the release holds the authors’ epoched, filtered, artifact-cleaned bipolar channels (article Methods), not the raw clinical recordings. ## Contents - sub-NN/ieeg/sub-NN_task-wmgating_ieeg.*: the released data.trial epochs written back-to-back (one New Segment marker

per trial; events.tsv gives each trial’s position, its authors’ time axis start (-1 s) and every trialinfo field as released: trl_num, context, congruency, stimuli, rt, acc, Rel, Irrel, stim1, stim2, stim3, digitStimuli, digit_position, target_position, distractor_position, s2_onset, s3_onset, resp_onset). Unit µV assumed (not stated in the release); values unchanged.

  • Channel names are the bipolar pairs (e.g. LF9-LF1). The channel type is INFERRED from the pair numbering following the article’s montage rule (sEEG: deep-surface neighbours, adjacent numbers; ECoG grids: row-by-row neighbours, non-adjacent numbers); the release itself does not state the electrode type per channel.

  • sub-NN_space-Other_electrodes.tsv: data.elec.chanpos of each bipolar channel as released. The release does not state whether chanpos is in native or MNI space (the article localizes in native space and transforms to MNI for group display), hence space-Other; see coordsystem.json. The per-subject yeo.mat (Yeo-7 network per task-responsive electrode) lists fewer entries than channels and carries no channel names, so it could not be mapped to channels; it is kept in sourcedata/ (counts per subject in code/conversion_report.json, key yeo_unmapped). Where its length matches the channel count the network is written to the yeo7_network column.

  • participants.tsv: no per-subject age or sex in the release (article Table S1).

  • sourcedata/osf-rx2zd/: every release file (data incl. behavior.mat, trialinfo.mat, scripts) unchanged apart from MAT text-header dates reduced to month/year; DEIDENTIFICATION_MANIFEST.tsv lists sha-256 before and after.

## Privacy Subjects are numbered S1-S11; files hold trial parameters, responses and reaction times only.

License: CC-BY-4.0

Authors:

  • Elizabeth L. Johnson

  • Jack J. Lin

  • David King-Stephens

  • Peter B. Weber

  • Kenneth D. Laxer

  • … and 5 more

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000480
§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

52029334243495780

Sampling frequencies (Hz)

5121000

Total recording duration: 1 h 33 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 49 (2), 42 (2), 5, 29, 33, 43, 80, 57, 20 ch · iEEG · 1000 Hz · mixed · 11 subjects, 11 recordings
Live trace viewer — sub-04 · task-wmgating

Showing one representative recording out of 11 subjects and 11 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _ieeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?ieeg=<url>) to inspect it.

Electrode layout — iEEG · 20 sensors — 20 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 — NM000480
HED event descriptors word cloud HED event descriptors word cloud — NM000480
§ 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

NM000480

Title

s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative)

Author (year)

—

Canonical

—

Importable as

NM000480

Year

2023

Authors

Elizabeth L. Johnson, Jack J. Lin, David King-Stephens, Peter B. Weber, Kenneth D. Laxer, Ignacio Saez, Fady Girgis, Mark D’Esposito, Robert T. Knight, David Badre

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000480

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000480,
  title = {s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative)},
  author = {Elizabeth L. Johnson and Jack J. Lin and David King-Stephens and Peter B. Weber and Kenneth D. Laxer and Ignacio Saez and Fady Girgis and Mark D'Esposito and Robert T. Knight and David Badre},
  doi = {10.82901/nemar.nm000480},
  url = {https://doi.org/10.82901/nemar.nm000480},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000480(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asNM000480
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000480(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative)

Study:

nm000480 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: NM000480.

Modality: ieeg; Subject type: Unknown. Subjects: 11; recordings: 11; 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/nm000480 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000480 DOI: https://doi.org/10.82901/nemar.nm000480

Examples

>>> from eegdash.dataset import NM000480
>>> dataset = NM000480(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 — NM000480.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 nm000480 to reproduce the tutorial on this dataset.

Citation

Elizabeth L. Johnson, Jack J. Lin, David King-Stephens, Peter B. Weber, Kenneth D. Laxer, … (2023). s500-literature-171: Working-memory gating task iEEG, epoched bipolar ECoG/sEEG from 11 patients with Yeo-7 network labels (Johnson et al., 2023) (derivative). 10.82901/nemar.nm000480

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000480.

BIDS
BIDS 1.10.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000480
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#