EEGdash›NeMAR›NM000461

Iss. 461 · 150 subjects · 1105 recordings · CDLA-Sharing-1.0

Dataset Brief · iEEG networks preprocessing study (Shi, Pattnaik et al. 2024…

NM000461: ieeg dataset, 150 subjects#

Name: iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative)
License: CDLA-Sharing-1.0

iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative)

Access recordings and metadata through EEGDash.

Citation: Haoer Shi, Akash Pattnaik, Erin Conrad (—). iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative). 10.82901/nemar.nm000461

Modality: ieeg Subjects: 150 Recordings: 1105 License: CDLA-Sharing-1.0 Source: nemar

Metadata: Good (70%)

150-participant iEEG dataset — iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative).

Data & curation Haoer Shi · Akash Pattnaik · Erin Conrad

Task · interictal

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 NM000461

dataset = NM000461(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 = NM000461(cache_dir="./data", subject="01")

Advanced query

dataset = NM000461(
    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{nm000461,
  title = {iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative)},
  author = {Haoer Shi and Akash Pattnaik and Erin Conrad},
  doi = {10.82901/nemar.nm000461},
  url = {https://doi.org/10.82901/nemar.nm000461},
}

§ 02Study · The README

About This Dataset#

No README content is available for this dataset.

NEMAR Metadata#

License: CDLA-Sharing-1.0

Authors:

Haoer Shi
Akash Pattnaik
Erin Conrad

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000461

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=2, range 16–59 yr, mean 37.5 yr · sex per subject not reported)

1555

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · iEEG · Varies · 150 subjects, 1105 recordings

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.NM000461(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000461

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

class eegdash.dataset.NM000461(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative)

Study:: nm000461 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000461.

Modality: ieeg; Subject type: Unknown. Subjects: 150; recordings: 1105; 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/nm000461 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000461 DOI: https://doi.org/10.82901/nemar.nm000461

Examples

>>> from eegdash.dataset import NM000461
>>> dataset = NM000461(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 — NM000461.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 nm000461 to reproduce the tutorial on this dataset.

Citation

Haoer Shi, Akash Pattnaik, Erin Conrad (n.d.). iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative). 10.82901/nemar.nm000461

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000461.

Related & sibling datasets

NM000487IEEG · 140 subj NM000464IEEG · 80 subj NM000454IEEG · 15 subj NM000406IEEG · 14 subj NM000485IEEG · 37 subj

+ 1 more — see See Also below →

BIDS

version not on file

Sidecars

not yet probed

Provenance

CDLA-Sharing-1.0 · 10.82901/nemar.nm000461

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000461`
Title	iEEG networks preprocessing study (Shi, Pattnaik et al. 2024; HUP + MUSC): functional-connectivity networks for 2 re-referencing x 24 connectivity pipelines (derivative)
Author (year)	—
Canonical	—
Importable as	`NM000461`
Year	—
Authors	Haoer Shi, Akash Pattnaik, Erin Conrad
License	CDLA-Sharing-1.0
Citation / DOI	10.82901/nemar.nm000461
Source links	OpenNeuro \| NeMAR \| Source URL

NM000461: ieeg dataset, 150 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#