NM000181: eeg dataset, 2417 subjects#

NMT: Neurodiagnostic Montage Template Scalp EEG

Access recordings and metadata through EEGDash.

Citation: Hussain A. Khan (2019). NMT: Neurodiagnostic Montage Template Scalp EEG. 10.5281/zenodo.10909103

Modality: eeg Subjects: 2417 Recordings: 2417 License: CC BY-SA 4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000181

dataset = NM000181(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 = NM000181(cache_dir="./data", subject="01")

Advanced query

dataset = NM000181(
    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{nm000181,
  title = {NMT: Neurodiagnostic Montage Template Scalp EEG},
  author = {Hussain A. Khan},
  doi = {10.5281/zenodo.10909103},
  url = {https://doi.org/10.5281/zenodo.10909103},
}

About This Dataset#

NMT: Neurodiagnostic Montage Template Scalp EEG Dataset

Overview

2,417 clinical EEG recordings (normal and abnormal) in standard 10-20 montage with 19 EEG channels + 2 reference electrodes. EDF format, variable sampling rates and durations. This dataset was collected for EEG-based pathology detection and normal/abnormal classification tasks. Source: Zenodo (doi:10.5281/zenodo.10909103) License: CC BY-SA 4.0

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	`NM000181`
Title	NMT: Neurodiagnostic Montage Template Scalp EEG
Author (year)	`Khan2019`
Canonical	—
Importable as	`NM000181`, `Khan2019`
Year	2019
Authors	Hussain A. Khan
License	CC BY-SA 4.0
Citation / DOI	doi:10.5281/zenodo.10909103
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: 2417
Recordings: 2417
Tasks: 1

Channels & sampling rate

Channels: 21
Sampling rate (Hz): 200.0
Duration (hours): 488.9631958333334

Electrode Layout#

Electrode layout — EEG · 21 sensors — 21 channels

Dataset Statistics#

Channel counts: 21 ch (n=2417 recordings)

Sampling frequencies: 200.0 Hz (n=2417 recordings)

Total recording duration: 488 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000181 class to access this dataset programmatically.

class eegdash.dataset.NM000181(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

NMT: Neurodiagnostic Montage Template Scalp EEG

Study:: nm000181 (NeMAR)
Author (year):: Khan2019
Canonical:: —

Also importable as: NM000181, Khan2019.

Modality: eeg. Subjects: 2417; recordings: 2417; 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/nm000181 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000181 DOI: https://doi.org/10.5281/zenodo.10909103

Examples

>>> from eegdash.dataset import NM000181
>>> dataset = NM000181(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)