EEGdash›NeMAR›NM000484

Iss. 484 · 1 subjects · 1 recordings · CC0-1.0

Dataset Brief · s500-literature-83

NM000484: ieeg dataset, 1 subjects#

s500-literature-83: Example intracranial EEG with qHFO detections from ‘Redaction of false High Frequency Oscillations due to muscle artifact’ (Stacey lab, Mendeley Data)

Access recordings and metadata through EEGDash.

Citation: William Stacey, Stacey lab (University of Michigan) (2019). s500-literature-83: Example intracranial EEG with qHFO detections from ‘Redaction of false High Frequency Oscillations due to muscle artifact’ (Stacey lab, Mendeley Data). 10.82901/nemar.nm000484

Modality: ieeg Subjects: 1 Recordings: 1 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

1-participant iEEG dataset — s500-literature-83: Example intracranial EEG with qHFO detections from 'Redaction of false High Frequency Oscillations due to muscle artifact' (Stacey lab, Mendeley Data).

Data & curation William Stacey · Stacey lab (University of Michigan)
Senior author Stacey lab (University of Michigan)
Year 2019 · Distributed via NeMAR

iEEG · 61 ch5000 HzBIDS 1.10.0Task · rest

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 NM000484

dataset = NM000484(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 = NM000484(cache_dir="./data", subject="01")

Advanced query

dataset = NM000484(
    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{nm000484,
  title = {s500-literature-83: Example intracranial EEG with qHFO detections from 'Redaction of false High Frequency Oscillations due to muscle artifact' (Stacey lab, Mendeley Data)},
  author = {William Stacey and Stacey lab (University of Michigan)},
  doi = {10.82901/nemar.nm000484},
  url = {https://doi.org/10.82901/nemar.nm000484},
}

§ 02Study · The README

About This Dataset#

The intracranial example recording distributed with the code of Ren, Gliske, Brang and Stacey (2019), “Redaction of false

high frequency oscillations due to muscle artifact improves specificity to epileptic tissue” (Clin Neurophysiol): 61 intracranial channels, 3000001 samples at 5000 Hz (10.0 min), with 357 automated qHFO detections (start/stop sample and channel). The release uses it to demonstrate the wavelet-entropy feature that separates EMG-contaminated false HFOs from real HFOs (threshold 8.956 in the release script).

(MATLAB v7.3, data int16 61 x 3000001), qHFOdetection.mat (startIdx, stopIdx, chanIdx), data.mat (19-channel scalp EEG

example for the scalp EMG detector), MATLAB code and Readme.docx. All kept byte-identical in sourcedata/mendeley_bt58z3yhdy/.

DOI

Example intracranial EEG with qHFO detections (Stacey lab, Mendeley Data bt58z3yhdy)

Sampling frequency: fs = 5000 in example_intracranial_feature.m (“sampling frequency of raw iEEG”); the scalp example uses FS = 5000 as well.

Content

sub-01/ieeg/sub-01_task-rest_ieeg.*: the 61 intracranial channels as BrainVision IEEE float32, integer values unchanged. The release gives no channel names (channels are ch01..``ch61`` = matrix rows), no electrode types or positions, no reference and no unit (units = n/a; the amplitudes, std ~250, are consistent with microvolts but this is not stated). Channel type OTHER.
events.tsv: one row per qHFO detection (onset/duration from the release sample indices, channel). Whether a detection is an EMG-false HFO is computed by the release code (not released as a table).
The 19-channel scalp EEG example (data.mat) is in sourcedata only (no channel names or montage released).

Participants

One patient under intracranial monitoring; no demographic or clinical information in the release.

Privacy

The release holds anonymous matrices and sample indices only; no dates (acq_time n/a), names or identifiers.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000484-blue)](https://doi.org/10.82901/nemar.nm000484) # Example intracranial EEG with qHFO detections (Stacey lab, Mendeley Data bt58z3yhdy) The intracranial example recording distributed with the code of Ren, Gliske, Brang and Stacey (2019), “Redaction of false high frequency oscillations due to muscle artifact improves specificity to epileptic tissue” (Clin Neurophysiol): 61 intracranial channels, 3000001 samples at 5000 Hz (10.0 min), with 357 automated qHFO detections (start/stop sample and channel). The release uses it to demonstrate the wavelet-entropy feature that separates EMG-contaminated false HFOs from real HFOs (threshold 8.956 in the release script). ## Source - Mendeley Data doi:10.17632/bt58z3yhdy.1 (contributor William Stacey), licence CC0 1.0. examples.zip = intracranialEEG.mat

(MATLAB v7.3, data int16 61 x 3000001), qHFOdetection.mat (startIdx, stopIdx, chanIdx), data.mat (19-channel scalp EEG example for the scalp EMG detector), MATLAB code and Readme.docx. All kept byte-identical in sourcedata/mendeley_bt58z3yhdy/.

Sampling frequency: fs = 5000 in example_intracranial_feature.m (“sampling frequency of raw iEEG”); the scalp example uses FS = 5000 as well.

## Content - sub-01/ieeg/sub-01_task-rest_ieeg.*: the 61 intracranial channels as BrainVision IEEE float32, integer values unchanged.

The release gives no channel names (channels are ch01..`ch61` = matrix rows), no electrode types or positions, no reference and no unit (units = n/a; the amplitudes, std ~250, are consistent with microvolts but this is not stated). Channel type OTHER.

events.tsv: one row per qHFO detection (onset/duration from the release sample indices, channel). Whether a detection is an EMG-false HFO is computed by the release code (not released as a table).
The 19-channel scalp EEG example (data.mat) is in sourcedata only (no channel names or montage released).

## Participants One patient under intracranial monitoring; no demographic or clinical information in the release. ## Privacy The release holds anonymous matrices and sample indices only; no dates (acq_time n/a), names or identifiers.

License: CC0-1.0

Authors:

William Stacey
Stacey lab (University of Michigan)

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000484

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 61 ch (n=1 recordings)

Sampling frequencies: 5000.0 Hz (n=1 recordings)

Total recording duration: 10 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 61 ch · iEEG · 5000 Hz · 1 subjects, 1 recordings

Live trace viewer — sub-01 · task-rest

Showing one representative recording out of 1 subjects and 1 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.

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.NM000484(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000484

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

class eegdash.dataset.NM000484(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

s500-literature-83: Example intracranial EEG with qHFO detections from ‘Redaction of false High Frequency Oscillations due to muscle artifact’ (Stacey lab, Mendeley Data)

Study:: nm000484 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000484.

Modality: ieeg; Subject type: Unknown. Subjects: 1; recordings: 1; 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/nm000484 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000484 DOI: https://doi.org/10.82901/nemar.nm000484

Examples

>>> from eegdash.dataset import NM000484
>>> dataset = NM000484(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 — NM000484.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 nm000484 to reproduce the tutorial on this dataset.

Citation

William Stacey, Stacey lab (University of Michigan) (2019). s500-literature-83: Example intracranial EEG with qHFO detections from 'Redaction of false High Frequency Oscillations due to muscle artifact' (Stacey lab, Mendeley Data). 10.82901/nemar.nm000484

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000484.

Related & sibling datasets

NM000443IEEG · 7 subj NM000435IEEG · 11 subj NM000455IEEG · 30 subj NM000463IEEG · 2 subj NM000446IEEG · 16 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000484

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000484`
Title	s500-literature-83: Example intracranial EEG with qHFO detections from ‘Redaction of false High Frequency Oscillations due to muscle artifact’ (Stacey lab, Mendeley Data)
Author (year)	—
Canonical	—
Importable as	`NM000484`
Year	2019
Authors	William Stacey, Stacey lab (University of Michigan)
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000484
Source links	OpenNeuro \| NeMAR \| Source URL

NM000484: ieeg dataset, 1 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#