EEGdash›NeMAR›NM000463

Iss. 463 · 2 subjects · 155 recordings · CC-BY-SA-4.0

Dataset Brief · Network dynamics of HFOs (Lin & Stacey; Michigan)

NM000463: ieeg dataset, 2 subjects#

Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative)

Access recordings and metadata through EEGDash.

Citation: Jack Lin, William C. Stacey (20). Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative). 10.82901/nemar.nm000463

Modality: ieeg Subjects: 2 Recordings: 155 License: CC-BY-SA-4.0 Source: nemar

Metadata: Complete (100%)

2-participant iEEG dataset — Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative).

Data & curation Jack Lin · William C. Stacey
Senior author William C. Stacey
Year 20 · Distributed via NeMAR

iEEG · 200 (101), 106 (54) ch3600 HzBIDS 1.10.02 tasks

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 NM000463

dataset = NM000463(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 = NM000463(cache_dir="./data", subject="01")

Advanced query

dataset = NM000463(
    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{nm000463,
  title = {Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative)},
  author = {Jack Lin and William C. Stacey},
  doi = {10.82901/nemar.nm000463},
  url = {https://doi.org/10.82901/nemar.nm000463},
}

§ 02Study · The README

About This Dataset#

This is a derivative dataset. It repackages “Network dynamics of HFOs” by Jack Lin and William C. Stacey on Pennsieve Discover

(dataset 280 version 1, doi:10.26275/2yv6-g6vh, Stacey Lab, University of Michigan; README title “Epileptic High Frequency Oscillation EEG with Network Analysis”). The release README cites the manuscript “Network dynamics of interictal High Frequency Oscillations predict surgical outcome within the clinical workflow in refractory epilepsy” (then under submission); it was published as Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. “High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery”, Brain Communications 2024;6(1):fcae032, doi:10.1093/braincomms/fcae032 (same code repository https://github.com/J4KLin/HFO-Network; its Table 1 HFO counts, 216,041 and 441,168, equal the HFO tables of this release). The article’s data statement names a copy of the two patients’ data on University of Michigan Deep Blue Data (doi:10.7302/n9rt-sc45).

Two patients who had resective surgery: UMHS-0028 (good outcome) and UMHS-0030 (poor outcome). The raw long-term recordings of

UMHS-0028 are in NEMAR nm000444 (Michigan high-resolution iEEG, same subject label). No continuous recordings are included here.

DOI

Network dynamics of HFOs (Pennsieve 280): HFO and background iEEG snippets, derivative BIDS-iEEG

Contents

sub-<label>/ieeg/sub-<label>_task-hfo_acq-elecNN_ieeg.vhdr (153 runs: 53 UMHS0028, 100 UMHS0030): one run per released file UMHS-xxxx-elecNN.mat, i.e. up to 500 snippets around HFOs detected on electrode NN, showing all channels. Each snippet is 1441 samples at 3600 Hz (the file’s FS variable; PatientInfo.fs gives the recording rate, 4096 Hz). The snippets are concatenated in release order; events.tsv has one row per snippet (onset, snippet, hfo_index into the released HFO table

View full README

DOI

Network dynamics of HFOs (Pennsieve 280): HFO and background iEEG snippets, derivative BIDS-iEEG

Contents

sub-<label>/ieeg/sub-<label>_task-hfo_acq-elecNN_ieeg.vhdr (153 runs: 53 UMHS0028, 100 UMHS0030): one run per released file UMHS-xxxx-elecNN.mat, i.e. up to 500 snippets around HFOs detected on electrode NN, showing all channels. Each snippet is 1441 samples at 3600 Hz (the file’s FS variable; PatientInfo.fs gives the recording rate, 4096 Hz). The snippets are concatenated in release order; events.tsv has one row per snippet (onset, snippet, hfo_index into the released HFO table and the released per-HFO metadata: start/stop time, detection channel, day, session, sleep stage, ictal state, quality flags). Each run has the 80-500 Hz filtered signal of every channel and, as channels <name>_rms (type MISC), the RMS envelope. For 74549/74549 HFO snippets the detection channel (hfo_stdChanI) equals the file’s electrode of interest (elecROI).
sub-<label>/ieeg/sub-<label>_task-background_ieeg.vhdr: the 500 background (non-HFO) snippets per patient from Data/BKGEEG/UMHS-xxxx.mat, same layout; events give the released day number and sample interval of each snippet.
Channel names: UMHS0028: labels/types from NEMAR nm000444 (sub-UMHS0028/ses-0002/ieeg/sub-UMHS0028_ses-0002_task-ltm_acq-day05_channels.tsv) indexed by Electrodes.stdEEGChanIdx; verified: the released SOZ mask maps exactly onto nm000444’s soz channels [‘AD3’, ‘AD4’, ‘AD5’, ‘AD6’, ‘AP1’]. UMHS0030: E### = Electrodes.stdEEGChanIdx (channel labels not in the release); type MISC (grid/strip/depth not stated per channel).
sub-<label>/ieeg/sub-<label>_electrodes.tsv: PatientInfo.Electrodes.sejin3DCoords (origCoords as x/y/z, avgCoords as x_avg/y_avg/z_avg; space and units not stated, coordinate system Other), SOZ, resected-volume and remaining-contact masks.
participants.tsv: sex, age, ILAE class, seizure focus, pathology and SOZ resection from Lin et al. 2024 Table 1, and the released PatientInfo fields (ILAE/outcome as released: 1 and 3; Table 1 gives ILAE 1 and 5).
sourcedata/pennsieve-280-v1/: every released file: the 153 HFO .mat files, data.zip stored expanded under files/data_zip_expanded/Data/ (BKGEEG, CombinedData/combinedPatientData.mat with the centrality and HFO-rate tables, PatientData/UMHS-xxxx.mat, README.txt; CRC-32 of every member verified, ZIP_MANIFEST.json), Pennsieve manifest/readme. SOURCE_MANIFEST.json lists the original SHA-256 of every file. Files are byte-identical to the release except MAT-file header creation dates, which were set to day 01 (weekday/time removed, same length).

Signal values are stored as IEEE float32 (source float64; max relative difference 5.95e-08); round trip checked (the written float32 values are identical to the source values cast to float32, for every run). Amplitude units are not stated in the release (units n/a).

Total snippet time: 8.4 h.

Privacy

All files were scanned (text, MAT strings and headers, HDF5 strings) for names, initials, record numbers, full dates and ages above 89. Patient codes (UMHS-0028, UMHS-0030) are pseudonyms of the public Michigan database. Times are relative (day/session numbers, no

calendar dates). The release README’s dataset date and contact e-mail are record metadata and were kept. Key status: destroyed.

Licence

CC-BY-SA-4.0, as on the source record (Pennsieve: “Creative Commons Attribution - ShareAlike”; DataCite rightsList: Creative Commons Attribution Share Alike 4.0 International, cc-by-sa-4.0).

Ethics

Verbatim from the article (Lin et al. 2024, Materials and methods): “All patient data used in this study are approved by the local Institutional Review Board with written patient consent to share and use their de-identified information.”

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000463-blue)](https://doi.org/10.82901/nemar.nm000463) # Network dynamics of HFOs (Pennsieve 280): HFO and background iEEG snippets, derivative BIDS-iEEG This is a derivative dataset. It repackages “Network dynamics of HFOs” by Jack Lin and William C. Stacey on Pennsieve Discover (dataset 280 version 1, doi:10.26275/2yv6-g6vh, Stacey Lab, University of Michigan; README title “Epileptic High Frequency Oscillation EEG with Network Analysis”). The release README cites the manuscript “Network dynamics of interictal High Frequency Oscillations predict surgical outcome within the clinical workflow in refractory epilepsy” (then under submission); it was published as Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. “High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery”, Brain Communications 2024;6(1):fcae032, doi:10.1093/braincomms/fcae032 (same code repository https://github.com/J4KLin/HFO-Network; its Table 1 HFO counts, 216,041 and 441,168, equal the HFO tables of this release). The article’s data statement names a copy of the two patients’ data on University of Michigan Deep Blue Data (doi:10.7302/n9rt-sc45). Two patients who had resective surgery: UMHS-0028 (good outcome) and UMHS-0030 (poor outcome). The raw long-term recordings of UMHS-0028 are in NEMAR nm000444 (Michigan high-resolution iEEG, same subject label). No continuous recordings are included here. ## Contents - sub-<label>/ieeg/sub-<label>_task-hfo_acq-elecNN_ieeg.vhdr (153 runs: 53 UMHS0028, 100 UMHS0030): one run

per released file UMHS-xxxx-elecNN.mat, i.e. up to 500 snippets around HFOs detected on electrode NN, showing all channels. Each snippet is 1441 samples at 3600 Hz (the file’s FS variable; PatientInfo.fs gives the recording rate, 4096 Hz). The snippets are concatenated in release order; events.tsv has one row per snippet (onset, snippet, hfo_index into the released HFO table and the released per-HFO metadata: start/stop time, detection channel, day, session, sleep stage, ictal state, quality flags). Each run has the 80-500 Hz filtered signal of every channel and, as channels <name>_rms (type MISC), the RMS envelope. For 74549/74549 HFO snippets the detection channel (hfo_stdChanI) equals the file’s electrode of interest (elecROI).

sub-<label>/ieeg/sub-<label>_task-background_ieeg.vhdr: the 500 background (non-HFO) snippets per patient from Data/BKGEEG/UMHS-xxxx.mat, same layout; events give the released day number and sample interval of each snippet.
Channel names: UMHS0028: labels/types from NEMAR nm000444 (sub-UMHS0028/ses-0002/ieeg/sub-UMHS0028_ses-0002_task-ltm_acq-day05_channels.tsv) indexed by Electrodes.stdEEGChanIdx; verified: the released SOZ mask maps exactly onto nm000444’s soz channels [‘AD3’, ‘AD4’, ‘AD5’, ‘AD6’, ‘AP1’]. UMHS0030: E### = Electrodes.stdEEGChanIdx (channel labels not in the release); type MISC (grid/strip/depth not stated per channel).
sub-<label>/ieeg/sub-<label>_electrodes.tsv: PatientInfo.Electrodes.sejin3DCoords (origCoords as x/y/z, avgCoords as x_avg/y_avg/z_avg; space and units not stated, coordinate system Other), SOZ, resected-volume and remaining-contact masks.
participants.tsv: sex, age, ILAE class, seizure focus, pathology and SOZ resection from Lin et al. 2024 Table 1, and the released PatientInfo fields (ILAE/outcome as released: 1 and 3; Table 1 gives ILAE 1 and 5).
sourcedata/pennsieve-280-v1/: every released file: the 153 HFO .mat files, data.zip stored expanded under files/data_zip_expanded/Data/ (BKGEEG, CombinedData/combinedPatientData.mat with the centrality and HFO-rate tables, PatientData/UMHS-xxxx.mat, README.txt; CRC-32 of every member verified, ZIP_MANIFEST.json), Pennsieve manifest/readme. SOURCE_MANIFEST.json lists the original SHA-256 of every file. Files are byte-identical to the release except MAT-file header creation dates, which were set to day 01 (weekday/time removed, same length).

Signal values are stored as IEEE float32 (source float64; max relative difference 5.95e-08); round trip checked (the written float32 values are identical to the source values cast to float32, for every run). Amplitude units are not stated in the release (units n/a). Total snippet time: 8.4 h. ## Privacy All files were scanned (text, MAT strings and headers, HDF5 strings) for names, initials, record numbers, full dates and ages above 89. Patient codes (UMHS-0028, UMHS-0030) are pseudonyms of the public Michigan database. Times are relative (day/session numbers, no calendar dates). The release README’s dataset date and contact e-mail are record metadata and were kept. Key status: destroyed. ## Licence CC-BY-SA-4.0, as on the source record (Pennsieve: “Creative Commons Attribution - ShareAlike”; DataCite rightsList: Creative Commons Attribution Share Alike 4.0 International, cc-by-sa-4.0). ## Ethics Verbatim from the article (Lin et al. 2024, Materials and methods): “All patient data used in this study are approved by the local Institutional Review Board with written patient consent to share and use their de-identified information.”

License: CC-BY-SA-4.0

Authors:

Jack Lin
William C. Stacey

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000463

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=2, range 5–14 yr, mean 9.5 yr)

510

Female · 1Male · 1

Sex composition

Female
1
Male
1
F : M ratio
1.00 : 1

50% female · n = 2 subjects with reported sex.

Channel counts (ch)

106200

Sampling frequencies: 3600.0 Hz (n=155 recordings)

Total recording duration: 8 h 24 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 200 (101), 106 (54) ch · iEEG · 3600 Hz · 2 subjects, 155 recordings

Live trace viewer — sub-UMHS0030 · task-hfo

Showing one representative recording out of 2 subjects and 155 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 · 100 sensors — 100 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.

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.NM000463(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000463

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

class eegdash.dataset.NM000463(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative)

Study:: nm000463 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000463.

Modality: ieeg; Subject type: Unknown. Subjects: 2; recordings: 155; 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/nm000463 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000463 DOI: https://doi.org/10.82901/nemar.nm000463

Examples

>>> from eegdash.dataset import NM000463
>>> dataset = NM000463(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 — NM000463.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 nm000463 to reproduce the tutorial on this dataset.

Citation

Jack Lin, William C. Stacey (20). Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative). 10.82901/nemar.nm000463

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000463.

Related & sibling datasets

NM000435IEEG · 11 subj NM000443IEEG · 7 subj NM000455IEEG · 30 subj NM000484IEEG · 1 subj NM000413IEEG · 29 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · electrodes · coordsystem · eeg.json

Provenance

CC-BY-SA-4.0 · 10.82901/nemar.nm000463

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000463`
Title	Network dynamics of HFOs (Lin & Stacey; Michigan): filtered and RMS iEEG snippets around interictal HFOs, 2 patients (derivative)
Author (year)	—
Canonical	—
Importable as	`NM000463`
Year	20
Authors	Jack Lin, William C. Stacey
License	CC-BY-SA-4.0
Citation / DOI	10.82901/nemar.nm000463
Source links	OpenNeuro \| NeMAR \| Source URL

NM000463: ieeg dataset, 2 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#