EEGdash›NeMAR›NM000432

Iss. 432 · 18 subjects · 32 recordings · CC0-1.0

Dataset Brief · Human subthalamic nucleus LFP and prefrontal subdural recordi…

NM000432: ieeg dataset, 18 subjects#

Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative

Access recordings and metadata through EEGDash.

Citation: Baltazar A. Zavala, Anthony I. Jang, Kareem A. Zaghloul (2017). Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative. 10.82901/nemar.nm000432

Modality: ieeg Subjects: 18 Recordings: 32 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

18-participant iEEG dataset — Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative.

Data & curation Baltazar A. Zavala · Anthony I. Jang · Kareem A. Zaghloul
Senior author Kareem A. Zaghloul
Year 2017 · Distributed via NeMAR
Funding NIGMS NIH HHS T32 GM007309 · NINDS Intramural Research Program

iEEG · 38 (22), 23 (5), 32 (3), 12, 25 ch1000 HzBIDS 1.10.0Task · seqmem3 sessions

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 NM000432

dataset = NM000432(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 = NM000432(cache_dir="./data", subject="01")

Advanced query

dataset = NM000432(
    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{nm000432,
  title = {Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative},
  author = {Baltazar A. Zavala and Anthony I. Jang and Kareem A. Zaghloul},
  doi = {10.82901/nemar.nm000432},
  url = {https://doi.org/10.82901/nemar.nm000432},
}

§ 02Study · The README

About This Dataset#

Intraoperative recordings from patients with Parkinson’s disease undergoing STN DBS surgery at the NIH: macroelectrode LFP

from three STN trajectories and subdural strips (6 and 8 contacts) over the anterior and lateral prefrontal cortex, while participants saw eight digits in sequence and remembered only those inside a target shape (article). THIS IS A DERIVATIVE DATASET: the files of the release folder ecogLFPdata_rereferenced are, per the article, band-passed 1-500 Hz, notch-filtered at 60 Hz, downsampled to 1 kHz (raw macro rate 1.5024 kHz) and re-referenced (macro: common average of the simultaneously recorded macro contacts; subdural: bipolar). Single-number channels (e.g. LFP001) and pairs (e.g. LFP001-002) are both given as in the release.

Zenodo doi:10.5281/zenodo.4960485 (mirror of the Dryad deposit), License CC0 1.0. Article: eLife 6:e31007 (2017),

doi:10.7554/eLife.31007. All files downloaded on Voyager with md5 verification.

DOI

Human STN and prefrontal recordings during non-motor (working-memory encoding) decisions (Zavala, Jang & Zaghloul, 2018) - DERIVATIVE

Contents

sub-DBSxxx/ses-<k>/ieeg/*_task-seqmem_run-<n>: 32 recordings, 18 subjects, 11.88 h at 1 kHz. ses-<k> = behavioural session_k whose events point to the recording; run = the recording number in the source file name. Values = the release int16 numbers (scaling not stated; units n/a). LFPnnn = channels numbered 0NN (macro), ECOGnnn = channels numbered 5NN (subdural); the release does not map numbers to individual contacts or strips. ‘bad’ in a release file name -> status bad.
events.tsv: one row per digit from behavioral/session_k/events.mat (onset = eegoffset / 1000 s).
sourcedata/zenodo-4960485/SeqMemRawdata/: all channel files, behaviour, docs and Plexon spike times (sorted units from the microelectrodes; the unsorted wideband spiking data are not in the release), with these changes: recording dates removed from file names (day set to 01), dates in recordingdetails.txt set to day 01, events.mat mstime made relative to the first event and eegfile reduced to the renamed base name. README_for_SeqMemRawdata.rtf unchanged.

Localisation

No coordinates. STN trajectories (centre/lateral/anterior per side), target frame coordinates and recording depths are given per subject in docs/recordingdetails.txt (sourcedata). Article Figure 1C shows projected strip locations for 14 of 18 participants.

Participants

Article: 18 participants (16 male), 58.4 +/- 1.64 years (mean +/- SEM); not given per subject. Medication stopped 12 h before surgery.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000432-blue)](https://doi.org/10.82901/nemar.nm000432) # Human STN and prefrontal recordings during non-motor (working-memory encoding) decisions (Zavala, Jang & Zaghloul, 2018) - DERIVATIVE Intraoperative recordings from patients with Parkinson’s disease undergoing STN DBS surgery at the NIH: macroelectrode LFP from three STN trajectories and subdural strips (6 and 8 contacts) over the anterior and lateral prefrontal cortex, while participants saw eight digits in sequence and remembered only those inside a target shape (article). THIS IS A DERIVATIVE DATASET: the files of the release folder ecogLFPdata_rereferenced are, per the article, band-passed 1-500 Hz, notch-filtered at 60 Hz, downsampled to 1 kHz (raw macro rate 1.5024 kHz) and re-referenced (macro: common average of the simultaneously recorded macro contacts; subdural: bipolar). Single-number channels (e.g. LFP001) and pairs (e.g. LFP001-002) are both given as in the release. ## Source - Zavala BA, Jang AI, Zaghloul KA. Data from: Human subthalamic nucleus activity during non-motor decision making.

Zenodo doi:10.5281/zenodo.4960485 (mirror of the Dryad deposit), License CC0 1.0. Article: eLife 6:e31007 (2017), doi:10.7554/eLife.31007. All files downloaded on Voyager with md5 verification.

## Contents - sub-DBSxxx/ses-<k>/ieeg/*_task-seqmem_run-<n>: 32 recordings, 18 subjects, 11.88 h at 1 kHz.

ses-<k> = behavioural session_k whose events point to the recording; run = the recording number in the source file name. Values = the release int16 numbers (scaling not stated; units n/a). LFPnnn = channels numbered 0NN (macro), ECOGnnn = channels numbered 5NN (subdural); the release does not map numbers to individual contacts or strips. ‘bad’ in a release file name -> status bad.

events.tsv: one row per digit from behavioral/session_k/events.mat (onset = eegoffset / 1000 s).
sourcedata/zenodo-4960485/SeqMemRawdata/: all channel files, behaviour, docs and Plexon spike times (sorted units from the microelectrodes; the unsorted wideband spiking data are not in the release), with these changes: recording dates removed from file names (day set to 01), dates in recordingdetails.txt set to day 01, events.mat mstime made relative to the first event and eegfile reduced to the renamed base name. README_for_SeqMemRawdata.rtf unchanged.

## Localisation No coordinates. STN trajectories (centre/lateral/anterior per side), target frame coordinates and recording depths are given per subject in docs/recordingdetails.txt (sourcedata). Article Figure 1C shows projected strip locations for 14 of 18 participants. ## Participants Article: 18 participants (16 male), 58.4 +/- 1.64 years (mean +/- SEM); not given per subject. Medication stopped 12 h before surgery.

License: CC0-1.0

Authors:

Baltazar A. Zavala
Anthony I. Jang
Kareem A. Zaghloul

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000432

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

1223253238

Sampling frequencies: 1000.0 Hz (n=32 recordings)

Total recording duration: 11 h 53 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 38 (22), 23 (5), 32 (3), 12, 25 ch · iEEG · 1000 Hz · 18 subjects, 32 recordings

Live trace viewer — sub-DBS029 · ses-0 · task-seqmem · run-1

Showing one representative recording out of 18 subjects and 32 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.NM000432(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000432

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

class eegdash.dataset.NM000432(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative

Study:: nm000432 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000432.

Modality: ieeg; Subject type: Unknown. Subjects: 18; recordings: 32; 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/nm000432 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000432 DOI: https://doi.org/10.82901/nemar.nm000432

Examples

>>> from eegdash.dataset import NM000432
>>> dataset = NM000432(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 — NM000432.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 nm000432 to reproduce the tutorial on this dataset.

Citation

Baltazar A. Zavala, Anthony I. Jang, Kareem A. Zaghloul (2017). Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative. 10.82901/nemar.nm000432

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000432.

Related & sibling datasets

NM000405IEEG · 16 subj NM000480IEEG · 11 subj NM000473IEEG · 10 subj NM000482IEEG · 7 subj NM000481IEEG · 14 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000432

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000432`
Title	Human subthalamic nucleus LFP and prefrontal subdural recordings during a working-memory encoding decision task (Zavala, Jang & Zaghloul, 2018) - derivative
Author (year)	—
Canonical	—
Importable as	`NM000432`
Year	2017
Authors	Baltazar A. Zavala, Anthony I. Jang, Kareem A. Zaghloul
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000432
Source links	OpenNeuro \| NeMAR \| Source URL

NM000432: ieeg dataset, 18 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#