EEGdash›NeMAR›NM000369
Iss. 369 · 3 subjects · 3 recordings · CC-BY-4.0
Dataset Brief · Sensorimotor alpha and beta ECoG during movement imagery (Sto…

NM000369: ieeg dataset, 3 subjects#

Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6)

Access recordings and metadata through EEGDash.

Citation: Arjen Stolk, Loek Brinkman, Mariska J. Vansteensel, Erik Aarnoutse, Frans S.S. Leijten, Chris H. Dijkerman, Robert T. Knight, Floris P. de Lange, Ivan Toni (2019). Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6). 10.82901/nemar.nm000369

Modality: ieeg Subjects: 3 Recordings: 3 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

3-participant iEEG dataset — Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6).

Data & curation Arjen Stolk · Loek Brinkman · Mariska J. Vansteensel · Erik Aarnoutse · Frans S.S. Leijten · Chris H. Dijkerman · …
Senior author Ivan Toni
Year 2019 · Distributed via NeMAR
Funding Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) 446-14-007 (A. Stolk) · European Commission 658868 (A. Stolk) · + 1 more
iEEG · 120, 64, 112 ch512 HzBIDS 1.10.0Task · motorimagery
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000369

dataset = NM000369(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000369(cache_dir="./data", subject="01")

Advanced query

dataset = NM000369(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000369,
  title = {Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6)},
  author = {Arjen Stolk and Loek Brinkman and Mariska J. Vansteensel and Erik Aarnoutse and Frans S.S. Leijten and Chris H. Dijkerman and Robert T. Knight and Floris P. de Lange and Ivan Toni},
  doi = {10.82901/nemar.nm000369},
  url = {https://doi.org/10.82901/nemar.nm000369},
}
§ 02Study · The README

About This Dataset#

Electrocorticography (subdural grids/strips, University Medical Center Utrecht) recorded while epilepsy patients imagined

grasping a cylinder with the left or right hand (movement imagery task, 60 trials per session). The paper analysed 9 of 11 implanted participants; the authors’ OSF record (https://osf.io/z4hfm/) states: “Due to privacy issues, we are not allowed to share the data of all the patients reported in our paper. The data of the patients that have explicitly granted permission to share their data is shared here.” Those are participants S4, S5 and S6, released as trial segments.

Paper: Stolk A, Brinkman L, Vansteensel MJ, Aarnoutse E, Leijten FSS, Dijkerman CH, Knight RT, de Lange FP, Toni I (2019).

Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system. eLife 8:e48065. https://doi.org/10.7554/eLife.48065 . License of the OSF data record: CC-By Attribution 4.0 International.

DOI

Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019) - participants S4, S5, S6

Overview

Participants / cohort (paper, Methods ‘Participants’)

  • Paper cohort: 11 epilepsy patients (7 males, 14-45 years) implanted subdurally with grid and strip arrays at the University

View full README

DOI

Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019) - participants S4, S5, S6

Overview

Participants / cohort (paper, Methods ‘Participants’)

  • Paper cohort: 11 epilepsy patients (7 males, 14-45 years) implanted subdurally with grid and strip arrays at the University Medical Center Utrecht (The Netherlands) to localise the seizure onset zone for surgical resection; arrays on the left hemisphere in 8 cases, on the right in 3; 81.3 +/- 11.2 electrodes (mean +/- SEM). All had normal hearing and vision. Two participants were excluded for not adhering to the task instructions (9 analysed behaviourally); two had no upper-limb sensorimotor coverage (7 analysed neurally). No seizures occurred during task administration.

  • This release: S4 (right hemisphere, 112 electrodes), S5 (left, 120), S6 (left, 64); hemisphere and counts from the released electrode positions/atlas labels. In the paper, S4 and S5 contributed alpha/beta local-maxima electrodes; S6 had limited sensorimotor coverage and only its four stimulation-positive electrodes were used, for temporal dynamics only.

  • Per-participant age, sex and handedness are not published (n/a in participants.tsv); recording dates/years are not stated.

Task (paper, Methods ‘Movement imagery task’)

Participants lay semi-recumbent in their hospital bed and performed up to three sessions (mean 2 +/- 0.2) of 60 trials (10 min). A black-white cylinder (17.5 x 3.5 cm), tilted in 1 of 15 orientations (24 deg apart, pseudo-random), was shown for 2-5 s (adjusted per participant); participants imagined grasping its middle third with the left or right hand (hand alternating every ten trials, visual cue). A response screen (black and white squares, order pseudo-random) followed, and participants reported whether the thumb ended on the black or white part by pressing the left or right button with the left or right thumb on a button box held with both hands; a fixation cross (3-4 s) followed. A control task (same visual input, judge which side of the cylinder is larger) was completed by 8 of 9 participants; it is not identified in this release.

Acquisition (paper, Methods ‘ECoG acquisition and analysis’)

128-channel Micromed system (Treviso, Italy; 22 bits), analog band-pass 0.15-134.4 Hz, sampled at 512 Hz. Ad-Tech subdural grids and strips (Racine, USA), 10 mm spacing, 2.3 mm exposed diameter. Epochs with overt movements or distracting events were excluded by the authors (6 +/- 2 % of trials). Electrode localisation: post-implantation CT fused with the pre-operative T1 MRI (Philips 3T Achieva; CT Philips Tomoscan SR7000), projection to FreeSurfer surfaces (Stolk et al. 2018, https://doi.org/10.1038/s41596-018-0009-6).

Ethics: Medical Ethical Committee of UMC Utrecht, reference 12-075. The paper’s own analysis then filtered (1-200 Hz), removed line noise and re-referenced to the common average; that processing is NOT applied to the shared segments (see below).

Content, preprocessing and conversion (known caveats)

  • sub-S4: 112 ECoG channels, 173 epochs of 2305 samples (-1.5 s to 3.000 s around the trial time zero), 512 Hz

  • sub-S5: 120 ECoG channels, 49 epochs of 2049 samples (-1.5 s to 2.500 s around the trial time zero), 512 Hz

  • sub-S6: 64 ECoG channels, 180 epochs of 2561 samples (-1.5 s to 3.500 s around the trial time zero), 512 Hz

  • The source contains only trial segments (FieldTrip data.trial), not continuous recordings. Each participant’s segments are written back-to-back into one BrainVision file (RecordingType: epoched; events.tsv marks every epoch, its time-zero sample and the 9 undocumented trialinfo columns verbatim). Do not treat the file as continuous: epoch boundaries are discontinuities.

  • No filtering, re-referencing or resampling was applied. Values are stored losslessly (round trip exact, values in microvolts): sub-S4 BrainVision INT_16 x 0.09765625; sub-S5 BrainVision INT_16 x 0.09765625; sub-S6 no exact integer coding exists (off-grid values, likely resampled by the authors), so the float64 samples are stored losslessly in NWB (acquisition ‘ECoG’). Units: the .mat files do not state the signal unit (FieldTrip elec.chanunit ‘V’ is the toolbox default for sensor definitions). The values are labelled microvolts because the recording system is Micromed and the quantum (0.09765625) is the same as in Micromed data with declared microvolt units; this is an inference, not a source statement.

  • Electrodes: positions from the FieldTrip elec structure (mm, coordinate system not named by the source), plus the authors’ electrode table (11 mm atlas look-ups, discard/epileptic/out-of-brain flags) as extra columns. Channels flagged by the authors are status=bad in channels.tsv (no channel removed).

  • Reference: not reported for the shared segments (n/a).

  • sourcedata/osf-z4hfm/: the original OSF files, byte-identical: S*_raw_segmented.mat, S*_electable_11mm.xlsx and FreeSurfer pial surfaces S*_lh.pial/S*_rh.pial (cortical meshes, no facial information).

  • Per-participant age/sex are not released (paper: 11 participants, 7 males, 14-45 years): n/a in participants.tsv.

How to load

import mne_bids
bp = mne_bids.BIDSPath(root=".", subject="S4", task="motorimagery", datatype="ieeg")
raw = mne_bids.read_raw_bids(bp)  # sub-S4/S5 BrainVision; sub-S6 is NWB (read with pynwb)

Remember that the files are epoched (RecordingType = epoched): use events.tsv (sample, time_zero_sample) to cut epochs.

Citation

Stolk A et al. (2019) eLife 8:e48065, https://doi.org/10.7554/eLife.48065 ; data: https://osf.io/z4hfm/ .

Provenance

OSF project z4hfm (“Data accompanying Stolk et al. 2019 …”, created 2020-04-21); paper full text PMC6785220. Metadata enriched 2026-10-07 from the paper (participants, task, acquisition, acknowledgements) and from the released electrode tables.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000369-blue)](https://doi.org/10.82901/nemar.nm000369) # Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019) - participants S4, S5, S6 ## Overview Electrocorticography (subdural grids/strips, University Medical Center Utrecht) recorded while epilepsy patients imagined grasping a cylinder with the left or right hand (movement imagery task, 60 trials per session). The paper analysed 9 of 11 implanted participants; the authors’ OSF record (https://osf.io/z4hfm/) states: “Due to privacy issues, we are not allowed to share the data of all the patients reported in our paper. The data of the patients that have explicitly granted permission to share their data is shared here.” Those are participants S4, S5 and S6, released as trial segments. Paper: Stolk A, Brinkman L, Vansteensel MJ, Aarnoutse E, Leijten FSS, Dijkerman CH, Knight RT, de Lange FP, Toni I (2019). Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system. eLife 8:e48065. https://doi.org/10.7554/eLife.48065 . License of the OSF data record: CC-By Attribution 4.0 International. ## Participants / cohort (paper, Methods ‘Participants’) - Paper cohort: 11 epilepsy patients (7 males, 14-45 years) implanted subdurally with grid and strip arrays at the University

Medical Center Utrecht (The Netherlands) to localise the seizure onset zone for surgical resection; arrays on the left hemisphere in 8 cases, on the right in 3; 81.3 +/- 11.2 electrodes (mean +/- SEM). All had normal hearing and vision. Two participants were excluded for not adhering to the task instructions (9 analysed behaviourally); two had no upper-limb sensorimotor coverage (7 analysed neurally). No seizures occurred during task administration.

  • This release: S4 (right hemisphere, 112 electrodes), S5 (left, 120), S6 (left, 64); hemisphere and counts from the released electrode positions/atlas labels. In the paper, S4 and S5 contributed alpha/beta local-maxima electrodes; S6 had limited sensorimotor coverage and only its four stimulation-positive electrodes were used, for temporal dynamics only.

  • Per-participant age, sex and handedness are not published (n/a in participants.tsv); recording dates/years are not stated.

## Task (paper, Methods ‘Movement imagery task’) Participants lay semi-recumbent in their hospital bed and performed up to three sessions (mean 2 +/- 0.2) of 60 trials (10 min). A black-white cylinder (17.5 x 3.5 cm), tilted in 1 of 15 orientations (24 deg apart, pseudo-random), was shown for 2-5 s (adjusted per participant); participants imagined grasping its middle third with the left or right hand (hand alternating every ten trials, visual cue). A response screen (black and white squares, order pseudo-random) followed, and participants reported whether the thumb ended on the black or white part by pressing the left or right button with the left or right thumb on a button box held with both hands; a fixation cross (3-4 s) followed. A control task (same visual input, judge which side of the cylinder is larger) was completed by 8 of 9 participants; it is not identified in this release. ## Acquisition (paper, Methods ‘ECoG acquisition and analysis’) 128-channel Micromed system (Treviso, Italy; 22 bits), analog band-pass 0.15-134.4 Hz, sampled at 512 Hz. Ad-Tech subdural grids and strips (Racine, USA), 10 mm spacing, 2.3 mm exposed diameter. Epochs with overt movements or distracting events were excluded by the authors (6 +/- 2 % of trials). Electrode localisation: post-implantation CT fused with the pre-operative T1 MRI (Philips 3T Achieva; CT Philips Tomoscan SR7000), projection to FreeSurfer surfaces (Stolk et al. 2018, https://doi.org/10.1038/s41596-018-0009-6). Ethics: Medical Ethical Committee of UMC Utrecht, reference 12-075. The paper’s own analysis then filtered (1-200 Hz), removed line noise and re-referenced to the common average; that processing is NOT applied to the shared segments (see below). ## Content, preprocessing and conversion (known caveats) - sub-S4: 112 ECoG channels, 173 epochs of 2305 samples (-1.5 s to 3.000 s around the trial time zero), 512 Hz - sub-S5: 120 ECoG channels, 49 epochs of 2049 samples (-1.5 s to 2.500 s around the trial time zero), 512 Hz - sub-S6: 64 ECoG channels, 180 epochs of 2561 samples (-1.5 s to 3.500 s around the trial time zero), 512 Hz - The source contains only trial segments (FieldTrip data.trial), not continuous recordings. Each participant’s segments are

written back-to-back into one BrainVision file (RecordingType: epoched; events.tsv marks every epoch, its time-zero sample and the 9 undocumented trialinfo columns verbatim). Do not treat the file as continuous: epoch boundaries are discontinuities.

  • No filtering, re-referencing or resampling was applied. Values are stored losslessly (round trip exact, values in microvolts): sub-S4 BrainVision INT_16 x 0.09765625; sub-S5 BrainVision INT_16 x 0.09765625; sub-S6 no exact integer coding exists (off-grid values, likely resampled by the authors), so the float64 samples are stored losslessly in NWB (acquisition ‘ECoG’). Units: the .mat files do not state the signal unit (FieldTrip elec.chanunit ‘V’ is the toolbox default for sensor definitions). The values are labelled microvolts because the recording system is Micromed and the quantum (0.09765625) is the same as in Micromed data with declared microvolt units; this is an inference, not a source statement.

  • Electrodes: positions from the FieldTrip elec structure (mm, coordinate system not named by the source), plus the authors’ electrode table (11 mm atlas look-ups, discard/epileptic/out-of-brain flags) as extra columns. Channels flagged by the authors are status=bad in channels.tsv (no channel removed).

  • Reference: not reported for the shared segments (n/a).

  • sourcedata/osf-z4hfm/: the original OSF files, byte-identical: S*_raw_segmented.mat, S*_electable_11mm.xlsx and FreeSurfer pial surfaces S*_lh.pial/S*_rh.pial (cortical meshes, no facial information).

  • Per-participant age/sex are not released (paper: 11 participants, 7 males, 14-45 years): n/a in participants.tsv.

## How to load `python import mne_bids bp = mne_bids.BIDSPath(root=".", subject="S4", task="motorimagery", datatype="ieeg") raw = mne_bids.read_raw_bids(bp)  # sub-S4/S5 BrainVision; sub-S6 is NWB (read with pynwb) ` Remember that the files are epoched (RecordingType = epoched): use events.tsv (sample, time_zero_sample) to cut epochs. ## Citation Stolk A et al. (2019) eLife 8:e48065, https://doi.org/10.7554/eLife.48065 ; data: https://osf.io/z4hfm/ . ## Provenance OSF project z4hfm (“Data accompanying Stolk et al. 2019 …”, created 2020-04-21); paper full text PMC6785220. Metadata enriched 2026-10-07 from the paper (participants, task, acquisition, acknowledgements) and from the released electrode tables.

License: CC-BY-4.0

Authors:

  • Arjen Stolk

  • Loek Brinkman

  • Mariska J. Vansteensel

  • Erik Aarnoutse

  • Frans S.S. Leijten

  • … and 4 more

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000369
§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

64112120

Sampling frequencies: 512.0 Hz (n=3 recordings)

Total recording duration: 31 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 120, 64, 112 ch · iEEG · 512 Hz · 3 subjects, 3 recordings
Live trace viewer — sub-S5 · task-motorimagery

Showing one representative recording out of 3 subjects and 3 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 · 112 sensors — 112 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.

NEMAR pipeline statistics — NM000369
HED event descriptors word cloud HED event descriptors word cloud — NM000369
§ 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…
Full dataset metadata table

Dataset ID

NM000369

Title

Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6)

Author (year)

—

Canonical

—

Importable as

NM000369

Year

2019

Authors

Arjen Stolk, Loek Brinkman, Mariska J. Vansteensel, Erik Aarnoutse, Frans S.S. Leijten, Chris H. Dijkerman, Robert T. Knight, Floris P. de Lange, Ivan Toni

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000369

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000369,
  title = {Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6)},
  author = {Arjen Stolk and Loek Brinkman and Mariska J. Vansteensel and Erik Aarnoutse and Frans S.S. Leijten and Chris H. Dijkerman and Robert T. Knight and Floris P. de Lange and Ivan Toni},
  doi = {10.82901/nemar.nm000369},
  url = {https://doi.org/10.82901/nemar.nm000369},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000369(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asNM000369
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000369(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6)

Study:

nm000369 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: NM000369.

Modality: ieeg; Subject type: Unknown. Subjects: 3; recordings: 3; 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/nm000369 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000369 DOI: https://doi.org/10.82901/nemar.nm000369

Examples

>>> from eegdash.dataset import NM000369
>>> dataset = NM000369(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 — NM000369.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 nm000369 to reproduce the tutorial on this dataset.

Citation

Arjen Stolk, Loek Brinkman, Mariska J. Vansteensel, Erik Aarnoutse, Frans S.S. Leijten, … (2019). Sensorimotor alpha and beta ECoG during movement imagery (Stolk et al. 2019, shared participants S4-S6). 10.82901/nemar.nm000369

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000369.

BIDS
BIDS 1.10.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000369
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#