EEGdash›NeMAR›NM000404

Iss. 404 · 25 subjects · 25 recordings · CC-BY-4.0

Dataset Brief · Internal-external attention switching SEEG epochs, DMN and DA…

NM000404: ieeg dataset, 25 subjects#

Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024)

Access recordings and metadata through EEGDash.

Citation: Jiri Hammer (2024). Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024). 10.82901/nemar.nm000404

Modality: ieeg Subjects: 25 Recordings: 25 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

25-participant iEEG dataset — Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024).

Data & curation Jiri Hammer
Senior author Jiri Hammer
Year 2024 · Distributed via NeMAR
Funding Project LX22NPO5107 (MEYS), financed by the European Union - Next Generation EU · Czech Science Foundation, Grant No. 20-21339S · + 2 more

iEEG · 17 (2), 48 (2), 64 (2), 49, 45, 28, 60, 47, 30, 21, 61, 11, 62, 38, 36, 44, 26, 51, 46, 39, 63, 23 ch512 HzBIDS 1.10.0Task · attentionswitch

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 NM000404

dataset = NM000404(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 = NM000404(cache_dir="./data", subject="01")

Advanced query

dataset = NM000404(
    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{nm000404,
  title = {Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024)},
  author = {Jiri Hammer},
  doi = {10.82901/nemar.nm000404},
  url = {https://doi.org/10.82901/nemar.nm000404},
}

§ 02Study · The README

About This Dataset#

Stereo-EEG epochs released with:

Hammer J, Kajsova M, Kalina A, Krysl D, Fabera P, Kudr M, Jezdik P, Janca R, Krsek P, Marusic P (2024). Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal-external attention switching. Communications Biology 7:1105. https://doi.org/10.1038/s42003-024-06732-2

DOI

Internal-external attention switching: SEEG epochs from 25 epilepsy patients (derivative)

Source record: Zenodo https://doi.org/10.5281/zenodo.12796062 (v3, CC-BY-4.0): msSEI_exportTrials.zip. This is a derivative dataset. The release contains the authors’ preprocessed epochs, not the continuous recordings.

Participants and task (from the paper)

25 patients with drug-resistant epilepsy (15 female; age 34 +/- 12 years) in presurgical SEEG monitoring at Motol

View full README

DOI

Internal-external attention switching: SEEG epochs from 25 epilepsy patients (derivative)

Source record: Zenodo https://doi.org/10.5281/zenodo.12796062 (v3, CC-BY-4.0): msSEI_exportTrials.zip. This is a derivative dataset. The release contains the authors’ preprocessed epochs, not the continuous recordings.

Participants and task (from the paper)

25 patients with drug-resistant epilepsy (15 female; age 34 +/- 12 years) in presurgical SEEG monitoring at Motol University Hospital, Prague; implantation by clinical need only; approved by the hospital’s ethics committee; written informed consent. Per-subject age and sex are not in the release; since 2026-10-08 participants.tsv gives them from the article’s Supplementary Table 1.

Subjects alternated between an external-attention task (visual search: find the T among 35 Ls on a 6x6 grid and report whether it is in the upper or lower half) and an internal-attention task (yes/no answer to a statement about their own past experiences), answering on a gamepad within 5 s, with no pause between trials and not switching on every trial.

Four sessions of several minutes (about 30 min). Each epoch is centred on a task switch: E-I (external to internal) or I-E (internal to external).

Recording and preprocessing (by the authors)

DIXI Medical depth electrodes, Quantum amplifiers / NeuroWorks, 2048 Hz (0.01-682 Hz), reference and ground in white matter. The authors downsampled to 512 Hz, removed broken channels and channels in the seizure-onset or irritative zone or in heterotopic cortex, built bipolar derivations along each shank, high-pass filtered at 0.1 Hz and notch filtered at 50 Hz and harmonics (Butterworth, 6th order, zero phase), cut epochs from -4 to +4 s around each switch, and kept only channels assigned to the default mode network (DMN) or dorsal attention network (DAN) by the Yeo-7 atlas.

Files

sub-P<k>/ieeg/*_ieeg.vhdr|.eeg|.vmrk: the epochs written back to back (4097 samples = 8.002 s each), BrainVision IEEE float32. Values are the release’s float64 values rounded to float32; no other change. The release does not state the physical unit; the channels are labelled µV because amplitudes of a few to tens of units match µV-scaled iEEG, but this is our assumption, not a statement of the authors.
*_events.tsv: one row per epoch at the switch time, with trial_type (E-I / I-E), epoch number, epoch start and the fraction of samples marked rejected in the release.
*_channels.tsv: bipolar channel names as released, with the network label (DMN / DAN).
*_space-Other_electrodes.tsv: the release’s MNI coordinates per bipolar channel.
sourcedata/zenodo-12796062/: the original trials_P<k>.mat files (including the per-sample rejection masks) and the release read-me, unchanged.

The figure source data (code_figures.zip) and analysis code (code_pipeline.zip) of the record are not copied; they are available from the source record and GitHub.

Licence

CC-BY-4.0, as the source record. Please cite the paper and the Zenodo record.

Additional metadata and localisation (added 2026-10-08)

Compiled after the upload from the article, its supplement and the source deposit (each statement names its source). Text and sidecar metadata only; no data file was changed. Recording system. Medical amplifiers (Quantum, NeuroWorks), sampled at 2048 Hz (bandwidth 0.01-682 Hz), later downsampled to 512 Hz (doi:10.1038/s42003-024-06732-2, Methods ‘iEEG data recording and preprocessing’). Deposited trials: 4097 samples from -4.0 to 4.0 s (512 Hz), trials x channels per subject in D.trials (Voyager Job ieeg-b3enr-c-hammer-1007220748). The deposited data are bipolar referenced, high-pass filtered at 0.1 Hz and notch filtered at 50 Hz and harmonics; D.rejected marks rejected samples (deposit readme). Reference scheme. Recording reference and ground electrodes in white matter (subject-specific locations) (doi:10.1038/s42003-024-06732-2); the deposited channels are bipolar pairs of neighbouring contacts (e.g. ‘A1-A2’) (deposit readme; doi:10.1038/s42003-024-06732-2 Results). Electrode types. Intracerebral (SEEG) electrodes, DIXI Medical; cylindrical contacts 0.8 mm diameter, 2 mm height, 1.5 mm spacing (doi:10.1038/s42003-024-06732-2). Localisation method. Contacts localised on post-implantation CT coregistered to pre-implantation MRI, verified on post-implantation MRI, MRI normalised to MNI space with SPM12; each bipolar channel was given the MNI coordinate of the centre between its contacts and assigned to the DMN or DAN with the Yeo-7 atlas; only DMN/DAN channels are exported (doi:10.1038/s42003-024-06732-2, Methods ‘iEEG channel assignment’; deposit readme). The deposit gives channels_MNI for every exported channel; the column atlas_label_AAL3v1 of each electrodes.tsv adds an AAL3v1 atlas lookup of these coordinates (ieeg-atlas coord_regions.py, nearest labelled voxel; a derived label, not given by the authors).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000404-blue)](https://doi.org/10.82901/nemar.nm000404) # Internal-external attention switching: SEEG epochs from 25 epilepsy patients (derivative) Stereo-EEG epochs released with: > Hammer J, Kajsova M, Kalina A, Krysl D, Fabera P, Kudr M, Jezdik P, Janca R, Krsek P, Marusic P (2024). > Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during > internal-external attention switching. Communications Biology 7:1105. https://doi.org/10.1038/s42003-024-06732-2 Source record: Zenodo https://doi.org/10.5281/zenodo.12796062 (v3, CC-BY-4.0): msSEI_exportTrials.zip. This is a derivative dataset. The release contains the authors’ preprocessed epochs, not the continuous recordings. ## Participants and task (from the paper) 25 patients with drug-resistant epilepsy (15 female; age 34 +/- 12 years) in presurgical SEEG monitoring at Motol University Hospital, Prague; implantation by clinical need only; approved by the hospital’s ethics committee; written informed consent. Per-subject age and sex are not in the release; since 2026-10-08 participants.tsv gives them from the article’s Supplementary Table 1. Subjects alternated between an external-attention task (visual search: find the T among 35 Ls on a 6x6 grid and report whether it is in the upper or lower half) and an internal-attention task (yes/no answer to a statement about their own past experiences), answering on a gamepad within 5 s, with no pause between trials and not switching on every trial. Four sessions of several minutes (about 30 min). Each epoch is centred on a task switch: E-I (external to internal) or I-E (internal to external). ## Recording and preprocessing (by the authors) DIXI Medical depth electrodes, Quantum amplifiers / NeuroWorks, 2048 Hz (0.01-682 Hz), reference and ground in white matter. The authors downsampled to 512 Hz, removed broken channels and channels in the seizure-onset or irritative zone or in heterotopic cortex, built bipolar derivations along each shank, high-pass filtered at 0.1 Hz and notch filtered at 50 Hz and harmonics (Butterworth, 6th order, zero phase), cut epochs from -4 to +4 s around each switch, and kept only channels assigned to the default mode network (DMN) or dorsal attention network (DAN) by the Yeo-7 atlas. ## Files - sub-P<k>/ieeg/*_ieeg.vhdr|.eeg|.vmrk: the epochs written back to back (4097 samples = 8.002 s each), BrainVision

IEEE float32. Values are the release’s float64 values rounded to float32; no other change. The release does not state the physical unit; the channels are labelled µV because amplitudes of a few to tens of units match µV-scaled iEEG, but this is our assumption, not a statement of the authors.

*_events.tsv: one row per epoch at the switch time, with trial_type (E-I / I-E), epoch number, epoch start and the fraction of samples marked rejected in the release.
*_channels.tsv: bipolar channel names as released, with the network label (DMN / DAN).
*_space-Other_electrodes.tsv: the release’s MNI coordinates per bipolar channel.
sourcedata/zenodo-12796062/: the original trials_P<k>.mat files (including the per-sample rejection masks) and the release read-me, unchanged.

The figure source data (code_figures.zip) and analysis code (code_pipeline.zip) of the record are not copied; they are available from the source record and GitHub. ## Licence CC-BY-4.0, as the source record. Please cite the paper and the Zenodo record. ## Additional metadata and localisation (added 2026-10-08) Compiled after the upload from the article, its supplement and the source deposit (each statement names its source). Text and sidecar metadata only; no data file was changed. Recording system. Medical amplifiers (Quantum, NeuroWorks), sampled at 2048 Hz (bandwidth 0.01-682 Hz), later downsampled to 512 Hz (doi:10.1038/s42003-024-06732-2, Methods ‘iEEG data recording and preprocessing’). Deposited trials: 4097 samples from -4.0 to 4.0 s (512 Hz), trials x channels per subject in D.trials (Voyager Job ieeg-b3enr-c-hammer-1007220748). The deposited data are bipolar referenced, high-pass filtered at 0.1 Hz and notch filtered at 50 Hz and harmonics; D.rejected marks rejected samples (deposit readme). Reference scheme. Recording reference and ground electrodes in white matter (subject-specific locations) (doi:10.1038/s42003-024-06732-2); the deposited channels are bipolar pairs of neighbouring contacts (e.g. ‘A1-A2’) (deposit readme; doi:10.1038/s42003-024-06732-2 Results). Electrode types. Intracerebral (SEEG) electrodes, DIXI Medical; cylindrical contacts 0.8 mm diameter, 2 mm height, 1.5 mm spacing (doi:10.1038/s42003-024-06732-2). Localisation method. Contacts localised on post-implantation CT coregistered to pre-implantation MRI, verified on post-implantation MRI, MRI normalised to MNI space with SPM12; each bipolar channel was given the MNI coordinate of the centre between its contacts and assigned to the DMN or DAN with the Yeo-7 atlas; only DMN/DAN channels are exported (doi:10.1038/s42003-024-06732-2, Methods ‘iEEG channel assignment’; deposit readme). The deposit gives channels_MNI for every exported channel; the column atlas_label_AAL3v1 of each electrodes.tsv adds an AAL3v1 atlas lookup of these coordinates (ieeg-atlas coord_regions.py, nearest labelled voxel; a derived label, not given by the authors).

License: CC-BY-4.0

Authors:

Jiri Hammer

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000404

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=25, range 12–55 yr, mean 34.3 yr)

101520253035404555

Female · 15Male · 10

Sex composition

Female
15
Male
10
F : M ratio
1.50 : 1

60% female · n = 25 subjects with reported sex.

HandednessRight · 18Left · 7

Channel counts (ch)

11172123262830363839444546474849516061626364

Sampling frequencies: 512.0 Hz (n=25 recordings)

Total recording duration: 6 h 53 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 17 (2), 48 (2), 64 (2), 49, 45, 28, 60, 47, 30, 21, 61, 11, 62, 38, 36, 44, 26, 51, 46, 39, 63, 23 ch · iEEG · 512 Hz · 25 subjects, 25 recordings

Live trace viewer — sub-P1 · task-attentionswitch

Showing one representative recording out of 25 subjects and 25 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 · 63 sensors — 63 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.NM000404(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000404

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

class eegdash.dataset.NM000404(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024)

Study:: nm000404 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000404.

Modality: ieeg; Subject type: Unknown. Subjects: 25; recordings: 25; 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/nm000404 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000404 DOI: https://doi.org/10.82901/nemar.nm000404

Examples

>>> from eegdash.dataset import NM000404
>>> dataset = NM000404(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 — NM000404.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 nm000404 to reproduce the tutorial on this dataset.

Citation

Jiri Hammer (2024). Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024). 10.82901/nemar.nm000404

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000404.

Related & sibling datasets

NM000354IEEG · 20 subj NM000368IEEG · 35 subj NM000375IEEG · 16 subj NM000387IEEG · 9 subj NM000374IEEG · 11 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000404

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000404`
Title	Internal-external attention switching SEEG epochs, DMN and DAN channels, 25 epilepsy patients (Hammer et al. 2024)
Author (year)	—
Canonical	—
Importable as	`NM000404`
Year	2024
Authors	Jiri Hammer
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000404
Source links	OpenNeuro \| NeMAR \| Source URL

NM000404: ieeg dataset, 25 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#