EEGdash›OpenNeuro›DS004752

Iss. 4752 · 15 subjects · 136 recordings · CC0

Dataset Brief · Dataset of intracranial EEG, scalp EEG and beamforming source…

DS004752: eeg, ieeg dataset, 15 subjects#

Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task

Citation: Vasileios Dimakopoulos, Lennart Stieglitz, Lukas Imbach, Johannes Sarnthein (20). Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task. 10.18112/openneuro.ds004752.v1.0.1

15-participant EEG, iEEG dataset — Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task.

Data & curation Vasileios Dimakopoulos · Lennart Stieglitz · Lukas Imbach · Johannes Sarnthein
Year 20 · Distributed via OpenNeuro
Funding Swiss National Science Foundation, SNSF 204651 · Alexander S Onassis Foundation PhD Scholarship

EEG, iEEG · 64 (34), 8 (16), 20 (15), 21 (14), 19 (10), 10 (7), 23 (6), 36 (6), 68 (6), 46 (6), 48 (4), 62 (4), 40 (3), 32 (3), 80 (2) ch200, 2000, 4000, 4096 HzBIDS 1.4.0Task · verbalWM8 sessionsEpilepsyAuditoryMemory

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 DS004752

dataset = DS004752(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 = DS004752(cache_dir="./data", subject="01")

Advanced query

dataset = DS004752(
    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{ds004752,
  title = {Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task},
  author = {Vasileios Dimakopoulos and Lennart Stieglitz and Lukas Imbach and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds004752.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004752.v1.0.1},
}

§ 02Study · The README

About This Dataset#

We present an electrophysiological dataset recorded from fifteen subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10-20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms, and the MNI coordinates and anatomical labels of all intracranial electrodes. The dataset includes also reconstructed virtual sensor data that were created by performing LCMV beamforming on the EEG at specific brain regions including, temporal superior lobe, lateral prefrontal cortex, occipital cortex, posterior parietal cortex, and Broca. Subject characteristics and information on sessions (set size, match/mismatch, correct/incorrect, response, response time for each trial) are also provided. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, alongside reconstructed beamforming EEG sources that can enable further cognitive analysis such as replay of memory items.

Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.

Dataset of intracranial EEG, scalp EEG and beamforming sources from human epilepsy patients performing a verbal working memory task

Description

Subfolders

verbalWM/sub-/: Contains folders for each subject, named sub- and session information.
verbalWM/sub-/ses-/ieeg/: Contains the raw iEEG data in .edf format for each subject. Each subject performed more than 1 working memory session (ses-0x) each of which includes ~50 trials. Each *ieeg.edf file contains continuous iEEG data during the working memory task. Details about the channels are given in the corresponding .tsv file. We also provide the information on the trial start and end in the events.tsv files by specifying the start and end sample of each trial.
verbalWM/sub-/ses-/eeg/: Contains the raw EEG data in .edf format for each subject. Each subject performed more than 1 working memory session (ses-0x) each of which includes ~50 trials. Each *eeg.edf file contains continuous EEG data during the working memory task. Details about the channels are given in the corresponding .tsv file. We also provide the information on the trial start and end in the events.tsv files by specifying the start and end sample of each trial.
verbalWM/derivatives/sub-/: Contains the LCMV beamforming sources during encoding and maintenance. The beamforming sources are in the form of virtual EEG sensors each of which corresponds to a specific brain region. The naming convention used for the virtual sensors is the following: DLPFC; dorsolateral pre-frontal cortex, OFC; orbitofrontal cortex, PPC; posterior parietal cortex, AC; auditory cortex, V1; primary visual cortex

BIDS Conversion

bids-starter-kid and custom Matlab scripts were used to convert the dataset into BIDS format.

References

[1] Dimakopoulos V, Megevand P, Stieglitz LH, Imbach L, Sarnthein J. Information flows from hippocampus to auditory cortex during replay of verbal working memory items. Elife 2022;11. 10.7554/eLife.78677 [2] Boran E, Fedele T, Klaver P, Hilfiker P, Stieglitz L, Grunwald T, et al. Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load. Science Advances 2019;5(3):eaav3687. 10.1126/sciadv.aav3687 [3] Boran E, Fedele T, Steiner A, Hilfiker P, Stieglitz L, Grunwald T, et al. Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task. Scientific Data 2020;7(1):30. 10.1038/s41597-020-0364-3

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=15, range 18–56 yr, mean 31.8 yr)

1520253035455055

Female · 8Male · 7

Sex composition

Female
8
Male
7
F : M ratio
1.14 : 1

53% female · n = 15 subjects with reported sex.

Channel counts (ch)

81019202123323640464862646880

Sampling frequencies (Hz)

200200040004096

Total recording duration: 18 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 (34), 8 (16), 20 (15), 21 (14), 19 (10), 10 (7), 23 (6), 36 (6), 68 (6), 46 (6), 48 (4), 62 (4), 40 (3), 32 (3), 80 (2) ch · EEG, iEEG · 200, 2000, 4000, 4096 Hz · 15 subjects, 136 recordings

Live trace viewer — sub-13 · ses-02 · task-verbalWM · run-01

Showing one representative recording out of 15 subjects and 136 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 8 sensors — 8 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.DS004752(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Dimakopoulos2023_intracranial

Canonical—

Importable asDS004752 · Dimakopoulos2023_intracranial

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

class eegdash.dataset.DS004752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task

Study:: ds004752 (OpenNeuro)
Author (year):: Dimakopoulos2023_intracranial
Canonical:: —

Also importable as: DS004752, Dimakopoulos2023_intracranial.

Modality: eeg, ieeg. Subjects: 15; recordings: 136; 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/ds004752 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004752 DOI: https://doi.org/10.18112/openneuro.ds004752.v1.0.1 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS004752
>>> dataset = DS004752(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 FacePre-bundled mirror at EEGDash/ds004752 · pull with datasets.load_dataset("EEGDash/ds004752").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004752.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 ds004752 to reproduce the tutorial on this dataset.

Citation

Vasileios Dimakopoulos, Lennart Stieglitz, Lukas Imbach, Johannes Sarnthein (20). Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task. 10.18112/openneuro.ds004752.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004752.v1.0.1.

Related & sibling datasets

DS004477EEG · 9 subj DS003570EEG · 40 subj DS004000EEG · 43 subj DS002814EEG · 21 subj DS004851EEG · 66 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.4.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004752.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004752`
Title	Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task
Author (year)	`Dimakopoulos2023_intracranial`
Canonical	—
Importable as	`DS004752`, `Dimakopoulos2023_intracranial`
Year	20
Authors	Vasileios Dimakopoulos, Lennart Stieglitz, Lukas Imbach, Johannes Sarnthein
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004752.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS004752: eeg, ieeg dataset, 15 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#