EEGdash›OpenNeuro›DS003374

Iss. 3374 · 9 subjects · 18 recordings · CC0

Dataset Brief · Dataset of neurons and intracranial EEG from human amygdala d…

DS003374: ieeg dataset, 9 subjects#

Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation

Citation: Tommaso Fedele, Ece Boran, Valeri Chirkov, Peter Hilfiker, Thomas Grunwald, Lennart Stieglitz, Hennric Jokeit, Johannes Sarnthein (—). Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation. 10.18112/openneuro.ds003374.v1.1.1

9-participant iEEG dataset — Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation.

Data & curation Tommaso Fedele · Ece Boran · Valeri Chirkov · Peter Hilfiker · Thomas Grunwald · Lennart Stieglitz · …
Funding Swiss National Science Foundation, SNSF 320030_156029 · Mach-Gaensslen Stiftung · + 3 more

iEEG · 4 (10), 2 (8) ch2000 HzBIDS 1.4.0Task · jokeitEpilepsyVisualAffect

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 DS003374

dataset = DS003374(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 = DS003374(cache_dir="./data", subject="01")

Advanced query

dataset = DS003374(
    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{ds003374,
  title = {Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation},
  author = {Tommaso Fedele and Ece Boran and Valeri Chirkov and Peter Hilfiker and Thomas Grunwald and Lennart Stieglitz and Hennric Jokeit and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds003374.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds003374.v1.1.1},
}

§ 02Study · The README

About This Dataset#

We present an electrophysiological dataset collected from the amygdalae of nine subjects attending a visual dynamic stimulation of emotional aversive content. The subjects were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Subjects were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition).

We provide the recordings of intracranial EEG (iEEG) and metadata related to the task, subjects, sessions and electrodes in the BIDS standard.

We also provide a more extended version of the dataset that includes neuronal spike times and waveforms in the NIX standard under the folder “bidsignore/data_NIX”. This extended dataset is also available in G-Node at https://gin.g-node.org/USZ_NCH/Human_Amygdala_MUA_sEEG_FearVideo/.

Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation

Summary

This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.

Repository structure

View full README

Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation

Summary

This dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.

Repository structure

Main directory

Contains metadata in the BIDS standard.

Directories sub-**

Contains folders for each subject, named sub-<subject number>.

Directory bidsignore

Contains data in the NIX standard, and metadata files. Subject_Characteristics.pdf describes subjects and NIX_File_Structure.pdf describes the structure of the NIX files.

Directory code_MATLAB

Contains MATLAB code for loading the data and generating the publication figures. Main_Load_NIX_Data.m contains code snippets for reading NIX data and task related information. Main_Plot_Figures.m uses the functions Figure_2.m and Figure_3.m to generate figures.

Required dependencies to run the script Main_Load_NIX_Data.m: * Nix-mx v1.4.1

Required dependencies to run the script Main_Plot_Figures.m: * Nix-mx v1.4.1 * Gramm * FieldTrip

Directory data_NIX

Contains nix files for each session of the task. Each file is named with the format:

Data_Subject_<subject number>_Session_<session number>.h5

Support

For questions on the dataset or the task, contact Johannes Sarnthein at johannes.sarnthein@usz.ch.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=9, range 19–48 yr, mean 29.4 yr)

152025303545

Female · 2Male · 7

Sex composition

Female
2
Male
7
F : M ratio
0.29 : 1

22% female · n = 9 subjects with reported sex.

Channel counts (ch)

24

Sampling frequencies: 2000.0 Hz (n=18 recordings)

Total recording duration: 2 h 36 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 4 (10), 2 (8) ch · iEEG · 2000 Hz · 9 subjects, 18 recordings

Live trace viewer — sub-08 · ses-01 · task-jokeit · run-01

Showing one representative recording out of 9 subjects and 18 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 · 2 sensors — 2 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.DS003374(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Fedele2020

Canonical—

Importable asDS003374 · Fedele2020

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

class eegdash.dataset.DS003374(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation

Study:: ds003374 (OpenNeuro)
Author (year):: Fedele2020
Canonical:: —

Also importable as: DS003374, Fedele2020.

Modality: ieeg; Experiment type: Affect; Subject type: Epilepsy. Subjects: 9; recordings: 18; 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/ds003374 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003374 DOI: https://doi.org/10.18112/openneuro.ds003374.v1.1.1 NEMAR citation count: 4

Examples

>>> from eegdash.dataset import DS003374
>>> dataset = DS003374(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/ds003374 · pull with datasets.load_dataset("EEGDash/ds003374").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003374.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 ds003374 to reproduce the tutorial on this dataset.

Citation

Tommaso Fedele, Ece Boran, Valeri Chirkov, Peter Hilfiker, Thomas Grunwald, … (n.d.). Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation. 10.18112/openneuro.ds003374.v1.1.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds003374.v1.1.1.

Related & sibling datasets

DS006136IEEG · 13 subj DS004473IEEG · 8 subj DS005411IEEG · 47 subj DS004194IEEG · 14 subj DS005059IEEG · 69 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.4.0

Sidecars

events · events.json · channels

Provenance

CC0 · 10.18112/openneuro.ds003374.v1.1.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003374`
Title	Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation
Author (year)	`Fedele2020`
Canonical	—
Importable as	`DS003374`, `Fedele2020`
Year	—
Authors	Tommaso Fedele, Ece Boran, Valeri Chirkov, Peter Hilfiker, Thomas Grunwald, Lennart Stieglitz, Hennric Jokeit, Johannes Sarnthein
License	CC0
Citation / DOI	10.18112/openneuro.ds003374.v1.1.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS003374: ieeg dataset, 9 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#