DS003374#

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

Access recordings and metadata through EEGDash.

Citation: Tommaso Fedele, Ece Boran, Valeri Chirkov, Peter Hilfiker, Thomas Grunwald, Lennart Stieglitz, Hennric Jokeit, Johannes Sarnthein (2020). Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation. 10.18112/openneuro.ds003374.v1.1.1

Modality: ieeg Subjects: 9 Recordings: 104 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

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

Summary

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.

View full README

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

Summary

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/.

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:

Required dependencies to run the script Main_Plot_Figures.m:

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_.

Dataset Information#

Dataset ID

DS003374

Title

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

Year

2020

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
Copy-paste 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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 9

  • Recordings: 104

  • Tasks: 1

Channels & sampling rate

  • Channels: 4 (20), 2 (16)

  • Sampling rate (Hz): 2000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Epilepsy

  • Modality: Visual

  • Type: Affect

Files & format

  • Size on disk: 167.3 MB

  • File count: 104

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds003374.v1.1.1

Provenance

API Reference#

Use the DS003374 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds003374. 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#