DS005574: ieeg dataset, 9 subjects#
The “Podcast” ECoG dataset
Citation: Zaid Zada, Samuel A. Nastase, Bobbi Aubrey, Itamar Jalon, Ariel Goldstein, Sebastian Michelmann, Haocheng Wang, Liat Hasenfratz, Werner Doyle, Daniel Friedman, Patricia Dugan, Lucia Melloni, Sasha Devore, Orrin Devinsky, Adeen Flinker, Uri Hasson (—). The “Podcast” ECoG dataset. 10.18112/openneuro.ds005574.v1.0.2
9-participant iEEG dataset — The "Podcast" ECoG dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005574
dataset = DS005574(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005574(cache_dir="./data", subject="01")
Advanced query
dataset = DS005574(
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{ds005574,
title = {The "Podcast" ECoG dataset},
author = {Zaid Zada and Samuel A. Nastase and Bobbi Aubrey and Itamar Jalon and Ariel Goldstein and Sebastian Michelmann and Haocheng Wang and Liat Hasenfratz and Werner Doyle and Daniel Friedman and Patricia Dugan and Lucia Melloni and Sasha Devore and Orrin Devinsky and Adeen Flinker and Uri Hasson},
doi = {10.18112/openneuro.ds005574.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds005574.v1.0.2},
}
About This Dataset#
The “Podcast” ECoG dataset for modeling neural activity during natural story listening.
We introduce the “Podcast” electrocorticography (ECoG) dataset for modeling neural activity supporting natural narrative comprehension. This dataset combines the exceptional spatiotemporal resolution of human intracranial electrophysiology with a naturalistic experimental paradigm for language comprehension. In addition to the raw data, we provide a minimally preprocessed version in the high-gamma spectral band to showcase a simple pipeline and to make it easier to use. Furthermore, we include the auditory stimuli, an aligned word-level transcript, and linguistic features ranging from low-level acoustic properties to large language model (LLM) embeddings. We also include tutorials replicating previous findings and serve as a pedagogical resource and a springboard for new research. The dataset comprises 9 participants with 1,330 electrodes, including grid, depth, and strip electrodes. The participants listened to a 30-minute story with over 5,000 words. By using a natural story with high-fidelity, invasive neural recordings, this dataset offers a unique opportunity to investigate language comprehension.
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 4 h 29 min
Signal · Electrodes & live trace#
Live trace viewer — sub-08 · task-podcast
Showing one representative recording out of
9 subjects and 9 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 · 75 sensors — 75 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
The “Podcast” ECoG dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Zaid Zada, Samuel A. Nastase, Bobbi Aubrey, Itamar Jalon, Ariel Goldstein, Sebastian Michelmann, Haocheng Wang, Liat Hasenfratz, Werner Doyle, Daniel Friedman, Patricia Dugan, Lucia Melloni, Sasha Devore, Orrin Devinsky, Adeen Flinker, Uri Hasson |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005574,
title = {The "Podcast" ECoG dataset},
author = {Zaid Zada and Samuel A. Nastase and Bobbi Aubrey and Itamar Jalon and Ariel Goldstein and Sebastian Michelmann and Haocheng Wang and Liat Hasenfratz and Werner Doyle and Daniel Friedman and Patricia Dugan and Lucia Melloni and Sasha Devore and Orrin Devinsky and Adeen Flinker and Uri Hasson},
doi = {10.18112/openneuro.ds005574.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds005574.v1.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005574 · Zada2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
The “Podcast” ECoG dataset
- Study:
ds005574(OpenNeuro)- Author (year):
Zada2024- Canonical:
—
Also importable as:
DS005574,Zada2024.Modality:
ieeg; Experiment type:Other; Subject type:Unknown. Subjects: 9; recordings: 9; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds005574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005574 DOI: https://doi.org/10.18112/openneuro.ds005574.v1.0.2
Examples
>>> from eegdash.dataset import DS005574 >>> dataset = DS005574(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds005574").huggingface
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 ds005574 to reproduce the tutorial on this dataset.
Citation
Zaid Zada, Samuel A. Nastase, Bobbi Aubrey, Itamar Jalon, Ariel Goldstein, … (n.d.). The "Podcast" ECoG dataset. 10.18112/openneuro.ds005574.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005574.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset