DS005574#

The “Podcast” ECoG dataset

Access recordings and metadata through EEGDash.

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 (2024). The “Podcast” ECoG dataset. 10.18112/openneuro.ds005574.v1.0.2

Modality: ieeg Subjects: 9 Recordings: 77 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS005574`
Title	The “Podcast” ECoG dataset
Year	2024
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	doi:10.18112/openneuro.ds005574.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

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: 77
Tasks: 1

Channels & sampling rate

Channels: 114 (2), 174 (2), 178 (2), 205 (2), 91 (2), 167 (2), 124 (2), 264 (2), 138 (2)
Sampling rate (Hz): 512.0 (16), 2048.0 (2)
Duration (hours): 0.0

API Reference#

Use the DS005574 class to access this dataset programmatically.

class eegdash.dataset.DS005574(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005574. 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

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/ds005574 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005574

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, 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