DS004703: ieeg dataset, 10 subjects#
sEEG Passive listening to natural speech
Citation: Anna Mai, Stephanie Ries, Sharona Ben-Haim, Jerry Shih, Timothy Gentner (20). sEEG Passive listening to natural speech. 10.18112/openneuro.ds004703.v1.1.0
10-participant iEEG dataset — sEEG Passive listening to natural speech.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004703
dataset = DS004703(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004703(cache_dir="./data", subject="01")
Advanced query
dataset = DS004703(
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{ds004703,
title = {sEEG Passive listening to natural speech},
author = {Anna Mai and Stephanie Ries and Sharona Ben-Haim and Jerry Shih and Timothy Gentner},
doi = {10.18112/openneuro.ds004703.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds004703.v1.1.0},
}
About This Dataset#
CONTACT
For questions about this data set, please contact Anna Mai (anna.mai@mpi.nl; ORCiD 0000-0002-8343-9216). PERMISSIONS These data may not be used for commericial purposes, including but not limited to use in any kind of training set for commercial machine learning applications.
These data may not be used in any way that either in part or in whole disambiguates participant identity, including but not limited to attempts at 3D facial reconstruction.
RECORDING SETUP These data were collected from June 2018 to August 2019.
For all patients, a scalp electrode was used for referencing and ground. These were 13mm, 2.5M single lead subdermal electrodes made by Rochester Electro-Medical with serial number S81025-A-24RM. Depth electrodes were manufactured by Ad-Tech and are Spencer Probe depth electrodes. Each electrode has 10 leads evenly spaced 3-7mm apart.
With the exception of patients SD012 and SD022, all implants are depth electrodes. Patients SD012 and SD022 had grid and strip electrodes implanted in addition to several depth electrodes. Any channel names beginning with ``C’’ were not used and should be dropped from analyses. TASK Participants passively listened to 30-45s passages of conversational speech and verbally answered a 2AC content question after each passage. 6 blocks with 7 passages per block. MISSING DATA Anatomical scans for particpant SD012 are not available due to excessive movement artifacts.
Cohort#
Dataset Statistics#
Age distribution by gender (n=10, range 21–55 yr, mean 32.3 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 9 h 5 min
Signal · Electrodes & live trace#
Live trace viewer — sub-SD018 · ses-01 · task-PassiveListen
Showing one representative recording out of
10 subjects and 11 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
sEEG Passive listening to natural speech |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Anna Mai, Stephanie Ries, Sharona Ben-Haim, Jerry Shih, Timothy Gentner |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004703,
title = {sEEG Passive listening to natural speech},
author = {Anna Mai and Stephanie Ries and Sharona Ben-Haim and Jerry Shih and Timothy Gentner},
doi = {10.18112/openneuro.ds004703.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds004703.v1.1.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004703 · Mai2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004703(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
sEEG Passive listening to natural speech
- Study:
ds004703(OpenNeuro)- Author (year):
Mai2023- Canonical:
—
Also importable as:
DS004703,Mai2023.Modality:
ieeg; Experiment type:Memory; Subject type:Surgery. Subjects: 10; recordings: 11; 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/ds004703 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004703 DOI: https://doi.org/10.18112/openneuro.ds004703.v1.1.0 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004703 >>> dataset = DS004703(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/ds004703").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 ds004703 to reproduce the tutorial on this dataset.
Citation
Anna Mai, Stephanie Ries, Sharona Ben-Haim, Jerry Shih, Timothy Gentner (20). sEEG Passive listening to natural speech. 10.18112/openneuro.ds004703.v1.1.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004703.v1.1.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset