DS006136: ieeg dataset, 13 subjects#
OWM-Dataset
Citation: Vladimir Omelyusik, Tyler S. Davis, Satish S. Nair, Behrad Noudoost, Patrick Hackett, Elliot H. Smith, Shervin Rahimpour, John D. Rolston, Bornali Kundu (20). OWM-Dataset. 10.18112/openneuro.ds006136.v1.0.1
13-participant iEEG dataset — OWM-Dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006136
dataset = DS006136(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006136(cache_dir="./data", subject="01")
Advanced query
dataset = DS006136(
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{ds006136,
title = {OWM-Dataset},
author = {Vladimir Omelyusik and Tyler S. Davis and Satish S. Nair and Behrad Noudoost and Patrick Hackett and Elliot H. Smith and Shervin Rahimpour and John D. Rolston and Bornali Kundu},
doi = {10.18112/openneuro.ds006136.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds006136.v1.0.1},
}
About This Dataset#
The dataset contains processed intracranial EEG recordings from frontal (LMFG, RMFG) and temporal (LMTG, RMTG) areas of 13 subjects (epilepsy patients) while they performed a load-3 object working memory task. Please see the associated publication (Paper): https://doi.org/10.1016/j.neuroimage.2026.121718
The dataset includes trials which were used for the final analyses (i.e., after artifact rejection; see the Methods section of the Paper for a full description of preprocessing procedures). Note that since some artifact rejection procedures were performed at the single-trial level, trial indexes are not matched across channels even for the same subject (i.e., trial 1 of channel 1 may not correspond to trial 1 of channel 2) and have to be read separately. The sourcedata/ folder contains per-subject trial indexes for trial matching.
OWM-Dataset
Description
Trial structure
Each trial is 6498 ms long (1000 ms of fixation, 1500 ms of encoding and 3998 ms of delay).
Storage format
To comply with the .edf format, trials for every channel were concatenated into a single one-dimensional array. Due to a different number of trials across channels, each array was padded with 0s on the right, ensuring the same data length for all channels within a subject. The total number of concatenated trials per channel and the padding length are recorded in the “_channels.tsv” file for each subject.
Performance
The sourcedata/ folder contains performance results for every subject. Rows of each table correspond to trials (the order matches LFP recordings). Columns represent whether the subject selected the presented stimuli during the search period (0 = no, 1 = yes).
Reading the data and replicating the results
The Paper repository (V-Marco/FT-bursting-WM) includes a Python function for reading the data, performing trial matching, appending performance information, and representing the recordings as a 2D table. The repository also includes examples on replicating the main figures.
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 1000.0 Hz (n=14 recordings)
Total recording duration: 3 h 44 min
Signal · Electrodes & live trace#
Live trace viewer — sub-MU1 · task-OWM
Showing one representative recording out of
13 subjects and 14 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 · 9 sensors — 9 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 |
OWM-Dataset |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Vladimir Omelyusik, Tyler S. Davis, Satish S. Nair, Behrad Noudoost, Patrick Hackett, Elliot H. Smith, Shervin Rahimpour, John D. Rolston, Bornali Kundu |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006136,
title = {OWM-Dataset},
author = {Vladimir Omelyusik and Tyler S. Davis and Satish S. Nair and Behrad Noudoost and Patrick Hackett and Elliot H. Smith and Shervin Rahimpour and John D. Rolston and Bornali Kundu},
doi = {10.18112/openneuro.ds006136.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds006136.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006136 · Omelyusik2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006136(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
OWM-Dataset
- Study:
ds006136(OpenNeuro)- Author (year):
Omelyusik2025- Canonical:
—
Also importable as:
DS006136,Omelyusik2025.Modality:
ieeg; Experiment type:Memory; Subject type:Epilepsy. Subjects: 13; recordings: 14; 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/ds006136 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006136 DOI: https://doi.org/10.18112/openneuro.ds006136.v1.0.1
Examples
>>> from eegdash.dataset import DS006136 >>> dataset = DS006136(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/ds006136").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 ds006136 to reproduce the tutorial on this dataset.
Citation
Vladimir Omelyusik, Tyler S. Davis, Satish S. Nair, Behrad Noudoost, Patrick Hackett, … (20). OWM-Dataset. 10.18112/openneuro.ds006136.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006136.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset