DS006136: ieeg dataset, 13 subjects#

OWM-Dataset

Access recordings and metadata through EEGDash.

Citation: Vladimir Omelyusik, Tyler S. Davis, Satish S. Nair, Behrad Noudoost, Patrick Hackett, Elliot H. Smith, Shervin Rahimpour, John D. Rolston, Bornali Kundu (2025). OWM-Dataset. 10.18112/openneuro.ds006136.v1.0.1

Modality: ieeg Subjects: 13 Recordings: 14 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

OWM-Dataset

Description

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

Data structure

Included trials

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.

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.

Dataset Information#

Dataset ID	`DS006136`
Title	OWM-Dataset
Author (year)	`Omelyusik2025`
Canonical	—
Importable as	`DS006136`, `Omelyusik2025`
Year	2025
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	doi:10.18112/openneuro.ds006136.v1.0.1
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: 13
Recordings: 14
Tasks: 1

Channels & sampling rate

Channels: 12 (2), 7 (2), 8 (2), 9 (2), 18, 11, 5, 17, 6, 14
Sampling rate (Hz): 1000.0
Duration (hours): 25.273888888888887

Electrode Layout#

Electrode layout — iEEG · 6 sensors — 6 channels

Dataset Statistics#

Channel counts (ch)

567891112141718

Sampling frequencies: 1000.0 Hz (n=14 recordings)

Total recording duration: 25 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS006136 class to access this dataset programmatically.

class eegdash.dataset.DS006136(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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/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)