DS004752#

Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task

Access recordings and metadata through EEGDash.

Citation: Vasileios Dimakopoulos, Lennart Stieglitz, Lukas Imbach, Johannes Sarnthein (2023). Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task. 10.18112/openneuro.ds004752.v1.0.1

Modality: eeg Subjects: 15 Recordings: 904 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004752

dataset = DS004752(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 = DS004752(cache_dir="./data", subject="01")

Advanced query

dataset = DS004752(
    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{ds004752,
  title = {Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task},
  author = {Vasileios Dimakopoulos and Lennart Stieglitz and Lukas Imbach and Johannes Sarnthein},
  doi = {10.18112/openneuro.ds004752.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004752.v1.0.1},
}

About This Dataset#

Dataset of intracranial EEG, scalp EEG and beamforming sources from human epilepsy patients performing a verbal working memory task

Description

We present an electrophysiological dataset recorded from fifteen subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10-20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms, and the MNI coordinates and anatomical labels of all intracranial electrodes. The dataset includes also reconstructed virtual sensor data that were created by performing LCMV beamforming on the EEG at specific brain regions including, temporal superior lobe, lateral prefrontal cortex, occipital cortex, posterior parietal cortex, and Broca. Subject characteristics and information on sessions (set size, match/mismatch, correct/incorrect, response, response time for each trial) are also provided. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, alongside reconstructed beamforming EEG sources that can enable further cognitive analysis such as replay of memory items.

View full README

Dataset of intracranial EEG, scalp EEG and beamforming sources from human epilepsy patients performing a verbal working memory task

Description

We present an electrophysiological dataset recorded from fifteen subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10-20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms, and the MNI coordinates and anatomical labels of all intracranial electrodes. The dataset includes also reconstructed virtual sensor data that were created by performing LCMV beamforming on the EEG at specific brain regions including, temporal superior lobe, lateral prefrontal cortex, occipital cortex, posterior parietal cortex, and Broca. Subject characteristics and information on sessions (set size, match/mismatch, correct/incorrect, response, response time for each trial) are also provided. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, alongside reconstructed beamforming EEG sources that can enable further cognitive analysis such as replay of memory items.

Repository structure

Main directory (verbal WM)

Contains metadata files in the BIDS standard about the participants and the study. Folders are explained below.

Subfolders

verbalWM/sub-/: Contains folders for each subject, named sub- and session information.
verbalWM/sub-/ses-/ieeg/: Contains the raw iEEG data in .edf format for each subject. Each subject performed more than 1 working memory session (ses-0x) each of which includes ~50 trials. Each *ieeg.edf file contains continuous iEEG data during the working memory task. Details about the channels are given in the corresponding .tsv file. We also provide the information on the trial start and end in the events.tsv files by specifying the start and end sample of each trial.
verbalWM/sub-/ses-/eeg/: Contains the raw EEG data in .edf format for each subject. Each subject performed more than 1 working memory session (ses-0x) each of which includes ~50 trials. Each *eeg.edf file contains continuous EEG data during the working memory task. Details about the channels are given in the corresponding .tsv file. We also provide the information on the trial start and end in the events.tsv files by specifying the start and end sample of each trial.
verbalWM/derivatives/sub-/: Contains the LCMV beamforming sources during encoding and maintenance. The beamforming sources are in the form of virtual EEG sensors each of which corresponds to a specific brain region. The naming convention used for the virtual sensors is the following: DLPFC; dorsolateral pre-frontal cortex, OFC; orbitofrontal cortex, PPC; posterior parietal cortex, AC; auditory cortex, V1; primary visual cortex

BIDS Conversion

bids-starter-kid and custom Matlab scripts were used to convert the dataset into BIDS format.

References

[1] Dimakopoulos V, Megevand P, Stieglitz LH, Imbach L, Sarnthein J. Information flows from hippocampus to auditory cortex during replay of verbal working memory items. Elife 2022;11. 10.7554/eLife.78677

[2] Boran E, Fedele T, Klaver P, Hilfiker P, Stieglitz L, Grunwald T, et al. Persistent hippocampal neural firing and hippocampal-cortical coupling predict verbal working memory load. Science Advances 2019;5(3):eaav3687. 10.1126/sciadv.aav3687

[3] Boran E, Fedele T, Steiner A, Hilfiker P, Stieglitz L, Grunwald T, et al. Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task. Scientific Data 2020;7(1):30. 10.1038/s41597-020-0364-3

Dataset Information#

Dataset ID	`DS004752`
Title	Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task
Year	2023
Authors	Vasileios Dimakopoulos, Lennart Stieglitz, Lukas Imbach, Johannes Sarnthein
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004752.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: 15
Recordings: 904
Tasks: 1

Channels & sampling rate

Channels: 64 (68), 8 (32), 20 (30), 21 (28), 19 (20), 10 (14), 68 (12), 46 (12), 23 (12), 36 (12), 62 (8), 48 (8), 40 (6), 32 (6), 80 (4)
Sampling rate (Hz): 2000.0 (80), 4000.0 (80), 200.0 (72), 4096.0 (40)
Duration (hours): 0.0

API Reference#

Use the DS004752 class to access this dataset programmatically.

class eegdash.dataset.DS004752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004752. Modality: eeg, ieeg; Experiment type: Memory; Subject type: Epilepsy. Subjects: 15; recordings: 136; 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/ds004752 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004752

Examples

>>> from eegdash.dataset import DS004752
>>> dataset = DS004752(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