EEGdash›OpenNeuro›DS005034
Iss. 5034 · 25 subjects · 100 recordings · CC0
Dataset Brief · The effect of theta tACS on working memory

DS005034: eeg dataset, 25 subjects#

The effect of theta tACS on working memory

Citation: Yuri G. Pavlov, Dauren Kasanov (—). The effect of theta tACS on working memory. 10.18112/openneuro.ds005034.v1.0.1

25-participant EEG dataset — The effect of theta tACS on working memory.

Data & curation Yuri G. Pavlov · Dauren Kasanov
EEG · 129 ch1000 HzBIDS 1.8.02 tasks2 sessionsHealthyVisualMemory
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005034

dataset = DS005034(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005034(cache_dir="./data", subject="01")

Advanced query

dataset = DS005034(
    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{ds005034,
  title = {The effect of theta tACS on working memory},
  author = {Yuri G. Pavlov and Dauren Kasanov},
  doi = {10.18112/openneuro.ds005034.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005034.v1.0.1},
}
§ 02Study · The README

About This Dataset#

Following either a 20-minute verum or sham stimulation applied to Fpz-CPz at 1 mA and 6 Hz, the participants performed WM tasks, while EEG was recorded. The task required participants to either mentally manipulate memory items or retain them in memory as they were originally presented. In addition, before the working memory task, resting state EEG with eyes closed was recorded for 3 minutes and with eyes open for 1.5 minutes.

Behavioral performance data are available on OSF (https://osf.io/v2qwc/)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=25, range 18–39 yr, mean 24.2 yr)

1520253035
Female · 10Male · 15

Sex composition

25
subjects
Female
10
Male
15
F : M ratio
0.67 : 1
40% female · n = 25 subjects with reported sex.
HandednessRight · 23Left · 2

Channel counts: 129 ch (n=100 recordings)

Sampling frequencies: 1000.0 Hz (n=100 recordings)

Total recording duration: 34 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 129 ch · EEG · 1000 Hz · 25 subjects, 100 recordings
Live trace viewer — sub-14 · ses-verum · task-rest

Showing one representative recording out of 25 subjects and 100 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 129 sensors — 129 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.

NEMAR pipeline statistics — DS005034
HED event descriptors word cloud HED event descriptors word cloud — DS005034
§ 05Manifest · BIDS tree

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS005034

Title

The effect of theta tACS on working memory

Author (year)

Pavlov2024_effect_theta_tACS

Canonical

—

Importable as

DS005034, Pavlov2024_effect_theta_tACS

Year

—

Authors

Yuri G. Pavlov, Dauren Kasanov

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005034.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005034,
  title = {The effect of theta tACS on working memory},
  author = {Yuri G. Pavlov and Dauren Kasanov},
  doi = {10.18112/openneuro.ds005034.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005034.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005034(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Pavlov2024_effect_theta_tACS
Canonical—
Importable asDS005034 · Pavlov2024_effect_theta_tACS
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

The effect of theta tACS on working memory

Study:

ds005034 (OpenNeuro)

Author (year):

Pavlov2024_effect_theta_tACS

Canonical:

—

Also importable as: DS005034, Pavlov2024_effect_theta_tACS.

Modality: eeg. Subjects: 25; recordings: 100; tasks: 2.

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/ds005034 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005034 DOI: https://doi.org/10.18112/openneuro.ds005034.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005034
>>> dataset = DS005034(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.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds005034 · pull with datasets.load_dataset("EEGDash/ds005034").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005034.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
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 ds005034 to reproduce the tutorial on this dataset.

Citation

Yuri G. Pavlov, Dauren Kasanov (n.d.). The effect of theta tACS on working memory. 10.18112/openneuro.ds005034.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.ds005034.v1.0.1.

BIDS
BIDS 1.8.0
Sidecars
events · channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#