DS005034: eeg dataset, 25 subjects#

The effect of theta tACS on working memory

Access recordings and metadata through EEGDash.

Citation: Yuri G. Pavlov, Dauren Kasanov (2024). The effect of theta tACS on working memory. 10.18112/openneuro.ds005034.v1.0.1

Modality: eeg Subjects: 25 Recordings: 100 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

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/)

Dataset Information#

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	2024
Authors	Yuri G. Pavlov, Dauren Kasanov
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005034.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: 25
Recordings: 100
Tasks: 2

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 1000.0
Duration (hours): 34.91862888888889

Electrode Layout#

Electrode layout — EEG · 129 sensors — 129 channels

Dataset Statistics#

Age distribution (n=25, range 18–39 yr)

1520253035

Sex distribution

10

15

Female Male Total: 25

Channel counts: 129 ch (n=100 recordings)

Sampling frequencies: 1000.0 Hz (n=100 recordings)

Total recording duration: 34 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 DS005034 class to access this dataset programmatically.

class eegdash.dataset.DS005034(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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)