DS006126: eeg dataset, 5 subjects#

TDCS Modulation of Visual Cortex in Motor Imagery

Access recordings and metadata through EEGDash.

Citation: Anthony Mensah, Gleb Perevoznyuk, Artyom Batov, Aleksandra S. Pleskovskaya (2025). TDCS Modulation of Visual Cortex in Motor Imagery. 10.18112/openneuro.ds006126.v1.0.0

Modality: eeg Subjects: 5 Recordings: 90 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006126

dataset = DS006126(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 = DS006126(cache_dir="./data", subject="01")

Advanced query

dataset = DS006126(
    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{ds006126,
  title = {TDCS Modulation of Visual Cortex in Motor Imagery},
  author = {Anthony Mensah and Gleb Perevoznyuk and Artyom Batov and Aleksandra S. Pleskovskaya},
  doi = {10.18112/openneuro.ds006126.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006126.v1.0.0},
}

About This Dataset#

TDCS Neuromodulated Motor Imagery TMS Dataset

Research/Experiment Description

[Your research description goes here]

BIDS Report

The TDCS Modulation of Visual Cortex in Motor Imagery dataset was created by

Anthony Mensah, Gleb Perevoznyuk, Artyom Batov, and Aleksandra S. Pleskovskaya and conforms to BIDS version 1.7.0. This report was generated with MNE-BIDS (https://doi.org/10.21105/joss.01896). The dataset consists of 5 participants (comprised of 3 male and 3 female participants; comprised of 6 right hand, 0 left hand and 0 ambidextrous; ages ranged from 18.0 to 30.0 (mean = 23.0, std = 5.1)) and 3 recording sessions: An, Ca, and Sh. Data was recorded using an EEG system (Brain Products) sampled at 5000.0 Hz with line noise at 60.0 Hz. There were 90 scans in total. Recording durations ranged from 363.7 to 2910.98 seconds (mean = 429.21, std = 270.19), for a total of 38629.34 seconds of data recorded over all scans. For each dataset, there were on average 3.0 (std = 0.0) recording channels per scan, out of which 3.0 (std = 0.0) were used in analysis (0.0 +/- 0.0 were removed from analysis).

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

Dataset Information#

Dataset ID	`DS006126`
Title	TDCS Modulation of Visual Cortex in Motor Imagery
Author (year)	`Mensah2025`
Canonical	—
Importable as	`DS006126`, `Mensah2025`
Year	2025
Authors	Anthony Mensah, Gleb Perevoznyuk, Artyom Batov, Aleksandra S. Pleskovskaya
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006126.v1.0.0
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: 5
Recordings: 90
Tasks: 6

Channels & sampling rate

Channels: 3
Sampling rate (Hz): 5000.0
Duration (hours): 10.730372611111113

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=6, range 18–30 yr)

152030

Sex distribution

3

Female Male Total: 6

Channel counts: 3 ch (n=90 recordings)

Sampling frequencies: 5000.0 Hz (n=90 recordings)

Total recording duration: 10 h 43 min

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 DS006126 class to access this dataset programmatically.

class eegdash.dataset.DS006126(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

TDCS Modulation of Visual Cortex in Motor Imagery

Study:: ds006126 (OpenNeuro)
Author (year):: Mensah2025
Canonical:: —

Also importable as: DS006126, Mensah2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 90; tasks: 6.

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/ds006126 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006126 DOI: https://doi.org/10.18112/openneuro.ds006126.v1.0.0

Examples

>>> from eegdash.dataset import DS006126
>>> dataset = DS006126(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)