DS005779#

Real-time personalized brain state-dependent TMS in healthy adults

Access recordings and metadata through EEGDash.

Citation: Uttara Khatri, Sara Hussain (2025). Real-time personalized brain state-dependent TMS in healthy adults. 10.18112/openneuro.ds005779.v1.0.1

Modality: eeg Subjects: 19 Recordings: 1523 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005779

dataset = DS005779(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005779(cache_dir="./data", subject="01")

Advanced query

dataset = DS005779(
    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{ds005779,
  title = {Real-time personalized brain state-dependent TMS in healthy adults},
  author = {Uttara Khatri and Sara Hussain},
  doi = {10.18112/openneuro.ds005779.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005779.v1.0.1},
}

About This Dataset#

This dataset contains raw data for the following publication: Khatri, U.U., Pulliam, K., Manesiya, M., Cortez, M.V., Millán, J.D.R. and Hussain, S.J., 2024. Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time. Brain Stimulation, in press. Real-time and offline analysis code can be found here: SMNPLab/Realtime_decoding_neurotypical.git This work was funded by NINDS under award number R21NS133605.

Dataset Information#

Dataset ID

DS005779

Title

Real-time personalized brain state-dependent TMS in healthy adults

Year

2025

Authors

Uttara Khatri, Sara Hussain

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005779.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005779,
  title = {Real-time personalized brain state-dependent TMS in healthy adults},
  author = {Uttara Khatri and Sara Hussain},
  doi = {10.18112/openneuro.ds005779.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005779.v1.0.1},
}

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: 19

  • Recordings: 1523

  • Tasks: 16

Channels & sampling rate

  • Channels: 67 (470), 64 (28), 70 (2)

  • Sampling rate (Hz): 5000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Other

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 88.7 GB

  • File count: 1523

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds005779.v1.0.1

Provenance

API Reference#

Use the DS005779 class to access this dataset programmatically.

class eegdash.dataset.DS005779(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005779. Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Healthy. Subjects: 19; recordings: 250; tasks: 16.

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/ds005779 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005779

Examples

>>> from eegdash.dataset import DS005779
>>> dataset = DS005779(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

See Also#