DS004624#

Intracranial recordings using BCI2000 and the CorTec BrainInterchange

Access recordings and metadata through EEGDash.

Citation: F. Mivalt, F. Lampert, M.A. van den Boom, P. Brunner, J. Kim, Andrea Duque-lopez, M. Krakorova, V. Kremen, D. Hermes, G.A. Worrell, K. J. Miller (2023). Intracranial recordings using BCI2000 and the CorTec BrainInterchange. 10.18112/openneuro.ds004624.v2.0.0

Modality: ieeg Subjects: 3 Recordings: 614 License: CC0 Source: openneuro Citations: 0.0

Metadata: Good (80%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004624

dataset = DS004624(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004624(cache_dir="./data", subject="01")

Advanced query

dataset = DS004624(
    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{ds004624,
  title = {Intracranial recordings using BCI2000 and the CorTec BrainInterchange},
  author = {F. Mivalt and F. Lampert and M.A. van den Boom and P. Brunner and J. Kim and Andrea Duque-lopez and M. Krakorova and V. Kremen and D. Hermes and G.A. Worrell and K. J. Miller},
  doi = {10.18112/openneuro.ds004624.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004624.v2.0.0},
}

About This Dataset#

An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in Humans This study aims to develop an ecosystem for the purpose of neurmodulation using the Cortec BCI device and BCI2000 software. Contact: For questions regarding this dataset, please contact mivalt.filip@mayo.edu or Miller.Kai@mayo.edu Funding: NIH U01NS128612

Dataset Information#

Dataset ID

DS004624

Title

Intracranial recordings using BCI2000 and the CorTec BrainInterchange

Year

2023

Authors

  1. Mivalt, F. Lampert, M.A. van den Boom, P. Brunner, J. Kim, Andrea Duque-lopez, M. Krakorova, V. Kremen, D. Hermes, G.A. Worrell, K. J. Miller

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004624.v2.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004624,
  title = {Intracranial recordings using BCI2000 and the CorTec BrainInterchange},
  author = {F. Mivalt and F. Lampert and M.A. van den Boom and P. Brunner and J. Kim and Andrea Duque-lopez and M. Krakorova and V. Kremen and D. Hermes and G.A. Worrell and K. J. Miller},
  doi = {10.18112/openneuro.ds004624.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004624.v2.0.0},
}

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: 3

  • Recordings: 614

  • Tasks: 28

Channels & sampling rate

  • Channels: Varies

  • Sampling rate (Hz): Varies

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 19.3 GB

  • File count: 614

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004624.v2.0.0

Provenance

API Reference#

Use the DS004624 class to access this dataset programmatically.

class eegdash.dataset.DS004624(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004624. Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 4; recordings: 66425; tasks: 32.

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/ds004624 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004624

Examples

>>> from eegdash.dataset import DS004624
>>> dataset = DS004624(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#