DS002885#

DBS Phantom Recordings

Access recordings and metadata through EEGDash.

Citation: Ahmet Levent Kandemir, Vladimir Litvak, Esther Florin (2020). DBS Phantom Recordings. 10.18112/openneuro.ds002885.v1.0.1

Modality: meg Subjects: 2 Recordings: 115 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002885

dataset = DS002885(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002885(cache_dir="./data", subject="01")

Advanced query

dataset = DS002885(
    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{ds002885,
  title = {DBS Phantom Recordings},
  author = {Ahmet Levent Kandemir and Vladimir Litvak and Esther Florin},
  doi = {10.18112/openneuro.ds002885.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds002885.v1.0.1},
}

About This Dataset#

This dataset is a part of the data used for the study: ‘Kandemir, A.L., Litvak, V., Florin, E., 2020. The comparative performance of DBS artefact rejection methods for MEG recordings, NeuroImage, 2020, https://doi.org/10.1016/j.neuroimage.2020.117057.’

Please use the latest version of the dataset.

For detailed information about measurement protocol please refer to https://doi.org/10.1016/j.neuroimage.2020.117057. Additional information about CTF Phantom measurement is provided below. The customized Matlab code for artefact rejection methods is available at: lkandemir/dbs-artefact-rejection.

CTF Phantom Measurement Stimulation reference signal is captured with EEG001 Movement trigger is captured with UPPT001 Dipole activity is captured with HADC006

Dataset Information#

Dataset ID

DS002885

Title

DBS Phantom Recordings

Year

2020

Authors

Ahmet Levent Kandemir, Vladimir Litvak, Esther Florin

License

CC0

Citation / DOI

10.18112/openneuro.ds002885.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002885,
  title = {DBS Phantom Recordings},
  author = {Ahmet Levent Kandemir and Vladimir Litvak and Esther Florin},
  doi = {10.18112/openneuro.ds002885.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds002885.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: 2

  • Recordings: 115

  • Tasks: 4

Channels & sampling rate

  • Channels: 306 (4), 3 (4), 2 (3), 314 (3)

  • Sampling rate (Hz): 19200.0 (8), 3000.0 (6)

  • Duration (hours): 0.0

Tags

  • Pathology: Other

  • Modality: Other

  • Type: Other

Files & format

  • Size on disk: 20.1 GB

  • File count: 115

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds002885.v1.0.1

Provenance

API Reference#

Use the DS002885 class to access this dataset programmatically.

class eegdash.dataset.DS002885(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002885. Modality: meg; Experiment type: Other; Subject type: Other. Subjects: 2; recordings: 7; tasks: 4.

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/ds002885 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002885

Examples

>>> from eegdash.dataset import DS002885
>>> dataset = DS002885(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#