DS006035#

somatomotor

Access recordings and metadata through EEGDash.

Citation: Fa-Hsuan Lin, Deirdre Foxe von Pechmann, Kaisu Lankinen, Seppo Ahlfors, Bruce Rosen, Jyrki Ahveninen, Matti Hämäläinen, Tommi Raij (2025). somatomotor. 10.18112/openneuro.ds006035.v1.0.0

Modality: meg Subjects: 5 Recordings: 98 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006035

dataset = DS006035(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 = DS006035(cache_dir="./data", subject="01")

Advanced query

dataset = DS006035(
    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{ds006035,
  title = {somatomotor},
  author = {Fa-Hsuan Lin and Deirdre Foxe von Pechmann and Kaisu Lankinen and Seppo Ahlfors and Bruce Rosen and Jyrki Ahveninen and Matti Hämäläinen and Tommi Raij},
  doi = {10.18112/openneuro.ds006035.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006035.v1.0.0},
}

About This Dataset#

Accession: #: ds006035 Description: Multi-subject, multi-modal (sMRI+MEG+EEG) neuroimaging dataset for median nerve stimulation and motor responses. This dataset contains MRI (T1w), MEG, and EEG data for median nerve electrical stimuli delivered at the right wrist. The task was to respond by lifting the left hand idex finger as quickly as possible after each right median nerve stimulus.

meg/

Three anatomical fiducials were digitized for aligning the MEG with the MRI: 1. nasion (lowest depression between the eyes);

Dataset Information#

Dataset ID	`DS006035`
Title	somatomotor
Year	2025
Authors	Fa-Hsuan Lin, Deirdre Foxe von Pechmann, Kaisu Lankinen, Seppo Ahlfors, Bruce Rosen, Jyrki Ahveninen, Matti Hämäläinen, Tommi Raij
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006035.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: 98
Tasks: 1

Channels & sampling rate

Channels: 70 (15), 388 (12), 387 (3)
Sampling rate (Hz): 1004.01611328125
Duration (hours): 0.0

API Reference#

Use the DS006035 class to access this dataset programmatically.

class eegdash.dataset.DS006035(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006035. Modality: meg; Experiment type: Motor; Subject type: Unknown. Subjects: 5; recordings: 15; tasks: 1.

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/ds006035 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006035

Examples

>>> from eegdash.dataset import DS006035
>>> dataset = DS006035(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