DS004017#

Embodied Learning for Literacy EEG

Access recordings and metadata through EEGDash.

Citation: Linn Damsgaard, Marta Topor, Anne-Mette Veber Nielsen, Anne Kær Gejl, Anne Sofie Bøgh Malling, Mark Schram Christensen, Rasmus Ahmt Hansen, Søren Kildahl, Jacob Wienecke (2022). Embodied Learning for Literacy EEG. 10.18112/openneuro.ds004017.v1.0.3

Modality: eeg Subjects: 21 Recordings: 63 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004017

dataset = DS004017(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 = DS004017(cache_dir="./data", subject="01")

Advanced query

dataset = DS004017(
    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{ds004017,
  title = {Embodied Learning for Literacy EEG},
  author = {Linn Damsgaard and Marta Topor and Anne-Mette Veber Nielsen and Anne Kær Gejl and Anne Sofie Bøgh Malling and Mark Schram Christensen and Rasmus Ahmt Hansen and Søren Kildahl and Jacob Wienecke},
  doi = {10.18112/openneuro.ds004017.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds004017.v1.0.3},
}

About This Dataset#

There are three files per participant collected for each of the three stages of the procedure. Stage 1 (before measurement): A two-alternative forced choice discrimination task including letters “b” and “d” Stage 2 (intervention measurement): A simple visual search task including a target letter (either b or d) and three distractor letters chosen at random (p or q). Stage 3 (after measurement): A two-alternative forced choice discrimination task including letters “b” and “d” Participants were assigned to two groups. Participants in the intervention group were: sub-04, sub-05, sub-06, sub-07, sub-09, sub-10, sub-12, sub-14, sub-15, sub-16, sub-21 Participants in the control group were: sub-01, sub-02, sub-03, sub-08, sub-11, sub-13, sub-17, sub-18, sub-19, sub-20 Events in all recordings correspond to stimulus presentation. The value of 100 represents letter b stimuli and 200 represents letter d stimuli. Events marked with 10 (b) and 20 (d) represent practice trials. The detailed description of the tasks and the procedure can be found in this preprint: For questions about the tasks and the data please email Jacob Wienecke at wienecke@nexs.ku.dk. Marta Topor 17/03/2022

Dataset Information#

Dataset ID	`DS004017`
Title	Embodied Learning for Literacy EEG
Year	2022
Authors	Linn Damsgaard, Marta Topor, Anne-Mette Veber Nielsen, Anne Kær Gejl, Anne Sofie Bøgh Malling, Mark Schram Christensen, Rasmus Ahmt Hansen, Søren Kildahl, Jacob Wienecke
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004017.v1.0.3
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: 21
Recordings: 63
Tasks: —

Channels & sampling rate

Channels: 64 (63), 65 (63)
Sampling rate (Hz): 2048.0
Duration (hours): 0.0

API Reference#

Use the DS004017 class to access this dataset programmatically.

class eegdash.dataset.DS004017(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004017. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; recordings: 63; tasks: 0.

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/ds004017 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004017

Examples

>>> from eegdash.dataset import DS004017
>>> dataset = DS004017(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