DS005648#

Mapping object space dimensions: new insights from temporal dynamics

Access recordings and metadata through EEGDash.

Citation: Alexis Kidder(*), Genevieve Quek, Tijl Grootswagers (2024). Mapping object space dimensions: new insights from temporal dynamics. 10.18112/openneuro.ds005648.v1.0.2

Modality: eeg Subjects: 21 Recordings: 67 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005648

dataset = DS005648(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 = DS005648(cache_dir="./data", subject="01")

Advanced query

dataset = DS005648(
    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{ds005648,
  title = {Mapping object space dimensions: new insights from temporal dynamics},
  author = {Alexis Kidder(*) and Genevieve Quek and Tijl Grootswagers},
  doi = {10.18112/openneuro.ds005648.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds005648.v1.0.2},
}

About This Dataset#

README

Experiment details for Mapping object space dimensions: new insights from temporal dynamics. The main folder contains the raw MEG data for all participants in standard bids format. See references. The “sourcedata” folder contains the trial behavioral data collected during the EEG Session. The data in this folder follows the following trial structure:

sourcedata

sub-[participant number]_task-targets_events.csv: contains all the events for each trial in the EEG session, detailing what was shown on the screen

sub-[participant number]:contains BIDS formatted raw EEG data

sub-[participant name]_task-targets_events_short.tsv: information about the channels used and sampling rate for all trials

sub-[participant name]_task-targets_eeg.bdf: EEG raw data

Dataset Information#

Dataset ID	`DS005648`
Title	Mapping object space dimensions: new insights from temporal dynamics
Year	2024
Authors	Alexis Kidder(*), Genevieve Quek, Tijl Grootswagers
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005648.v1.0.2
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: 67
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the DS005648 class to access this dataset programmatically.

class eegdash.dataset.DS005648(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005648. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 21; recordings: 21; 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/ds005648 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005648

Examples

>>> from eegdash.dataset import DS005648
>>> dataset = DS005648(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