DS005594: eeg dataset, 16 subjects#
Alphabetic Decision Task (Arial Light Font)
Citation: Jack E. Taylor, Rasmus Sinn, Cosimo Iaia, Christian J. Fiebach (2024). Alphabetic Decision Task (Arial Light Font). 10.18112/openneuro.ds005594.v1.0.3
16-participant EEG dataset — Alphabetic Decision Task (Arial Light Font).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005594
dataset = DS005594(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005594(cache_dir="./data", subject="01")
Advanced query
dataset = DS005594(
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{ds005594,
title = {Alphabetic Decision Task (Arial Light Font)},
author = {Jack E. Taylor and Rasmus Sinn and Cosimo Iaia and Christian J. Fiebach},
doi = {10.18112/openneuro.ds005594.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds005594.v1.0.3},
}
About This Dataset#
Generated from raw data by MNE-BIDS (Appelhoff et al., 2019) and custom code to join to behavioural data, stimulus information, and metadata.
For full details on this dataset, see our preprint: Taylor et al. (2024) https://doi.org/10.1101/2024.11.11.622929
General notes:
* An issue during recording meant that sub-05 completed the first block without data being saved. The experiment was restarted from the beginning for this participant. This participant was not included in our analyses, but the data are included in this dataset. They are also identified with the recording_restarted field in participants.tsv.
* A separate issue during recording meant that EEG data for some trials were lost for sub-01, though enough trials were recorded in total to meet our criteria for inclusion in the analysis. The raw data comprised two separate recordings. In this dataset, the two recordings are concatenated end-to-end into one file. The point at which the files are joined is marked with a boundary event. This participant is identified with the recording_interrupted field in participants.tsv.
* During the course of the experiment, we identified an issue with the wiring in one splitter box, which meant that voltages from channels FT7 and FC3 were swapped in the raw recorded data. We elected to keep the wiring as it was for the duration of the experiment, and then swapped the data from the two channels in the code that generated this BIDS dataset. This means that this issue has been corrected in this BIDS version of the data.
* “BAD” periods (MNE term) for key presses and break periods are included in the events files.
* Recording dates/times have been anonymised by shifting all recordings backwards in time by a constant number of days (same constant for all participants). This obscures information that may be used to identify participants, but preserves time-of-day information, and the relative times elapsed between different recordings.
References
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8
Cohort#
Dataset Statistics#
Age distribution by gender (n=16, range 20–27 yr, mean 23.1 yr)
Sex composition
Channel counts: 66 ch (n=16 recordings)
Sampling frequencies: 1000.0 Hz (n=16 recordings)
Total recording duration: 12 h 18 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-alphabeticdecision
Showing one representative recording out of
16 subjects and 16 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 64 sensors — 64 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Alphabetic Decision Task (Arial Light Font) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2024 |
Authors |
Jack E. Taylor, Rasmus Sinn, Cosimo Iaia, Christian J. Fiebach |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005594,
title = {Alphabetic Decision Task (Arial Light Font)},
author = {Jack E. Taylor and Rasmus Sinn and Cosimo Iaia and Christian J. Fiebach},
doi = {10.18112/openneuro.ds005594.v1.0.3},
url = {https://doi.org/10.18112/openneuro.ds005594.v1.0.3},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005594 · Taylor2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005594(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Alphabetic Decision Task (Arial Light Font)
- Study:
ds005594(OpenNeuro)- Author (year):
Taylor2024- Canonical:
—
Also importable as:
DS005594,Taylor2024.Modality:
eeg; Experiment type:Perception; Subject type:Healthy. Subjects: 16; recordings: 16; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds005594 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005594 DOI: https://doi.org/10.18112/openneuro.ds005594.v1.0.3 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS005594 >>> dataset = DS005594(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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds005594").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds005594 to reproduce the tutorial on this dataset.
Citation
Jack E. Taylor, Rasmus Sinn, Cosimo Iaia, Christian J. Fiebach (2024). Alphabetic Decision Task (Arial Light Font). 10.18112/openneuro.ds005594.v1.0.3
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005594.v1.0.3.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset